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Suggest Sfx

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hassancs91
suggest-sfx

The SFX pass. Analyze a short's beats + narration and propose tasteful sound effects synced to them, drawing from (and growing) a shared, reusable SFX library, then render an SFX-mixed audition preview. Use when the user wants to "add SFX / sound effects", "suggest sfx", "score the transitions", "sound-design this beat", generate/source sound effects, build or extend the sfx library/catalog, author or audit a sfx-plan, or mix SFX over a rendered short in this repo. Covers reading beats + word times + brand §7, the library-first flow, generating misses with the ElevenLabs Sound Effects API, the per-video sfx-plan.json, the hard user-audit gate, and mixing with tools/mix_sfx.py (light voice ducking).

Overview

Publisherhassancs91
Repositoryclaude-faceless-shorts-creator
Skill namesuggest-sfx
Stars
279
Forks
105
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by hassancs91 on GitHub. Read the source before you install it.

Installation

Install the Suggest Sfx AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/hassancs91/claude-faceless-shorts-creator/tree/main/.claude/skills/suggest-sfx
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hassancs91/claude-faceless-shorts-creator.git /tmp/claude-faceless-shorts-creator
mkdir -p .claude/skills
cp -r /tmp/claude-faceless-shorts-creator/.claude/skills/suggest-sfx .claude/skills/suggest-sfx
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Suggest Sfx in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Suggest Sfx on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Suggest Sfx is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

suggest-sfx — the SFX pass

Take a rendered, voiced short and add tasteful sound effects, synced to the visual beats and the narration, drawn from a shared, growing SFX library. Work collaboratively and beat-by-beat; the user audits the plan and gives notes.

The shape: a declarative plan (sfx-plan.json) is the source of truth → a tool consumes it (tools/mix_sfx.py) → a library grows (media/library/sfx/) → a hard USER-AUDIT gate before anything is mixed.

Sound taste is a brand contract — read brand.md §7 every time. The house style is calm/premium (Linear / Anthropic / Vercel), felt-not-heard, always under the voice. NOT MrBeast-loud.

Inputs (read these first, every session)

  • brand.md §7 (Sound design) — the SFX taste contract: subtlety, density, palette, levels, sync, signature motifs, source policy. Non-negotiable.
  • shorts/short-N-<niche>/beats.json — the beats and REAL per-word voice times (written back by gen_voice.py). Sync cues to these words (a pop on "boom", a chime on "free").
  • The shot source remotion/src/shots/short-N/*.tsx — the INTERNAL animation frames are where the real visual beats are (piece lands, digit pops, badge stamps). Read the shot, convert its local frame to global time: at_s = (local_frame + sequence_from) / fps. Do not guess — the cue must land on the exact frame the thing happens.
  • media/library/sfx/catalog.json + palette.json — the library you draw from and grow.

The library (media/library/sfx/) — the durable asset

  • palette.json — generation recipes: generic, reusable sound ids + prompts + duration + tags. The source of truth for what the library SHOULD contain.
  • catalog.json — the manifest of what EXISTS: {id, file, category, tags, duration_s, peak_dbfs, loudness_lufs, source, model, license, prompt, used_in} per clip. Written by gen_sfx.py.
  • clips/*.mp3 — loudness-normalized clips (~−20 LUFS, −1.5 dBFS ceiling) so a plan's per-cue gain_db is perceptually meaningful.
  • Library-first, always. Reuse an existing clip before generating. Name/tag clips GENERICALLY (ui-toggle-on, whoosh-soft, pop-reveal) so future videos reuse them — the library is the durable asset, each video is one draw from it. Only genuinely-missing sounds get added to palette.json and generated.

