ChatGPT answers, unedited

Yes. There are tools that automatically scan a long recording and produce candidate shorts, but there’s an important distinction: most let you describe what you want (topics, speakers, highlights), while fewer let you define explicit signal-based rules like “clip whenever applause exceeds X dB” or “find the top 10 volume peaks.”

A few options:

• opus.pro — automatically identifies highlights and generates multiple shorts, with reframing, captions, etc. It supports topic filtering, but its documented workflow is more AI/semantic than explicit audio-threshold rules. Opus Opus
• riverside.com — generates candidate clips and lets you specify duration, speaker, and keywords. It also scores generated clips. Again, this is primarily semantic/AI selection rather than “if volume > X, cut here.” Riverside Help Center Riverside
• eklipse.gg — particularly good for streams. It scans an entire VOD, detects highlight moments, ranks them, and outputs vertical clips. It can use things such as reactions/chat spikes, but is aimed more at gaming/stream highlights. Eklipse

If you mean literal rules

For something like:

Create a candidate clip when:
  applause > threshold
  OR laughter detected
  OR audio peak > -3 dB
  OR audience reaction > threshold

Start clip 8 seconds before trigger
End clip 12 seconds after trigger
Minimum gap between clips: 30 sec
Export every candidate as a separate MP4

that's a somewhat different category. You're looking for a rule-based highlight detector, potentially combining audio analysis + speech/transcript analysis, rather than a conventional “AI viral clipper.”

A custom workflow using FFmpeg + audio analysis + speech/AI detection can do this quite precisely, and then hand the resulting timestamps to an automated clip exporter. There are also newer AI-agent/video-editing interfaces that expose programmatic clipping; for example, OpusClip now documents an MCP server for AI-controlled clipping and editing. Opus

If you tell me what kind of footage you're clipping (podcasts, stand-up, lectures, livestreams, conferences, sports, etc.), I can find the tools that get closest to “give me these rules → spit out 20 candidate clips with timestamps.”

Yes. The closest match I found is opus.pro.

• Custom clipping rules: Its ClipAnything system uses audio, visual, and sentiment cues, and its AI Copilot lets you give specific natural-language instructions for what to clip. Opus Opus Clip
• Candidate generation: It analyzes the full video and produces multiple candidate shorts rather than requiring you to find timestamps manually. Opus Clip
• Export: You can download the resulting clips, and it can also export XML for Premiere/DaVinci workflows. Opus Clip
• Automation/API: Its API supports clip duration ranges, topic keywords, custom prompts, time ranges, and retrieving exportable clips. GitHub

For your specific example, you could conceptually give it a rule like:

“Find moments with a strong audience reaction, especially applause or sudden increases in audio intensity. Return 20–60 second clips centered on the reaction, with enough lead-in context.”

One caveat: I don't see a documented UI/API rule that literally says applause > X dB or volume peak > threshold. OpusClip exposes those kinds of signals to its multimodal model, but its documented customization is more natural-language/prompt-based than a traditional rule engine. GitHub Opus Clip

If you want literal signal-based rules—e.g. “audio RMS > -12 dB + applause detected + clip 15 sec before/30 sec after”—that's a somewhat different category, and I can find tools/APIs that support that more precisely.

Yes. What you’re describing exists, although there’s a distinction between “AI finds good moments” and “I define explicit trigger rules.”

Best matches

• opus.pro — probably the closest overall. Its ClipAnything system analyzes visual, audio, and sentiment cues, and its API can auto-select the top N moments above a configurable threshold. You can also supply a custom prompt, set clip-duration ranges, and export the resulting clips. Opus Opus
• eklipse.gg — particularly good for streams. Its current detection engine considers audio peaks, voice vs. game audio, on-screen action density, game events, and continuity, then produces a ranked list of candidate clips. Eklipse Eklipse.gg Blog
• OpusClip API/MCP — interesting if you want to build your own rule system. You can programmatically submit videos, get candidate clips/scores, and export them. Opus GitHub

If you mean literal rules like

“Find every moment where applause > X OR volume peak > Y, take −10/+20 seconds around it, merge overlapping candidates, rank them, and export the top 20.”

Then I’d lean toward building a small pipeline around an API rather than relying on a normal AI clipper UI.

For example:

audio/video
   ↓
detect events
   ├── applause > threshold
   ├── volume peak > threshold
   ├── laughter
   ├── speech intensity
   └── visual activity
   ↓
merge nearby events
   ↓
expand each event ± N seconds
   ↓
score/rank candidates
   ↓
export top 20 as 9:16 shorts

OpusClip is particularly interesting for this because its API exposes both candidate selection and scoring, while its MCP integration can let an AI agent orchestrate the clipping/editing/export process. Opus Opus

If you tell me what kind of footage you're clipping (podcasts, stand-up, livestreams, sports, presentations, etc.), I can recommend the tool that gets closest to those exact rule-based triggers.

Yes—but there’s an important distinction between AI “find the best moments” and rule-based clipping like “make a clip whenever applause exceeds X dB.”

Best fits

• opus.pro — probably the closest turnkey option. It analyzes audio, video, and sentiment cues and generates candidate shorts automatically. Its API also supports custom prompts, clip-duration ranges, time ranges, and automated export workflows. Opus GitHub
• podcli.com — interesting if you specifically want a configurable/local pipeline. It supports saved presets and outputs clips to a directory, so it is much closer to building your own deterministic clipping rules. Podcli
• wisecut.ai — automatically finds highlights and creates vertical clips, but it's more AI/editorial than event-rule driven. Wisecut Wisecut
• riverside.fm — its Magic Clips automatically produces 30–90 second highlights, but again, you're delegating the selection to its AI rather than defining signal thresholds yourself. Riverside Help Center

If you mean rules like this

IF applause > threshold
   OR audience_laughter > threshold
   OR speech_energy > threshold
   OR volume_peak > threshold
THEN
   take 15 sec before event
   take 45 sec after event
   score candidate
   export as MP4

I don't know of a mainstream SaaS tool that exposes that exact level of audio-event rule configuration as its primary UI. OpusClip gets closest on the candidate generation side, while something like Podcli/custom FFmpeg + audio analysis gives you much more deterministic control.

A particularly useful architecture would be:

long recording → audio/event detection → candidate timestamps → deduplicate/merge → rank → render 9:16 shorts → export

That would let you combine objective signals (applause, laughter, volume peaks, silence, music changes) with semantic signals (interesting quote, punchline, controversy) rather than relying entirely on an AI “viral score.”

If that's what you're trying to build, I can also point you to the best existing tool/API specifically for defining those rules and batch-exporting the resulting clips.

Yes. There are tools that can do something close to this: define (or approximate) signals like loud audio peaks, crowd reactions, laughter, transcript moments, etc., then return a batch of candidate shorts for review. The exact “if applause > X dB, create a clip from -20s to +40s” workflow is less common in consumer tools, but these are the closest fits:

If you specifically mean rule-based clipping (rather than “AI decides what is interesting”), look for tools with:

• audio threshold triggers (e.g., RMS/LUFS/dB spike)
• event markers
• webhooks/API
• timeline padding rules (e.g., include 15 seconds before and 30 seconds after)
• batch export

A more technical DIY stack is also possible:

1. Run audio analysis (FFmpeg + librosa/pyannote/etc.) to detect peaks.
2. Generate timestamps.
3. Cut clips automatically with FFmpeg.
4. Feed those clips into a captioning/reframing tool.

For example, a rule might be:

IF loudness > baseline + 8 dB
AND duration > 1.5 seconds
THEN export:
  start = event_time - 20 sec
  end   = event_time + 45 sec

Most AI clippers today are better at “find likely viral moments” than exposing a full rule engine. If you’re building a workflow for podcasts, conferences, sports, or live events, the best choice differs.

