BB byted-byteplus-vod-video-enhancement
Upload video/audio media to BytePlus VOD (Video on Demand) storage, returning the Vid and playback references; supports both local file upload (ApplyUploadInfo + TOS + CommitUploadInfo) and URL pull upload (UploadMediaByUrl); also supports AI-based comprehensive quality restoration on already-uploaded videos (removing compression artifacts, noise, scratches, and improving clarity). Trigger keywords: upload video, upload media, upload to VOD, URL upload, pull upload, local upload, file upload, UploadMediaByUrl, ApplyUploadInfo, media ingestion, quality restoration, quality enhancement, comprehensive restoration, video denoising, denoise, compression artifact removal.
As a process B 70/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.
The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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
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high Secrets in code
meta-credential-filesscripts/.envCredential / dotenv files bundled with the skill (1)scripts/.env
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "env"
Process rating: all ten parameters 70/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 29 steps
- 100Failures and branches. 6 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2785 tokens
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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -32 of 5 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 674: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 29 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.