Workflow

  1. Read brand §7 + beats.json + the shot source + catalog. Pull exact cue times: for each candidate beat, find the visual moment's local frame in the shot and/or the narration word, and convert to global seconds.
  2. Decide taste with the user up front if unsettled (present-ness, density, which kinds, source) — then apply brand §7. Don't re-ask settled decisions.
  3. Propose the cue list — function-first. For each beat ask what it needs (brand §7): motion (whoosh) · tension (riser) · emphasis (impact/pop) · snap (click). Score key transitions, reveals, and tasteful click-sequences; a 3–4-shot click-sequence counts as ONE gesture. Mark genuinely deniable texture as "optional": true. Layer the 2–3 biggest moments (build-and-drop): riser→impact on a scripted reveal, whoosh→pop so a cut stands out — in the plan a layer is two events at the same/adjacent at_s that sum. Sync each cue to the VISUAL beat and often the exact word.
  4. Library-first sourcing. For each distinct sound the plan needs, reuse a catalog clip if one fits. For misses, add a generic recipe to palette.json and generate: python tools/gen_sfx.py (ElevenLabs Sound Effects API → normalized clip → catalog). Fall back to curated royalty-free only where generation is weak; record source + license either way.
  5. Author shorts/short-N-<niche>/sfx-plan.json — one event per cue, at_s in global seconds. (Schema below.)
  6. USER-AUDIT GATE (hard). Present the resolved cue sheet (python tools/mix_sfx.py <plan> --print) and get the user's approval/notes BEFORE mixing.
  7. Mix the audition preview once approved: python tools/mix_sfx.py shorts/short-N-<niche>/sfx-plan.json → the plan's render.out. Light sidechain duck under the voice + a safety limiter. Iterate on the user's notes (gains, timing, add/cut cues) — re-print, re-mix.
    • Verify audibility with numbers, not hope. After mixing, RMS-diff the mixed audio vs the voice-only preview at each cue window: a story-critical cue should add ≥ +4 dB, texture +1–3 dB. Inaudible cues hide in cue sheets — don't ship a cue you haven't confirmed lands.
    • Transient-clip gain gotcha. Short percussive clips (knock/stamp/keys/snap) hit the −1.5 dBFS peak ceiling BEFORE reaching the −20 LUFS loudness target, so they catalog ~3–5 dB quieter than sustained clips. Their plan gain_db must be ~3–5 dB higher than the brand table implies. The audibility check above is what surfaces this.
    • SHORTS calibration. Under a short's near-continuous narration (~2.7 w/s, few gaps) a conservative gain table is 4–8 dB too quiet across the board — the duck + voice masking eat everything. Proven landing zone: transitions/whooshes −3, story pops/impacts 0..+3, stamps/snaps 0..+7, layered-hero risers 0..+2. Start a short's plan there.
    • Cues that sit fully UNDER continuous speech never measure — accept felt-not-heard or cut them. The sidechain duck suppresses quiet clips the whole time the voice is active (a click-sequence can measure +0 dB at ANY gain). Set such cues at a sane +6-ish and let them live in the word gaps, or delete them; do NOT chase them with gain (a pause would make them spike).
    • Measure transients with tight windows. RMS over 0.6s dilutes a 50 ms click to nothing and an adjacent loud cue can leak in and fake a pass. Use ~0.3s windows for snap/pop/zap/stamp, and measure a riser at its final third (its energy is at the END).
    • A noisy TEXTURE cannot be fixed by gain. Static/glitchy/hummy clips read as NOISE over speech even at −4 dB; lowering them just makes quiet noise. If the user says "noisy", swap the sound's CHARACTER (clean mechanical snap) or use silence — see brand §7 "no static/glitch textures under narration".
  8. Save back. New clips stay in the library + catalog (they carry to the next video). Update used_in.