Yes. If by “rules” you mean detect moments based on signals like applause, loudness spikes, laughter, keywords, or other cues, then generate a batch of candidate Shorts, there are a few good options.

• clickclips.app — probably the closest match to what you described. It has audio detection for volume spikes, speech/keyword detection, and AI detection. You can specify clip duration and have it automatically assemble a set of highlights, which you can then export. ClickClips
• opus.pro — stronger general-purpose AI clipping. Its ClipAnything model considers audio, visual, and sentiment cues, and you can give it natural-language prompts describing what you want clipped. Its API also supports duration ranges, custom prompts, and retrieving/exporting the resulting clips. Opus GitHub
• viblo.ai — explicitly detects volume spikes, laughter, shouting, and applause, then produces a shortlist of highlight candidates for review/export. Viblo
• eklipse.gg — particularly good for streams. It scans the whole VOD and scores audio peaks and reactions, producing ranked candidate clips that can be exported as vertical Shorts/Reels/TikToks. Eklipse Eklipse

If you want literal rules

For something like:

“Find every moment where applause exceeds X dB, OR the audience gets loud for >2 seconds, take 15 seconds before + 30 seconds after, merge events within 10 seconds, and export the top 20 candidates.”

I'd look at ClickClips or an API-based workflow with OpusClip rather than a purely “AI, find viral moments” editor. OpusClip's API in particular exposes programmatic clip duration ranges and custom curation prompts, and returns exportable clips. GitHub

If you tell me what kind of footage you're clipping (podcast, live event, stand-up, gaming, speeches, etc.), I can narrow this down to the tool that best supports those specific rules.

Yes — but there’s an important distinction between “AI decides what’s interesting” and “I define measurable clipping rules.”

Best matches

• opus.pro — probably the closest general-purpose option. Its AI can generate candidate shorts, and its API lets you specify things like clip duration, topic keywords, and source ranges, then retrieve the resulting exportable clips. Opus Clip Opus Clip

Caveat: I don't see a documented rule system where you can literally say “only make a clip when applause > X dB” or combine arbitrary audio signals as Boolean conditions.

• eklipse.gg — particularly interesting if your source is streams/events. Its current highlight detector explicitly scores audio peaks, laughter/shouting, game events, and on-screen markers, then ranks candidate clips and lets you export them. Eklipse

This is closer to your “high applause + volume peak = candidate” concept, although it still isn't an arbitrary rule-builder.

• wisecut.ai — has an “Autopilot” workflow where you define editing/clip settings and it automatically processes new uploads. Wisecut It’s more automation-oriented than signal-based clipping.

If you mean literal rules

For example:

Create a candidate whenever applause > threshold OR audio peak > threshold, include ±15 seconds, reject clips shorter than 20 seconds, and export the top 20 candidates.

I don't know of a polished mainstream SaaS that exposes that exact kind of event/signal rule engine out of the box.

However, this is very doable as a custom pipeline: audio analysis → detect applause/peaks → generate timestamps → feed those timestamps into a video clipper → batch-export candidate Shorts. OpusClip's API could potentially handle the latter part, since it accepts source ranges and produces exportable clips. Opus Clip

If you're building this for podcasts, conferences, comedy, sports, or livestreams, tell me which one — I can point you to the best existing tool or sketch the exact pipeline for the rule system you have in mind.

Yes. What you’re describing is rule-based highlight detection: scan a long recording for measurable events, score/rank the moments, then export the best candidates as Shorts.

A few options are close:

• opus.pro — probably the strongest general-purpose option. It detects highlights, generates multiple clips, supports vertical reframing/captions, and can export clips or Premiere projects. Its newer MCP/API tooling is particularly interesting if you want to automate the workflow. Opus
• wisecut.ai — automatically detects highlights and can run an ongoing workflow where new videos become candidate clips. It also lets you describe editing behavior in natural language. Wisecut
• eklipse.gg — particularly good for livestreams. It scans VODs for reactions, plays, and other high-interest moments and produces ranked vertical clips. Eklipse

But there's an important distinction: if you literally want rules such as:

applause > 85th percentile
OR audio peak > -3 dB
OR audience laughter detected
AND clip duration 20–60 sec
AND speech continues for ±10 sec around event
→ export top 20 candidates

then the mainstream AI clippers aren't really designed as a precise event-rule engine. They generally use proprietary "engagement/highlight" models rather than exposing arbitrary audio/event thresholds.

If that's what you're trying to build, I can also point you toward a more programmable stack/API where you can define those rules yourself and automatically spit out a folder of candidate Shorts.

Yes. What you’re describing is closer to an event-based highlight detector than a normal AI clipper: you want to define signals (e.g., applause > X dB, crowd roar, laughter, transcript keywords, reaction spikes), have it find timestamps, and export candidate Shorts.

A few tools get close:

• Viblo — detects audio energy spikes like laughter, shouting, and applause-like moments, then surfaces highlight candidates you can review and export. Viblo
• ClickClips — has configurable detection modes including audio volume spikes, speech keywords, AI detection, clip counts, and length settings, then exports clips. ClickClips
• Eklipse — more automated: it scores moments using signals like audio peaks, reactions, and (for supported content) other context, then gives you ranked clips to export. Eklipse
• SnipSound — one of the closer fits for rule-based audio triggers: it exposes concepts like sensitivity thresholds, minimum spike duration, and clip windows around detected peaks. SnipSound

If you need literal rules, such as:

IF applause_level > -12 dB
AND duration > 2 seconds
AND transcript contains "wow" OR "amazing"
THEN create candidate clip:
  start = event_time - 15s
  end = event_time + 45s
  export = 9:16

then you may be better served by a workflow built on:

• FFmpeg for cutting/exporting
• Whisper (or another speech-to-text engine) for transcript triggers
• audio analysis (RMS/loudness, spectral features) for applause/volume detection
• a small rules engine (Python, Node, Zapier-style automation, etc.)

Most consumer AI clippers rank “interesting moments” with their own models; fewer let you fully specify the detection logic. Momevera

If you’re building a pipeline for podcasts, conferences, sports, or livestreams, the best choice changes a lot—those have different signals (applause vs. chat spikes vs. goals vs. punchlines).

Yes — but there’s an important distinction between “AI finds engaging moments” and “I define objective audio/event rules and it generates clips.”

Best fits

• opus.pro — probably the easiest starting point. It analyzes long videos and generates candidate shorts automatically, with captions, reframing, and export. It supports things like topic/keyword filtering, but it’s primarily AI-curation rather than a true rule engine. Opus Opus
• riverside.com — Magic Clips finds highlights and lets you control things like length, topics, speaker focus, and keywords, then export the candidates. Again, it isn't really “if applause > X dB, make a clip.” Riverside Riverside Help Center
• timebolt.io — interesting if your rules are audio/waveform-driven. It detects cuts using waveform information and is much closer to deterministic audio-based editing than the AI highlight tools. TimeBolt

What you're describing is more specialized

If you mean something like:

“Whenever applause exceeds -20 dB for 2+ seconds, or audience laughter occurs, or the speaker's volume peaks above -6 dB, grab 15 seconds before + 45 seconds after, rank the candidates, and export them as MP4s.”

I don't know of a mainstream SaaS tool that exposes that exact kind of rule-builder for clipping.

You'd typically build that with an audio/video analysis pipeline:

1. Analyze the audio waveform for volume peaks.
2. Detect applause/laughter/cheering with an audio classifier.
3. Apply your thresholds and minimum durations.
4. Expand each detected event into a clip window.
5. Merge overlapping events.
6. Score/rank candidates.
7. Automatically render 9:16 clips with captions.
8. Export a batch of candidate shorts.

