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Review Ugc Render

OrganizationPopular
gooseworks-ai
review-ugc-render

Mandatory pre-publish review gate for a UGC video render. Transcribes the finished render's AUDIO with Whisper and word-diffs it against the approved spoken script, then gates pinning the final render (video_project_upsert patch.final_render_id) — blocking a render whose generated audio mis-voices a word (e.g. the approved "human-vetted" spoken as "human witted"), says a different number or brand name, flips a negation, drops an approved phrase, or comes back silent. Correct speech written differently ("5mg" said "five milligrams", "30%" said "thirty percent", a spoken URL, "don't" said "do not", "braxleybands" said "braxley bands", a confirmed pronunciation like "AG1" said "A G one") passes. Runnable, gating counterpart to content-goose's review-transcript-integrity atom. Every ugc-video-formats recipe runs this after render and BEFORE pinning the final render.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill namereview-ugc-render
Stars
1.2K
Forks
216
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Review Ugc Render 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/gooseworks-ai/goose-skills/tree/main/skills/ads/packs/ugc-video-formats/review-ugc-render
  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/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/ads/packs/ugc-video-formats/review-ugc-render .claude/skills/review-ugc-render
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Ugc Render 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 Review Ugc Render 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 Review Ugc Render 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.

Superseded: the video kit now does this with the check-layer part, version 1.2.0, in the parts folder of this repository. It runs the same speech-against-script rules on every video with speech: numbers, units, negations and brand names must match, and confirmed pronunciations count as the written name. This atom stays, unchanged in behaviour, for skills outside the kit until they move; its scripts still run.

review-ugc-render

The QC gate every UGC video recipe MUST clear before it publishes. Not an eyeball /watch — a deterministic transcript-vs-script diff that exits non-zero on a defect so the recipe can hard-stop pinning the final render (video_project_upsert patch.final_render_id).

In short: it compares what the render actually SAYS with the script the user approved. Correct speech that is only written differently passes (numbers, units, URLs, contractions, fused brand names, confirmed pronunciations). A wrong number, a flipped negation, a mis-voiced word or brand, a dropped phrase or silence fails.

Why this exists

Seedance generates the audio natively. It sometimes mis-voices a word — the approved line human-vetted comes back spoken as human witted; documented siblings: Hume→Hune, Alitu→al-too. The defect lives in the render's audio, so an eyeball /watch ("dialogue matches the script") slips it through, and a downstream caption pass then bakes the wrong word in verbatim. Nothing was comparing the actual spoken audio against the script the user approved.

This gate does exactly that, deterministically, and refuses to publish on a miss.

When to run

  • MANDATORY in every remix-ugc-*-from-sample and create-ugc-*-video-from-refs recipe, in the QC phase, after the master render exists and before pinning it as the final render (video_project_upsert patch.final_render_id).
  • Re-run after every fix / re-roll until it PASSES.

Contract

Before rendering, persist the exact approved spoken lines (the verbatim utterance, no beat notes) to working/approved-script.txt. Then, after render:

bash
python3 <pack>/review-ugc-render/scripts/review_render.py \
  --video working/final.mp4 \
  --script-file working/approved-script.txt \
  --json working/review-verdict.json

When the voice-over used confirmed pronunciations, add them (recommended): --pronunciations working/brand-rules.json — the same file create-vo-elevenlabs read (its --rules brand-rules.json, or the read_pronunciations.py output; both carry a pronunciations: [{term, say_as}] list). Leave the flag out when there is no such file: a missing file is an ERROR (exit 3).

  • exit 0 → PASS — proceed: pin the final render (video_project_upsert patch.final_render_id).
  • exit 2 → FAIL — do NOT pin it. Read the report, fix, re-run.
  • exit 3 → ERROR — the check could not run (see below); fix the environment or the input, do not publish blind.

Transcription backend (in priority order): the GooseWorks whisper-proxy (CLI credentials or the sandbox token) → OPENAI_API_KEY (honors OPENAI_BASE_URL) → local whisper CLI. ffmpeg must be on PATH.

What counts as the same speech

Both the script and the transcript are put in one canonical spoken form before the diff. The rules are bounded — each is an exact rewrite, never a fuzzy match.