sfx-plan.json (the source of truth)

jsonc
{
  "master": "remotion/out/ShortNName-voiced.mp4", "master_fps": 30,
  "catalog": "media/library/sfx/catalog.json",
  "render": {
    "preview": "remotion/out/ShortNName-voiced.mp4",   // the voiced render to mix over
    "out": "shorts/short-N-<niche>/output/short-N-sfx.mp4",
    "end_s": 42, "duck": true
  },
  "events": [
    { "at_s": 4.9, "sfx_id": "chess-piece-thock", "gain_db": -8, "shot": "MainScene",
      "cue": "e4 pawn lands (local f51)", "note": "diegetic board foley" },
    { "at_s": 13.03, "sfx_id": "ui-click-soft", "gain_db": -18, "optional": true, "cue": "card locks" }
  ]
}
  • at_s — global-timeline seconds (same clock as beats.json).
  • gain_db — dB relative to the clip's normalized level (lower = quieter). See the SHORTS calibration above for starting values.
  • optional: true — deniable texture; mix_sfx.py --no-optional drops it. Use it liberally so the audit is about the core set.
  • cue / note — human-readable anchor (the exact frame/word) so the audit is legible.

Tooling quick reference

  • Grow the library: python tools/gen_sfx.py [--dry-run] [--only id1,id2] [--force] [--renorm]. --renorm re-balances existing clips to the loudness target (no API/billing). Needs ELEVENLABS_API_KEY in .env.
  • Print the cue sheet (the audit artifact): python tools/mix_sfx.py <plan> --print [--no-optional].
  • Mix: python tools/mix_sfx.py <plan> [--no-optional] [--no-duck] [--end S] [--out path].

Principles (the house style — apply them)

  • Under the voice, always. SFX are seasoning; the voice is the show. Duck them, keep them quiet, never let one peak above the narration. Silence is part of the mix.
  • Key transitions & reveals only — plus the diegetic action sounds that ARE a synthetic short's content (piece thocks, digit pops). Everything past that is optional.
  • Sync to the visual beat AND the word. A click exactly on the toggle flip; a pop exactly on the reveal. Off-by-100ms reads as sloppy — use real frame/word times.
  • Reusable-first. Generic names + tags so the library compounds across videos. Reuse before you generate. The library is the deliverable that outlives this video.
  • Function over vibe. Pick the sound by its job — motion/tension/emphasis/snap (brand §7) — not by browsing the catalog for something that "feels right." The same few foundational sounds do the heavy lifting; more is not better. Keep signature motifs consistent.
  • Layer the big moments, keep the rest single. Build-and-drop (riser→impact, whoosh→pop) is where SFX earn their keep — but only on the 2–3 hero beats.
  • Take structure, not drama. Calm/premium: no cymbal-urgency risers, no trailer slams, no whoosh on every move.
  • Audit before mixing. The user reads and approves sfx-plan.json first. Non-negotiable.

Done = the library has the needed clips (catalogued with source+license), sfx-plan.json is authored and audited by the user, the SFX-mixed preview is rendered and spot-checked by ear, and any new clips + used_in are saved back to the library.

Frequently asked questions

What does the Suggest Sfx AI skill do?

The SFX pass. Analyze a short's beats + narration and propose tasteful sound effects synced to them, drawing from (and growing) a shared, reusable SFX library, then render an SFX-mixed audition preview. Use when the user wants to "add SFX / sound effects", "suggest sfx", "score the transitions", "sound-design this beat", generate/source sound effects, build or extend the sfx library/catalog, author or audit a sfx-plan, or mix SFX over a rendered short in this repo. Covers reading beats + word times + brand §7, the library-first flow, generating misses with the ElevenLabs Sound Effects API,...

Why use Suggest Sfx on TypingMind?

Because you install it once and use it with any model. Suggest Sfx is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Suggest Sfx in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hassancs91/claude-faceless-shorts-creator/tree/main/.claude/skills/suggest-sfx. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Suggest Sfx?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Suggest Sfx?

As many as you like. As long as a model supports skills, you can use Suggest Sfx with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Suggest Sfx AI skill free?

Yes. It is published on GitHub by hassancs91 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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