That's actually a pretty tractable workflow, and it would give you considerably more control than OpusClip/Riverside.

If applause, laughter, crowd reaction, volume spikes, or specific sound events are the signals you care about, I can also find you the closest existing tool/API that lets you define those rules without building the whole thing from scratch.

Yes. There are a few ways to do this, but the closest fit to “define rules → scan long video → output candidate clips” is an API/workflow rather than a typical one-click shorts editor.

Best fit: Twelve Labs

Twelve Labs can search/segment video using audio, visual content, and transcription, and return the matching time ranges. Its API can, for example, search for segments containing particular audio events and return start/end timestamps. TwelveLabs Developer Documentation TwelveLabs Developer Documentation

So you could build rules like:

Candidate clip if:
  applause detected
  AND applause intensity > threshold
  AND speaker has just finished a sentence
  AND clip duration = 20–60 sec

OR:
  volume spike > threshold
  AND transcript contains a punchline/question

Then automatically export the resulting timestamp ranges as candidate Shorts.

The important distinction is that Twelve Labs' audio search understands sounds, such as environmental/non-speech audio, while a raw measurement like “RMS volume > -10 dB” is better handled by conventional audio analysis. TwelveLabs Developer Documentation

docs.twelvelabs.io

If you want a no-code/consumer tool

Tools such as OpusClip are better at “find the most engaging moments and turn them into Shorts automatically”, but they're less suited to precise rules like:

“Only create candidates when applause exceeds X, then include 8 seconds before the applause and 20 seconds after it.”

For that level of deterministic control, I'd use a small pipeline:

FFmpeg/librosa → event detection → Twelve Labs/LLM → clip generation → FFmpeg export

That gives you both hard numerical rules (volume peaks, applause duration, silence, etc.) and semantic rules (punchline, interesting statement, audience reaction).

If you tell me what kind of footage you're clipping (podcasts, stand-up, lectures, live events, interviews, etc.), I can recommend the best existing tool—or sketch a workflow that automatically produces a folder of candidate Shorts from a recording.

Yes. What you’re describing is usually called rule-based highlight detection, AI clipping, or a clip discovery pipeline. There are tools that can take long recordings, detect signals like loudness spikes/reactions/transcript moments, rank candidates, and export Shorts/Reels/TikToks. Cut.Pro Eklipse

A few options:

• clickclips.app — closer to your “set rules” idea. It has detection modes based on audio peaks, speech keywords, and AI detection, then exports clips. ClickClips
• supoclip.com — AI finds candidate moments from transcripts and signals, scores them, reframes to vertical, captions, and exports. SupoClip SupoClip
• eklipse.gg — aimed more at streams; it looks at game events, reactions, and audio peaks, then produces ranked highlights. Eklipse
• vimeo.com — more general long-form content clipping; it generates candidate clips from transcripts and meaningful moments. Vimeo

If you specifically want rules like:

• “Find moments where applause > X dB”
• “Find laughter + audience reaction within 5 seconds”
• “Find volume peaks after a quiet section”
• “Export 30–60 second vertical candidates”
• “Give me a CSV/JSON of timestamps before rendering”

…you may want a more customizable workflow rather than a consumer clipper. A typical setup is:

1. Extract audio features (RMS loudness, peaks, applause/laughter classifier).
2. Detect events and assign timestamps.
3. Combine signals into a score: - applause spike: +5
4. volume jump: +3
5. transcript excitement: +4
6. audience reaction: +5
7. Export top N segments.
8. Render selected candidates with FFmpeg or an editor API.

There are also open-source projects that expose more of this pipeline. For example, Clips Studio analyzes audio, transcript, and visual signals to find candidate peaks and export clips. GitHub

If you’re building this yourself, the key question is whether your input is podcasts/talks/events (applause/laughter detection) or streams/gaming (hype/game-event detection), because the best signals differ.

Yes. The closest match I found is opus.pro, specifically its ClipAnything feature.

• Audio cues: it analyzes audio signals, so things like loud reactions/peaks can factor into finding moments.
• Sentiment/engagement: it also considers sentiment and other multimodal cues.
• Rule-like prompting: you can describe what you want in natural language—for example, “find moments with big audience reactions, applause, and energetic delivery.” Opus Opus
• Candidate generation: it produces multiple candidate clips rather than making you scrub through the entire recording.
• Export: candidates can be turned into vertical Shorts/Reels/TikToks with captions and reframing. Opus

One caveat

If by "rules" you mean something very literal like:

Create a candidate whenever applause > X dB OR volume peak > Y dB, make it 15–45 sec, include 5 sec before the event and 3 sec after, then export all candidates

I wouldn't rely on OpusClip for that level of deterministic, threshold-based automation. Its approach is more AI/prompt-driven than an explicit event-trigger/rules engine.

Wisecut is another option—its AI detects highlights and lets you review/export batches of clips—but it similarly isn't primarily a programmable audio-event rule system. help.wisecut.video help.wisecut.video

If you're trying to build exactly the applause/volume-trigger workflow, I can also find tools that let you define actual detection thresholds + pre/post-roll + batch export, including more technical/local tools.

Yes. What you’re describing is essentially rule-based highlight detection: define signals such as “volume > X dB,” “applause detected,” “chat spikes,” etc., add a time window/cooldown, and have the tool output candidate clips for review/export.

A few options stand out:

• clipxtudio.com — probably closest to your description. It has configurable Smart Triggers based on voice, audio, chat, scene changes and keywords, plus audio-spike detection, cooldown rules, candidate scoring, and export. ClipXtudio for OBS
• clickclips.app — detects audio peaks, speech/keywords, or AI highlights and turns them into candidate clips. It supports custom clip duration and batch processing. ClickClips
• snipsound.com — particularly interesting if you want explicit thresholds. You can tune detection sensitivity and minimum sustained loudness, and it produces a ranked shortlist with configurable lead-in/out. It specifically mentions applause, laughs and punchlines. SnipSound
• eklipse.gg — more AI-driven than rule-driven. It combines audio peaks, reactions, game events and other signals, then ranks candidates for review/export. Eklipse
• reclipstudio.com — worth considering if you want something running locally; it has built-in moment detection and optional AI selection, with local processing. ReClipStudio

If you specifically mean rules like

Candidate =
applause detected OR volume peak > baseline + 8 dB
AND clip contains speech
AND minimum event duration = 2 sec
THEN capture 15 sec before + 30 sec after
cooldown = 60 sec
export candidates as MP4

then ClipXtudio or a custom/local pipeline is closer to what you want than the typical “AI finds viral moments” products.

If you tell me whether this is for podcasts, live events, sports, comedy, or streams, I can narrow it down to the 2–3 tools that actually support that kind of rule logic.

Yes. There are tools getting pretty close to exactly that workflow, though true rule-based clipping (“clip whenever applause exceeds X dB for Y seconds”) is less common than AI highlight detection.

Best matches

• clickclips.app — probably the closest match to what you described. It can scan long recordings for audio-volume spikes, detect spoken keywords, and use AI detection. You can set clip duration, choose how many clips to generate, batch-process up to 10 recordings, and export the resulting clips. ClickClips ClickClips
• snipsound.com — particularly interesting for applause/laughter/punchlines. You can tune sensitivity and minimum sustain time; it detects stretches where loudness rises above the recording's baseline, ranks the moments, and creates proposed clips with configurable lead-in/length. It runs locally in the browser. SnipSound
• opus.pro — better if you want the AI to judge why something is a good short rather than simply obey audio thresholds. It analyzes audio, visual and sentiment cues and can also accept natural-language instructions for finding specific moments. Opus
• descript.com — strong for podcast/interview workflows. Its AI can generate clips from an episode and you can give it directions about what topics/moments to look for, but it isn't primarily a programmable audio-event trigger system. Descript

If you mean something more programmable

For example, you want to define:

Candidate = applause > 6 dB above baseline
AND lasts > 1.5 sec
OR speaker volume peak > −3 dBFS
OR laughter detected
→ grab 12 sec before + 30 sec after
→ rank candidates
→ export top 20 as MP4s

ClickClips/SnipSound are the closest off-the-shelf options I found. If you need arbitrary combinations of signals and thresholds, I'd lean toward building a small pipeline around audio analysis + speech detection + FFmpeg rather than relying on a conventional AI clipper.

