CD 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 keeping the render as the final version (video_review) — 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", "larkfieldstraps" said "larkfield straps", a confirmed pronunciation like "TG1" said "T G one") passes. Runnable, gating counterpart to GooseWorks' fuller transcript-integrity review. Every ugc-video-formats recipe runs this after render and BEFORE keeping the final version.
Mandatory pre-publish review gate for a UGC video render.
As a process D 40/100 · Unfinished process — References files that are not bundled: scripts/read_pronunciations.py
The same skill appears in 1 more place: goose-skills
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
✓ No critical or high findings
Medium and low: 1
-
medium Exfiltration
net-redirectable-api-keyscripts/review_render.py:873Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: scripts/read_pronunciations.py - note
frontmatter-keyunknown frontmatter key "status" - note
frontmatter-keyunknown frontmatter key "superseded_by"
Process rating: all ten parameters 40/100
- 0Tools and files. 1 referenced file(s) missing: scripts/read_pronunciations.py
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 21 mutating operations with no state check
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 24 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3198 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 864: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 9 example trigger phrases
- +4Structure: 10 headings
- +3Step-by-step instructions: 24 items
- +4Has examples (2 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.