WrittenHeardRule
49, 105, 2,500, 1 millionforty-nine, one hundred and five, two thousand five hundred, a millionnumber words = digits
2.5, 2026, 249, 1st, 2ndtwo point five, twenty twenty six, two forty-nine, first, seconddecimals, years and prices read in pairs (only when spoken as words: written 2 20-minute is never 220), ordinals (second = 2nd only when the script writes 2nd)
No. 1, No.1, #1number onenumber sign, only when written with the dot or # (no 1-star reviews and no one stay negations)
5mg, 30g, 500ml, 12oz, 10 lbs, 30-dayfive milligrams, thirty grams, …, thirty daysunit after a quantity (weights, volumes, %, money, hours/minutes/seconds, days/weeks/months/years, calories, x times)
30%thirty percent / 30 per centpercent
$49, $49.99forty nine dollars, forty nine dollars and ninety nine centsmoney
braxleybands.com, www.example.combraxleybands dot com, w w w dot example dot com, example dot comURL; www. is optional
don't, can't, it's, you'redo not, cannot / can not, it is, you arecontractions
Braxleybands, Gooseworks, everyone, OneSkinBraxley Bands, goose works, every one, One Skinfused/split: exact join of 2–3 words
AG1AG1, AG one, A.G. onewritten forms, no alias needed
AG1A G one, A G 1 (letters spaced out)only with a confirmed alias

Guards that keep the rules honest:

  • A unit word not after a quantity is left alone — a brand "MG" never becomes "milligrams".
  • Letters spelled one by one ("A G") are not fused into a word. That needs a confirmed pronunciation.
  • Fusion is exact concatenation. "Braxly Bands" is not "Braxleybands".
  • A join never swallows a negation: "no table" is not "notable" ("no thing" is "nothing").
  • A number word joins a word only when every part has 2+ letters: "every one" is "everyone", but "G one" is not "gone".

What still FAILS

Report lineRoot causeFix
[high] said "59" where script has "49" — number differs…Wrong, added or dropped numberRe-roll. A number is never a benign paraphrase.
[high] dropped "5mg" — unit differs…A unit after a number was changed, added or dropped ("5mg" said "five")Re-roll. Only a dropped dollars/euros/pounds alone ("$9.99" said "nine ninety-nine") is not HIGH; dropped cents is.
[high] extra "doesn't" … negation changedA not/never/no/without was added or lost — the claim flipsRe-roll.
[high] said "Hune" where script has "Hume" — brand name not heard as approvedBrand mis-voiced or dropped (--brand-term / confirmed pronunciation)Re-roll; spell it phonetically in the SPOKEN LINE (e.g. Ali-too, never a (pronounced …) parenthetical). See create-video-seedance-2-fal Failure Modes.
[high] said "witted" where script has "vetted" — audio likely mis-voices…Seedance mis-voiced a similar-looking wordRe-roll a new seed.
[medium] dropped "…" / low similaritySeedance dropped an approved phraseRe-roll; if only a tail word, a surgical stitch_replacement.py window fix may recover it.
[low] extra "…" + low similarityExtra speech beyond benign fillerRe-roll. A single filler word ("so", "okay") in a normal-length line is LOW and passes.
⚠ audio is effectively silentWrong render / audio track lost in postRe-render / re-check the mux; never publish a silent take.
ERROR: no transcription backendNo proxy credentials, no OPENAI_API_KEY, no local whisperSign in / set the key / install whisper, then re-run.
ERROR: alias … would change a number, unit or negationA bad --alias or pronunciation entryFix the alias. Aliases may only respell a name.

The verdict passes only when similarity ≥ --min-ratio and there is no HIGH issue. Numbers, units, negations and declared brand names fail on their own (HIGH). Any other dropped or extra word is medium/low: it lowers the similarity, and fails the gate only when the similarity drops below --min-ratio (so one dropped ordinary word in a long line can pass). --expect-music is advisory only; it does not by itself fail the gate.

Brand names and confirmed pronunciations

Brand words are never removed from the diff. (Before 2026-10-06, --brand-term fuzzily stripped brand-like words, which let "Hune" pass for "Hume". That is gone.)