If you tell me what kind of footage you're clipping (podcasts, comedy, lectures, live events, gaming, etc.), I can narrow this down to the best 2–3 tools for your exact rules.

Yes. There are two broad approaches:

1. Rule-based clipping (detect audio peaks, applause, silence, scene changes, keywords)
2. AI highlight detection (scores moments based on engagement, story, emotion, and audio/visual cues)

If you specifically want to define rules like "applause > X dB", "volume spike", or "remove silence then export candidate clips", here are the best options:

If you want actual rule definitions

Very few commercial tools expose rules like:

• applause > 8 seconds
• audio peak > -10 dB
• crowd noise detected
• laughter probability > 70%
• speaker excitement
• cut 5 seconds before/after trigger

Most products instead hide these behind AI scoring.

For true rule-based workflows, people often build pipelines using:

• FFmpeg (silencedetect)
• PySceneDetect (scene changes)
• Whisper (transcription + keywords)
• PyAudioAnalysis, librosa, or Essentia (audio feature extraction)
• A small Python script that combines the signals and emits clip timestamps

That gives you full control over thresholds.

Best fit for your example

If your rule is:

Detect applause OR a loud volume spike → create a 30–60 second candidate Short

I'd recommend:

• Viblo AI if you want an off-the-shelf solution that detects applause, laughter, shouting, and other audio-energy events before surfacing clip candidates. viblo.ai
• Bytecap if you also want to guide the AI with prompts like "find standing ovations" or "find audience reactions" in addition to automatic highlight detection. www.bytecap.io
• A custom Python pipeline if you need deterministic, repeatable rules rather than AI judgments.

A custom pipeline can even output a CSV or JSON of candidate clips, then automatically render YouTube Shorts from those timestamps. If you're processing many hours of video or need consistent behavior, that's often the most flexible approach.

Yes. There are a few tools that get close to exactly this workflow, although explicit rule-based clipping like “applause > X dB AND speech present” is less common than AI scoring.

• snipsound.com — probably the closest to your example. It detects sustained loudness spikes, lets you tune sensitivity and minimum sustain time, ranks the moments, and creates proposed clips. It specifically mentions applause, laughs, crowd reactions, etc. Analysis runs locally in the browser. SnipSound
• rhei.ai — analyzes audio energy/peaks, scene changes, speaking patterns, pacing, and natural cuts, then returns candidate segments with in/out points and reasons. You can trim/reframe and export them. Rhei
• clickclips.app — has explicit Audio Detection modes, ranks detected moments, and lets you specify clip count and min/max duration. It also supports training your own highlight model, which is interesting if your definition of a “good clip” is very specific. ClickClips
• opus.pro — better if you want to build this into an automated pipeline. Its API produces ranked clips and supports parameters such as duration ranges and topic keywords, with exportable clips. Opus
• autoclip.dev — interesting specifically for your use case because its detection combines video/transcript analysis with audio detection for laughter, applause, music drops, and energy peaks. AutoClip

If you mean truly programmable rules

For something like:

IF applause > threshold
   OR volume_peak > threshold
   OR laughter_detected
THEN
   create candidate clip
   start = event - 10 sec
   end = event + 20 sec
   reject if duration < 15 sec
   rank by peak_strength
   export top 10

I'd lean toward a small custom pipeline rather than a conventional AI clipping app. You can detect audio events/peaks, combine them with transcript timestamps, and use FFmpeg to automatically produce the candidates.

SnipSound is the closest ready-made tool I found for the audio-trigger portion. If you want arbitrary combinations of signals and automatic batch exporting, custom + OpusClip/API-style infrastructure is more flexible. SnipSound Opus

If you tell me what your actual rules are (e.g. applause, laughter, cheering, speaker volume, specific words, chat spikes), I can suggest the best tool—or sketch the exact automated pipeline.

Yes — but there’s an important distinction between AI highlight detection and rule-based clipping.

• opus.pro is probably the closest turnkey option. It analyzes long videos, scores potential moments using signals including emotional intensity and audio dynamics, then generates vertical shorts. Its API also exposes scoring-related controls. Opus Opus
• wisecut.ai automatically detects highlights, creates clips, and exports them, but its workflow is more AI/editor-oriented than a programmable “if applause > X and peak > Y, make a clip” system. Wisecut Wisecut Resources

If you mean literal rules

Something like:

IF applause_level > 80%
AND audio_peak > -3 dB
AND duration between 20–90 sec
THEN
    mark ±10 sec around event as candidate
    export candidate.mp4

I wouldn't choose OpusClip/Wisecut as the core engine if that's your requirement. They're optimized to decide what is interesting rather than letting you define precise audio/event thresholds.

A more controllable setup would be:

FFmpeg/Python audio analysis → detect applause/volume peaks → generate timestamps → automatically cut/export candidates → optionally send those candidates through OpusClip for captions/reframing.

If you're trying to build this for live events, podcasts, comedy, church services, sports, or conference talks, I can recommend the best tool/stack for that specific use case.

Yes — but there’s an important distinction: most AI clipping tools let you describe what makes a good clip, but relatively few let you define hard, signal-based rules like “only surface moments where applause exceeds X dB.”

A few options worth looking at:

• opus.pro — probably the closest off-the-shelf option. Its API analyzes candidate moments using signals including emotional intensity and audio dynamics, then returns rendered short clips. Opus Opus
• wisecut.ai — automatically finds highlights and can turn long recordings into shorts, with automation/API options for higher-volume workflows. Wisecut Wisecut
• descript.com — more programmable. You can automate transcription, editing, and clip creation, so it could serve as the rendering/editing layer while your own rules determine which timestamps become candidates. Descript Descript API
• vimeo.com — generates candidate clips based on transcript/story structure and lets you edit/export them, but it's less oriented toward custom audio-signal rules. Vimeo

If you mean exactly this workflow

For example:

Upload 2-hour recording → detect applause/laughter/cheering → detect unusually high audio peaks → combine those signals → produce 10 candidate 30–90 sec windows → export them as separate MP4s.

I don't see a mainstream tool that exposes all of those rules directly as a simple rule builder. The more flexible approach is to build a small pipeline around an API:

video → audio analysis → event detection → scoring/rules → candidate timestamps → clip renderer → MP4s

You could have rules such as:

candidate if:
  applause_score > 0.75
  OR laughter_score > 0.80
  OR audio_peak > -6 dB

and:
  duration = 20–90 sec
  speech_present = true
  don't overlap another candidate by > 50%

rank by:
  applause × 0.4
  laughter × 0.2
  volume_peak × 0.2
  transcript/semantic score × 0.2

That would give you actual deterministic clipping rules, rather than hoping an AI's definition of "viral moment" matches yours.

If you tell me whether this is for podcasts, live events, comedy, sports, church/conferences, or something else, I can point you toward the best existing tool—or sketch the simplest way to build exactly this.

Yes. What you’re describing is more specific than the typical “AI finds viral clips” workflow: you want rule-based highlight detection, where signals such as applause, loudness, laughter, or other events produce candidate clips that you review and export.

