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Agent skill

Review Ugc Render

by gooseworks-ai in gooseworks-ai/goose-skills

Mandatory pre-publish review gate for a UGC video render. An agent skill from gooseworks-ai/goose-skills.

MITAuto-check passedMarketing & SEO

Install Review Ugc Render

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill review-ugc-render -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills review-ugc-render --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ads/packs/ugc-video-formats/review-ugc-render .claude/skills/review-ugc-render && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-ugc-render
GitHub stars
1.2k
Token cost
~3.4k tokens
SKILL.md length
1,714 words
Files
4 (incl. scripts)
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Mandatory pre-publish review gate for a UGC video render. An agent skill from gooseworks-ai/goose-skills.

  • Works in 2 steps: Where the term's words appear in the… → A differing span is accepted as the same…
  • Tasks that involve Influencer and creator marketing
  • SKILL.md covers Why this exists, When to run, Contract and What counts as the same speech, plus 5 more sections
  • Runs Python scripts from its folder; calls python3; needs OPENAI_API_KEY

What it does

Review Ugc Render is an agent skill from gooseworks-ai/goose-skills. 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 (videoprojectupsert patch.finalrenderid) — 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…

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `scripts/review_render.py`, `skill.meta.json` and `tests/test_review_render.py`).

It sits in Marketing & SEO, covering Influencer and creator marketing, Transcription and Language learning. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Influencer and creator marketing
  • Tasks that involve Transcription
  • Tasks that involve Language learning

Example prompts

  • “human-vetted”
  • “human witted”
  • “said”
  • “/review-ugc-render”

Requirements

  • Python 3
  • A credential in OPENAI_API_KEY

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Where the term's words appear in the script, a substitution or drop there is
  2. A differing span is accepted as the same brand (reported [low], counted as a

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Ugc Render loads about 3.4k tokens when it runs. Until then it costs about 223 tokens; SKILL.md has 1,714 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~223
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,714 words, ~3,382 tokens.

Download SKILL.mdSave it as .claude/skills/review-ugc-render/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
review-ugc-render
description
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.
owner
akhil
status
superseded
version
2.0.1
created
2026-07-04
updated
2026-10-06
superseded_by
check-layer@1.1.1

Superseded: the video kit now does this with the check-layer part, version 1.1.1, 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.

Show full SKILL.md (657 more words)Show less

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.

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in skills/ads/packs/ugc-video-formats/review-ugc-render of gooseworks-ai/goose-skills.

  • SKILL.md
  • scripts/review_render.py
  • skill.meta.json
  • tests/test_review_render.py

Open the folder on GitHubat commit c650c6d

Compare with similar skills

Review Ugc Render next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Ugc Render compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Ugc Render this skillgooseworks-ai/goose-skills1.2k—~3.4kAutomated safety check: PassMIT
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Algo Net Influenceasgard-ai-platform/skills242—~1.1kAutomated safety check: PassMIT
YouTube Captions FetcherZeroPointRepo/youtube-skills1k1 repos~1.1kAutomated safety check: PassMIT
Podwisehardhackerlabs/podwise-cli413—~1.4kAutomated safety check: PassMIT
Audience ResearchScrapeCreators/social-media-research-skills3.4k—~635Automated safety check: NotesMIT

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Categories

Questions about Review Ugc Render

What does Review Ugc Render do?

Mandatory pre-publish review gate for a UGC video render. An agent skill from gooseworks-ai/goose-skills. Review Ugc Render is an agent skill from gooseworks-ai/goose-skills. Mandatory pre-publish review gate for a UGC video render.

When should I use Review Ugc Render?

Review Ugc Render fits situations like: tasks that involve Influencer and creator marketing; tasks that involve Transcription; tasks that involve Language learning.

How do I install Review Ugc Render in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill review-ugc-render -a claude-code`. Or copy the skill folder (skills/ads/packs/ugc-video-formats/review-ugc-render in gooseworks-ai/goose-skills) into .claude/skills/review-ugc-render in your project. Claude Code loads it when a task matches its description.

How do I install Review Ugc Render in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill review-ugc-render -a codex`. Or copy the skill folder (skills/ads/packs/ugc-video-formats/review-ugc-render in gooseworks-ai/goose-skills) into .agents/skills/review-ugc-render in your project. Codex loads it when a task matches its description.

Can I use Review Ugc Render in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill review-ugc-render -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-ugc-render, .gemini/skills/review-ugc-render, .github/skills/review-ugc-render and .opencode/skills/review-ugc-render in your project.

What does Review Ugc Render need to run?

Going by SKILL.md and its folder, Review Ugc Render needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named OPENAI_API_KEY. Our summary lists: Python 3; A credential in OPENAI_API_KEY.

Does Review Ugc Render access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Ugc Render safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Review Ugc Render use?

Review Ugc Render is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Ugc Render use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Ugc Render?

Skills that share tags, products or a category with Review Ugc Render: Scenario Video Assembly (scenario-labs/skills, 946 stars), Algo Net Influence (asgard-ai-platform/skills, 242 stars), YouTube Captions Fetcher (ZeroPointRepo/youtube-skills, 1k stars) and Podwise (hardhackerlabs/podwise-cli, 413 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Ugc Render?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.