  • --pronunciations PATH (recommended when the voice-over used one) — the confirmed pronunciations file: create-vo-elevenlabs's brand-rules.json or the output of its scripts/read_pronunciations.py ({"brand_id", "basis", "pronunciations": [{"term", "say_as", "fact_id"}]}). Every entry becomes an alias and its term a brand term. An entry that cannot be used (see alias rules) is skipped with a WARNING, never fatal. A missing or unreadable file is an ERROR (exit 3), so pass the flag only when the file exists.
  • --alias "TERM=SPOKEN" (repeatable) — one confirmed spoken form, e.g. --alias "AG1=A G one". The term also becomes a brand term. A bad --alias is an ERROR (exit 3), checked before any transcription is spent.
  • --brand-term TERM (repeatable) — marks a brand name. Exactly:
    1. Where the term's words appear in the script, a substitution or drop there is HIGH (a brand mis-voicing). Its fused/split forms count as equal.
    2. A differing span is accepted as the same brand (reported [low], counted as a match) only when all of these hold: it has no negation, number or unit change; every word on both sides is an alphabetic word of some --brand-term; and the two sides look alike (character similarity ≥ 0.6). This keeps the older calling pattern working: a script written in the spoken form (Try ak-mee today) with --brand-term Acme --brand-term ak --brand-term mee passes when Whisper writes Try Acme today ("ak mee" vs "acme" = 0.67). Everything else still fails HIGH: a heard word that is not a declared term (Hume heard Hune), two different declared names (Hims vs Hers, Hume Body Pod vs Hume Band), a truncation (Acme heard ak), or a number inside a term (Pod 4 vs Pod 5, 7-Eleven: 7 days vs 11 days). Prefer --pronunciations over passing say_as words as --brand-term.

Rules for aliases:

  • Only pass spoken forms the user confirmed (saved brand pronunciations, or a form they confirmed in chat). Never invent one from the transcript to make the gate pass.
  • An alias may respell a name, digits and number-like syllables included ("AG1" = "A G one", "Tenzing" = "ten-zing", "Notion" = "NO-shun"). It is refused when the written term has a number, unit or negation that the spoken form changes ("AG1" = "A G two"), when the spoken form adds not, never, without, none, nothing or nobody ("Hume" = "never Hume"; no and nor are fine as syllables, "Nomad" = "no-mad"), or when the spoken form is only numbers, units or negations ("Decagon" = "five").
  • If you listened and the audio is right but Whisper spelled a coined brand name in a new way, ask the user to confirm that spelling, save it as a pronunciation, and re-run.

Inputs

  • --video PATH (required) — the rendered master mp4.
  • --script-file PATH or --script "text" — the approved spoken script. Omit both only for a genuinely script-free clip (the drift check is then skipped and the gate is advisory).
  • --brand-term TERM, --alias "TERM=SPOKEN", --pronunciations PATH — see above.
  • --min-ratio FLOAT (default 0.90) — transcript↔script similarity to pass, measured on the canonical spoken form.
  • --captions-srt PATH — optional SRT to check for caption-text defects.
  • --json PATH — write the machine verdict for the app's review panel. Each issue carries the canonical tokens (script_words / heard_words) and the original wording (script_text / heard_text).

Tests

bash
python3 tests/test_review_render.py    # or: python3 -m pytest tests/

Pure Python, no audio, network or paid call. Covers the QA-71 audit fixture table (units, URL, numbers, percent, contractions, fused brands, wrong price, negation, Hume→Hune), number words, units (including added/dropped units in long lines), URLs, negation flips, fused/split words, CLI-style brand terms, confirmed AG1 aliases and saved pronunciations with number-like syllables, omission, extra speech, the report wording, and the CLI exit codes with transcription stubbed out.

Relationship to the content-goose review engine

This is the shipped, single-file, gating slice of the fuller coworkers/video/molecules/review/review-loop (18-axis rubric). Here we enforce the one axis that catches audio-vs-script defects at publish time (review-transcript-integrity / brand_text_accuracy). Deeper multi-axis review stays in the content-goose lab.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Review Ugc Render AI skill do?

Mandatory pre-publish review gate for a UGC video render. Transcribes the finished render's AUDIO with Whisper and word-diffs it against the approved spoken script, then gates pinning the final render (video_project_upsert patch.final_render_id) — blocking a render whose generated audio mis-voices a word (e.g. the approved "human-vetted" spoken as "human witted"), says a different number or brand name, flips a negation, drops an approved phrase, or comes back silent. Correct speech written differently ("5mg" said "five milligrams", "30%" said "thirty percent", a spoken URL, "don't" said "do...

Why use Review Ugc Render on TypingMind?

Because you install it once and use it with any model. Review Ugc Render 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 Review Ugc Render in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/packs/ugc-video-formats/review-ugc-render. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Review Ugc Render?

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 Review Ugc Render?

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

Is the Review Ugc Render AI skill free?

Yes. It is published on GitHub by gooseworks-ai 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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