A few options stand out:

• clickclips.app — probably the closest match if you want explicit signal-based clipping. Its audio detector finds volume spikes and ranks moments; it also supports speech/keyword detection, configurable clip lengths, and batch processing. ClickClips
• rhei.ai — automatically surfaces candidates using audio energy/peaks, scene changes, speaking patterns, and natural cuts, then gives you in/out points to review and export. Rhei
• vizard.ai — good for podcasts/interviews; it detects high-energy and emotionally engaging sections, but it's more AI/editorial than explicit “if volume > X, make a clip” rules. Vizard
• eklipse.gg — particularly interesting for streams. It combines audio peaks, reactions, chat spikes, and game events, then ranks the resulting candidates for export. Eklipse

If you mean literal custom rules

For example:

Find every moment where applause > 85 dB, or laughter is detected, or audience reaction spikes; start 10 seconds before the event and end 20 seconds afterward; merge events within 15 seconds; output the top 20 clips.

That's not something most mainstream AI clippers expose as a flexible rule engine. There are, however, more programmable approaches. Prompt2Clip, for example, extracts audio features including RMS loudness, silence, dramatic pauses, and specifically detects laughter, applause, and cheering via YAMNet, then combines those signals with video and transcript analysis. GitHub

So if your goal is “give me 50 candidate shorts based on rules I define, rather than letting an AI decide what is viral,” I'd lean toward a programmable/local tool or building a small pipeline around audio-event detection + FFmpeg.

If you tell me what kind of footage you're clipping (podcasts, stand-up, speeches, livestreams, sports, etc.), I can narrow this down to the best tool for that exact workflow.

Yes. What you’re describing is usually called rule-based highlight detection, AI clip mining, or a highlight engine. There are tools that can scan long recordings, detect moments based on signals like audio peaks, laughter/applause, reactions, keywords, or transcript events, then return candidate clips for review/export. Rhei Poppercut

A few options:

• clickclips.app — closer to the “set detection rules” workflow. It supports audio detection (including volume spikes), speech/keyword detection, AI detection, choosing clip counts and lengths, and exporting selected clips. ClickClips
• poppercut.com — scans for high-energy moments using signals like audio energy, motion, faces, and commentary spikes, then ranks highlight candidates and exports Shorts-ready clips. Poppercut
• supoclip.com — AI-assisted clipping with transcript analysis, candidate scoring, vertical reframing, captions, and exports. It’s more “find the best moments” than explicit if/then rules. SupoClip
• vimeo.com — generates candidate clips from long videos based on transcript/story signals and provides editable exports. Vimeo

If you specifically want rules like:

IF applause > threshold
AND audio peak > 8 dB above baseline
AND crowd reaction lasts > 2 seconds
THEN create 45-second candidate clip

you’ll probably want either:

• a more technical/self-hosted pipeline (FFmpeg + audio analysis + Whisper/transcription + a clip generator), or
• a tool with an API/custom detection layer.

For example, open-source projects like Clips Studio expose more of the underlying pipeline: they analyze audio peaks, laughter/hype signals, transcripts, and other features to produce candidate clips. GitHub

The best fit depends on the source:

• sports / live events: applause, crowd noise, goals, reactions → Popper-style highlight engines
• podcasts/interviews: transcript + laughter + emotional peaks → SupoClip/Vimeo/Made-style tools
• your own custom scoring rules: build around FFmpeg + audio feature extraction + a clip queue

If you’re trying to do this for livestream VODs, podcasts, conferences, or sports footage, the best tools differ quite a bit.

Yes. The closest match I found is opus.pro.

• Rule-like instructions: OpusClip's ClipAnything lets you give natural-language instructions about what moments to find, using audio, visual, and sentiment cues. Opus Clip Opus Clip
• Audio peaks: It can use audio cues, so things like sudden loud reactions can contribute to candidate selection. Opus Clip
• Candidate generation: It scans the long video and produces a ranked set of short candidates rather than requiring you to manually scrub through it. Opus
• Export: You can download the resulting clips, including HD copies and XML for further editing. Opus Clip
• Automation/API: OpusClip also has an API, so you could build a pipeline that submits recordings automatically and retrieves the generated shorts. Opus Opus Clip

However, there's an important distinction: if you mean literal programmable rules, such as:

IF applause > 85th percentile AND volume peak > -6 dB THEN make a 30–60 sec candidate

OpusClip doesn't appear to expose that kind of low-level event/rule engine in its consumer UI. It's more AI prompt/semantic scoring than deterministic audio-feature rules.

Another interesting option is eklipse.gg, which explicitly scores audio peaks, laughter/shouting, and other signals, then returns ranked clip candidates. Eklipse

And wisecut.ai has an Autopilot workflow where you define settings and it automatically turns new long-form uploads into clips, but again it's more AI editing/automation than arbitrary signal-based rules. wisecut.ai

If you're specifically trying to build a "highlight detector" based on measurable signals like applause, laughter, volume, crowd noise, transcript keywords, camera cuts, etc., I can also find tools that let you define those rules much more explicitly.

Yes. What you’re describing is basically rule-based highlight detection: analyze the whole recording for signals like applause, loudness spikes, laughter, crowd reaction, motion, etc., then return candidate clips with in/out points for you to review/export.

A few options:

• Viblo — probably the closest match to your example. It explicitly detects volume spikes, laughter, shouting, and applause, combines those with visual/activity peaks, and surfaces roughly 5–10 highlight candidates for refinement/export. Viblo
• opus.pro — more general-purpose. Its ClipAnything system analyzes audio, visual, and sentiment cues, and you can give it natural-language instructions for what moments to find. It then generates ranked short candidates and can export them. OpusClip Opus Clip
• Remi / Rhei Smart Clipping — explicitly analyzes audio energy/peaks, scene changes, speaking patterns, pacing, and natural start/stop points, then gives you proposed segments with in/out points and explanations. Rhei
• Clips Studio — interesting if you want something local/self-hosted. Its scoring combines loudness spikes, burst density that can correspond to laughter/applause, silence→explosion patterns, visual motion, faces, etc. GitHub

If you mean literal rules

For example:

Find every moment where applause exceeds X dB, OR audio energy is in the top 5%, then make a 45–90 second clip around it, merge overlapping candidates, and export the top 20.

That's a little different from typical "AI find viral moments" products. Viblo/Remi are closer out of the box, while a small custom pipeline using FFmpeg + audio analysis + an LLM/video model gives you much more deterministic rules.

If you tell me what kind of footage you're clipping (podcasts, live events, sermons, sports, YouTube, etc.), I can narrow this down to the best 2–3 tools and specifically check whether they support custom thresholds/rules rather than just AI judgment.

Yes. There are a few tools that get close, but the exact workflow you describe—“apply rules like applause > X dB, volume peak, laughter, etc., then export all matching segments as candidate Shorts”—is more specialized than the usual “AI finds viral moments” workflow.

Best fits

• Klap — probably the closest turnkey option. It analyzes audio energy peaks, reactions, motion intensity, speaker engagement, and topic relevance, then produces ranked candidate clips and can batch-export them. It also supports prompts such as finding every particular type of moment. klap.app
• OpusClip — excellent for automatically generating lots of candidate Shorts. Its ClipAnything system considers audio, visual and sentiment cues, and you can use natural-language prompts to target particular moments. www.opus.pro
• Descript — better if you want to combine automated detection with more conventional editing. It has sophisticated audio/volume handling, but it's less purpose-built for “find every event matching my threshold and spit out clips.” www.descript.com

If you mean literal rules

For example:

Find every 10–45 sec segment where
applause > threshold OR laughter > threshold OR audio peak > threshold,
expand the window ±5 seconds, avoid overlapping clips, then export all candidates as 9:16 MP4s.

That's something I'd look at outside the typical AI clipping apps—potentially a workflow using audio/event detection + FFmpeg, with an AI clipper as the second-stage selector.

The advantage is that you can make the rules deterministic rather than trusting an AI's subjective definition of a “viral moment.”

If you tell me what kind of footage you're clipping (podcast, stand-up, live event, church/service, sports, interviews, etc.), I can point you to the tool/workflow that best supports specific audio/event thresholds rather than generic AI highlight detection.

Yes — but there’s an important distinction between “AI finds good clips” and “I define measurable clipping rules and it finds clips matching them.”

A few good options:

• OpusClip — probably the easiest turnkey option. It analyzes audio, visuals, sentiment, etc., and can generate multiple shorts automatically. It also supports natural-language instructions for finding specific moments. www.opus.pro
• Remi / Rhei Smart Clipping — particularly interesting for your use case because it explicitly analyzes audio peaks/energy, scene changes, speaking patterns, pacing, and natural boundaries, then outputs candidate segments with in/out points and reasons. www.rhei.ai
• Prompt2Clip — if you want actual rules rather than a black-box “viral score,” this is closer. It analyzes loudness, dramatic pauses, laughter/applause/cheering, visual changes, speech, etc., and is open source. github.com
• Ascynd — desktop-oriented and automatically identifies high-engagement moments using things like peak emotion, audio energy, speech clarity, and hook strength. ascynd.io

If you mean rules like this

Find every 20–60 sec segment where:

• applause exceeds X dB
• OR audio peak is in the top 5%
• OR laughter/cheering is detected
• speech before the event provides at least 8 sec of context
• don't overlap candidates by more than 10 sec
• rank by combined applause + volume + semantic excitement
• export the top 20 as MP4s

That's much less common in consumer clipping tools. Most of them let AI decide what is interesting rather than exposing a rule engine with thresholds.

For that particular workflow, I'd look hardest at Prompt2Clip / a custom FFmpeg + audio-analysis pipeline, or potentially build on an AI clipping API. OpusClip, for example, now exposes clipping through an MCP/API-oriented workflow, which makes it more amenable to automation. www.opus.pro

If you tell me what kind of footage you're clipping (podcasts, stand-up, live events, gaming, speeches, sports, etc.), I can narrow this down to the 2–3 tools that can actually implement those rules, rather than just generic AI clippers.

Yes. What you’re describing is usually called rule-based highlight detection, AI clipping, or auto-highlight extraction. The exact workflow—“define signals like applause > threshold, volume spike, laughter, crowd reaction → generate ranked short candidates”—exists, though tools vary in how much control they give you. viblo.aisnipsound.com

Some options:

If you need literal rules like:

• IF applause loudness > -10 dB for 2 seconds
• OR crowd noise increases 8 dB above baseline
• OR transcript contains "wow" / "amazing"
• THEN create a 45-second clip with 8 seconds of lead-in

then you may want a more customizable pipeline rather than a consumer app. A typical stack would be:

1. Audio analysis (FFmpeg + librosa/PyDub) → detect RMS/decibel spikes
2. Speech/transcript analysis (Whisper-style transcription) → keywords, sentiment, quotes
3. Video scoring → face reactions, motion, scene changes
4. Clip generation (FFmpeg) → export candidate MP4s
5. Optional: AI ranking model → “pick the top 10”

For a no-code creator workflow, I’d start with SnipSound/ClickClips-style tools. For a newsroom, sports, conference, or podcast operation where you want your own clipping rules, you’d likely want an API/custom pipeline. clickclips.appsnipsound.com

If you mean live clipping while an event is happening (e.g., a camera feed at a conference automatically producing applause moments), the tool choice changes quite a bit.

Yes. There are tools that can do something close to rule-based highlight detection (e.g., “find moments where applause exceeds X dB,” “flag loud reactions,” “export candidate Shorts”). The exact workflow you’re describing is usually called AI highlight detection, auto-clipping, or event-based clipping.

Some options:

• Viblo — detects audio energy changes like applause, laughter, shouting, and other high-energy moments, then surfaces candidate highlights for review/export. Viblo
• SnipSound — closer to your “set thresholds” idea: it analyzes loudness spikes and lets you tune sensitivity/minimum sustain duration to avoid false positives, then generates candidate clips. SnipSound
• ClickClips — supports audio peak detection, speech triggers, AI detection, configurable clip lengths, and batch export workflows. ClickClips
• Reelium — focuses on reaction signals such as laughter, applause, hype spikes, and chat activity to rank moments worth clipping. Reelium
• Eklipse — automatically scans long videos/streams, scores moments using signals like audio peaks and reactions, then lets you review/export Shorts-style clips. Eklipse

If you want literal rules, look for tools with:

• Audio threshold rules: “clip when loudness > -10 LUFS for 2 seconds”
• Event detectors: applause, laughter, cheering, crowd roar
• Window settings: include 10 seconds before + 20 seconds after the trigger
• Ranking/scoring: combine volume + transcript excitement + visual changes
• Batch export: generate 20 candidates overnight

For a custom pipeline, people often combine:

1. audio analysis (FFmpeg/librosa → peaks, RMS, LUFS),
2. speech-to-text (find keywords/punchlines),
3. an LLM/video model to rank candidates,
4. an exporter (FFmpeg) to render Shorts.

If your footage is podcasts/events with audience applause rather than gaming streams, I’d lean toward the audio-event tools (SnipSound/Viblo-style). If it’s streams, chat/reaction-aware tools tend to work better. Reelium Eklipse

Yes. There are a few tools that get close, but the ability to define explicit rules like “volume peak + applause + X seconds before/after” is less common than generic “AI finds engaging moments.”

Best matches

• SnipSound Highlight Clipper — probably the closest to your example. It detects sustained audio spikes, lets you tune sensitivity/minimum sustain time, ranks the resulting moments, and exports the selected clips. It explicitly works for applause, laughs, crowd reactions, talks, etc. snipsound.com
• ClickClips — has audio-peak detection, speech/keyword detection, and AI detection. It can automatically assemble highlight candidates and lets you control clip duration and ordering. clickclips.app
• Viblo — specifically analyzes volume spikes, laughter, shouting and applause, then surfaces roughly 5–10 candidates for review/export. viblo.ai
• OpusClip — much more general-purpose. Its newer clipping model considers audio, visual and sentiment cues and can accept natural-language instructions, but it's more “find viral moments” than a deterministic rule engine. www.opus.pro

If you mean a rule engine like:

Find every moment where
volume > 90th percentile AND applause detected
→ take 15 sec before + 45 sec after
→ rank by intensity
→ export the top 20 as 9:16 MP4s

That's a more specialized workflow. SnipSound is closest out of the box for the audio-trigger part. Vizard/OpusClip are better if you want the resulting candidates turned into polished Shorts with captions/reframing. Vizard's API, for example, supports maximum clip counts, keywords, preferred lengths, aspect ratios and automated clipping. docs.vizard.ai

If you're thinking about this for podcasts, live events, sports, lectures, or something else, tell me which—I can narrow it down to tools that support the exact triggers you want (audio peaks, applause, laughter, keywords, facial reaction, scene changes, etc.).

Yes. There are a few tools in this space, but the exact workflow you describe—define signal-based rules like “applause > X dB” or “volume peak,” then export candidate shorts—is more specialized than ordinary AI clipping.

Best matches

• Reelium — probably the closest conceptual match. It explicitly detects laughter, applause, audio-energy spikes, chat explosions, and hype spikes, then surfaces candidate moments rather than just blindly generating clips. reelium.ai
• ClickClips — particularly interesting if you want audio-driven rules. Its Audio Detection finds moments from volume spikes, and it can combine audio peaks with AI detection to assemble highlights. clickclips.app
• OpusClip — better if you want general-purpose AI clipping. Its current ClipAnything system analyzes audio, visual, and sentiment cues, and you can give it natural-language instructions for specific moments. It can also export clips/XML. www.opus.pro
• Vizard — good API option if you're building a pipeline. You can specify things like clip length, maximum number of clips, topics/keywords, and output format, then have it generate the candidate clips programmatically. docs.vizard.ai

If you mean literal programmable rules

For example:

Find every moment where audio RMS > 85%, applause detector > 0.7, or crowd reaction rises > 6 dB above the local baseline.
Add −10s/+20s padding.
Merge overlapping events.
Rank candidates.
Export the top 20 as 9:16 MP4s.

That's a slightly different category. I don't see the mainstream AI clippers exposing that level of low-level threshold/rule configuration as a first-class feature.

If that's what you're after, I'd look at building a lightweight pipeline around FFmpeg + audio analysis + an applause/event detector, optionally followed by an LLM/video model to rank the resulting candidates. That gives you deterministic clipping rules while still letting AI decide which candidates are actually worth publishing.

If you tell me what kind of footage you're clipping (podcasts, conferences, comedy, livestreams, sports, etc.), I can point you to the closest existing tool—or sketch the exact pipeline for making this work automatically.

Yes. The closest fit I found is OpusClip, particularly its ClipAnything model.

It can:

• Analyze audio, visual, and sentiment cues to find moments in a long video. help.opus.pro
• Accept natural-language prompts such as “find all the touchdowns” or “find moments most likely to go viral.” help.opus.pro
• Generate multiple candidate shorts automatically.
• Via its API, specify things like clip duration, source-video ranges, topics, and curation model, then retrieve the resulting exportable clips. help.opus.pro
• Trigger downstream actions/webhooks when processing finishes, which makes it suitable for an automated clipping pipeline. help.opus.pro

But there's an important distinction

If by:

“high applause”
“volume peak”
“laughter spike”
“crowd reaction”
“speaker gets excited”

you mean literal measurable rules, rather than an AI prompt, OpusClip isn't quite a rule engine. Its documented approach is multimodal AI + natural-language curation rather than something like:

IF applause_volume > X dB
AND audio_peak > Y
THEN
  create clip ± 15 seconds around event

For that, I'd actually consider a custom pipeline more powerful:

Video → audio analysis → detect events → rank events → generate clips → export candidates

For example:

Applause detector ──┐
Laughter detector ──┤
Volume peaks ───────┤
Speech/keywords ────┼──> score events ──> 30–60 sec clips
Silence/breaks ─────┤
Visual reactions ───┘

You could then have rules such as:

applause_score > 0.8
OR
volume_peak > 95th percentile
OR
(laughter > 0.7 AND speaker_energy > 0.8)

and automatically export, say, the top 20 candidate shorts with 10 seconds before and 25 seconds after each event.

If that's what you're trying to build, I can recommend the best existing tool/API stack for doing exactly that—including options that don't require you to build the audio/event detection from scratch.

Yes. What you’re describing is basically rule-based highlight detection: scan a long recording, detect events such as audio peaks/applause/laughter, generate a window around each event, and export the resulting candidates as Shorts.

A few options:

• AutoClip — probably closest to your example. It explicitly detects laughter, applause, dramatic music drops, and energy peaks, then scores candidate moments and produces short-form clips. autoclip.dev
• OpusClip — more general-purpose. Its API can automatically select the top N moments above a configurable score threshold, and its scoring considers audio dynamics including audience reactions/applause. www.opus.pro
• Eklipse — particularly good for streams. It detects microphone volume spikes, laughter/shouting, game events, etc., and returns ranked clips ready for vertical export. eklipse.gg
• Wisecut — more of an automatic editor; it detects highlights and can produce/export Shorts, but appears less oriented toward explicit custom trigger rules like “applause > X.” www.wisecut.ai

If you specifically want rules like

“Find every moment where applause > threshold OR volume peaks > threshold, grab −15s/+45s around it, eliminate overlapping candidates, then export the top 20”

I'd lean toward AutoClip or a custom pipeline using the OpusClip API. OpusClip's API is particularly interesting because it supports both scoring your own candidate windows and automatic selection above a configurable threshold. www.opus.pro

If you tell me what kind of footage you're clipping (podcasts, conferences, Twitch streams, sports, etc.), I can narrow this down to the tools that actually let you define those triggers rather than just saying “AI, find viral moments.”

Yes. If you mean “define objective signals, scan a long recording, and export the resulting moments as candidate Shorts,” there are a few good fits:

• OpusClip — probably the closest general-purpose option. It automatically finds highlights and supports natural-language instructions for finding specific moments. Its API can also auto-select the top N moments above a configurable score threshold, which is useful if you want to automate the pipeline. www.opus.pro
• Eklipse — particularly good for streams. It scores audio peaks/reactions along with visual/game events, then gives you a ranked candidate list to review and export. eklipse.gg
• AutoClip — interesting if your specific rule is “find applause/laughter/energy peaks.” It analyzes audio for laughter, applause and energy peaks and scores candidate moments. autoclip.dev
• SnipSound Highlight Clipper — the most literal match for rule-based volume clipping. You can set sensitivity and minimum sustain duration, and it creates candidate clips around detected loudness spikes. snipsound.com
• Stream Clipper — an open-source desktop option that lets you combine audio spikes + chat activity, with configurable weighting and automatic merging of adjacent highlights. github.com

If your rules are something like

“Create a candidate whenever applause exceeds X dB for ≥2 seconds, OR volume is ≥Y dB above baseline, then include 15 seconds before and 30 seconds after, merge events within 10 seconds, and export all candidates.”

SnipSound/Stream Clipper are closer to that deterministic workflow than OpusClip. OpusClip is better if you want the system to make a more semantic judgment about whether the moment is actually worth turning into a Short.

If you're looking to build a repeatable clipping pipeline with explicit rules rather than an AI that just guesses the best moments, I can also point you toward tools/APIs that expose the detection thresholds and timestamps directly.

Yes. The closest match I found is OpusClip, specifically its ClipAnything mode.

It can:

• Analyze audio, visuals, and sentiment, not just transcripts. help.opus.proopusclip.canny.iohelp.opus.pro
• Use natural-language prompts such as “find all moments with audience cheering,” “find exciting reactions,” or “find the moments most likely to go viral.” help.opus.pro
• Generate multiple candidate shorts automatically, rank them with a Virality Score, and let you export the resulting clips. opusclip.canny.io
• Be automated through its API, including submitting a long video and getting generated clips back. help.opus.pro

One caveat: if by “rules” you mean literal signal-processing rules like:

if applause > X dB OR peak_volume > Y dB → make a 45-second clip

OpusClip doesn't appear to expose that level of deterministic thresholding. Its interface is more AI/prompt-based than “set audio thresholds.” Its prompts can explicitly reference sounds such as audience cheering, though. help.opus.pro

If you're looking for exactly that rule-based workflow, I can also find tools/APIs that let you define things like LUFS/dB peaks + applause detection + silence + transcript keywords → automatically export candidate clips. That would be a different (and potentially much more programmable) category.

Yes. There are tools that do roughly this: define signals (or let the AI infer them), scan long videos, rank candidate moments, and export Shorts/Reels/TikToks. The exact “rule engine” you’re describing—“clip when applause > X dB, crowd reaction spikes, laughter detected, volume peak sustained for Y seconds”—is more common in event/sports tooling and developer workflows, while creator tools usually hide those rules behind AI scoring. snipsound.comviblo.ai

Some options:

If you want literal programmable rules, you’d typically build a pipeline like:

1. Extract audio features:
   • RMS/loudness peak
   • LUFS jump vs baseline
   • applause classifier
   • laughter classifier
   • crowd-noise classifier

2. Generate candidate timestamps:
   • “volume rises +8 dB for ≥2 sec”
   • “applause probability > 0.8”
   • “laughter detected within 5 sec of punchline”

3. Expand windows:
   • start 15 seconds before trigger
   • end 30 seconds after trigger

4. Export:
   • 9:16 crop
   • captions
   • ranked candidate list

For a non-coding workflow, I’d start with SnipSound/ClickClips-style tools if your signals are mostly audio. For podcasts, conferences, sports, or live events where “applause” is the key trigger, those are closer to your “rules → candidates” idea than generic viral-clip generators. clickclips.appsnipsound.com

If you mean a tool you can configure like Zapier/IFTTT (“when applause > threshold, create a clip”), there are fewer polished options; you may want an API-based workflow instead.

Yes. If you specifically want rule-based signals like “clip whenever applause gets unusually loud” or “find volume peaks,” there are a few options:

• SnipSound Highlight Clipper — probably the closest match to what you described. It analyzes loudness in dB, establishes a baseline, lets you tune sensitivity and minimum sustain time, then produces a ranked shortlist with proposed clip boundaries. It explicitly targets applause, laughs, crowd reactions, etc. snipsound.com
• Eklipse — particularly good for streams. It detects audio peaks/reactions and combines them with visual/game signals, then gives you candidates to review and export. eklipse.gg
• Made / Remi Smart Clipping — detects audio energy, scene changes, speaking patterns and natural cuts, and returns candidate segments with reasons and in/out points. www.rhei.ai
• AutoClip — interesting if you want something more programmable: it combines video/transcript analysis with audio detection for laughter, applause, music drops and energy peaks, then scores candidate moments. autoclip.dev
• OpusClip — more AI/editor-oriented than rule-oriented. It finds highlights automatically and supports natural-language requests, but it isn't primarily a “applause > X dB → make candidate” rules engine. www.opus.pro

If you want actual custom rules

Something like:

IF applause > 85 dB
AND duration > 1.5 sec
THEN create 45-sec candidate
WITH 12 sec pre-roll
AND 8 sec post-roll

OR

IF volume_peak > 2.5 × local_baseline
THEN candidate

OR

IF laughter + chat_velocity spike
THEN candidate

then SnipSound is the closest off-the-shelf fit I found for the audio side. For a more sophisticated pipeline, AutoClip or an API-based system would give you more room to combine signals. OpusClip's API also now supports supplying your own candidate windows and scoring them, as well as automatically selecting top moments above a configurable threshold. www.opus.pro

If you tell me what kind of footage you're clipping (podcasts, live events, sports, Twitch, speeches, etc.), I can narrow this down to the 2–3 tools that best support your exact rules.

Yes. The closest match I found is OpusClip, especially its ClipAnything mode. It can use audio, visual, and sentiment cues and lets you give natural-language instructions for what moments to clip. It then generates multiple candidate shorts and lets you export them. help.opus.pro

For your specific example—“find moments with high applause / crowd reaction / volume peaks”—there are a few levels:

• OpusClip / ClipAnything: Best if you want AI judgment plus instructions such as “find moments where the audience reacts strongly.” It supports custom prompts and batch clip generation. help.opus.pro
• ClickClips: More explicitly rule-like. Its Audio Detection finds moments from volume spikes, and it can set clip duration, number of clips, and ordering. clickclips.app
• SnipSound Highlight Clipper: Particularly interesting for your applause example. It measures loudness against the video's baseline, lets you tune sensitivity and minimum sustained duration, ranks the resulting moments, and proposes clips. It runs locally in the browser. snipsound.com

So if by “rules” you mean something like:

volume > baseline + 8 dB
sustained for > 2 sec
add 10 sec before + 20 sec after
generate top 20 candidates
export all as MP4

then SnipSound/ClickClips are closer to that deterministic workflow, while OpusClip is better if you want semantic/AI selection.

If you're trying to build an automated pipeline rather than use a consumer editor, I can also find APIs/tools that let you define these detection rules programmatically and batch-export the clips.

Yes. If your key requirement is “set signal-based rules → get candidate clips → export,” there are a few tools worth looking at.

• Eklipse — probably the closest match if you're clipping streams. Its current detection scores audio peaks, shouting/laughter, gameplay events, and chat reactions, then ranks candidate moments for review/export. It also supports your own instructions for what to clip. eklipse.ggreelium.ai
• Reelium — especially interesting for your applause/reaction example. It explicitly detects laughter, chat explosions, applause, hype spikes and audio spikes, then ranks moments from the whole VOD. reelium.ai
• Remi / Rhei Smart Clipping — more general-purpose. It analyzes audio peaks/energy, scene changes, speech patterns and natural start/stop points, producing proposed segments with in/out points that you can export. www.rhei.ai
• OpusClip — excellent general AI clipper, but it's more oriented toward AI determining what is interesting than giving you a precise volume > X AND applause > Y rule system. It does generate, score, edit and export Shorts. www.opus.pro

If you mean literal programmable rules

Something like:

candidate =
    volume_peak > 90%
    OR applause_detected
    OR laughter_detected

then:
    take -12s → +35s around event
    discard if silence > 4s
    rank by peak intensity
    export top 20 as 9:16

That's a somewhat different category. Most consumer AI clippers expose “tell the AI what you want” rather than a true rule engine. Eklipse comes relatively close, while Remi exposes the underlying signals more explicitly.

If you tell me what kind of footage you're clipping (podcasts, live events, Twitch, sermons, sports, DJ sets, etc.), I can find the best tool that supports actual threshold-based rules rather than just generic “viral moment” AI.

Yes. There are tools that get pretty close to “define signals → scan the recording → give me candidate clips → export them.”

The closest matches I found:

• SnipSound Highlight Clipper — probably the closest to your exact example. It detects loudness spikes relative to a baseline, lets you tune sensitivity and minimum sustain time, and creates proposed clips with configurable lead-in/length. It specifically targets applause, laughs, punchlines, cheers, etc., and can export the selected reel. snipsound.com
• Viblo — detects volume spikes, laughter, shouting, applause, plus visual activity, then surfaces roughly 5–10 candidate highlights for review. snipsound.comviblo.aicut.promediacopilot.io
• Eklipse — particularly good for streams. It scores audio peaks and reactions alongside events and produces a ranked clip list that you can edit/export as vertical Shorts. eklipse.gg
• Cut.Pro — analyzes energy peaks, reactions, structure, and speaker dynamics, then produces scored vertical clips with captions/reframing and export/publishing options. cut.pro
• SupoClip — interesting if you want something you can self-host/control. It does segment detection, virality scoring, face-aware crops, captions, and platform exports. www.supoclip.com

If you mean literal programmable rules

For example:

Find moments where applause is > X dB above baseline OR volume peak > Y dB,
take 15 seconds before + 30 seconds after,
merge events within 20 seconds,
rank by peak intensity,
export the top 20 as 9:16 MP4s.

SnipSound is the closest off-the-shelf fit I found because its detection is explicitly based on adjustable audio thresholds/sustain time rather than just asking an AI to guess what is “viral.” snipsound.com

If you want arbitrary combinations of signals—e.g. applause + laughter + transcript keyword + scene change + volume peak—that's more of an automation/custom-pipeline problem. There are tools with pieces of this (for example, MediaCopilot detects applause, scene changes and topic shifts), but I didn't find a polished consumer tool that exposes all of those as a true rule engine. mediacopilot.io

If you tell me what kind of footage you're clipping (podcasts, stand-up, conferences, sports, livestreams, etc.), I can narrow this down to the best 2–3 options for that workflow.