BF research-assistant
Auto-enrich Bear research notes tagged 「待整理」 with topic-matched GIFs. Reads notes via grizzly, searches GIFs via gifgrep, appends media, and removes the tag. Use when the user wants to batch-process or tidy up research notes in Bear.
As a process F 54/100 · Will not run — References files that are not bundled: %s
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 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 If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail.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
- 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 Secrets in code
secret-google-keyscripts/process_tagged.sh:90Google API key (detector / deny-list definition)GIF_URL=$(curl -s "https://tenor.googleapis.com/v2/search?q=… -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$QUERY")&key=AIzaSyAyimkuYQYF_FXVALexPuGQctUWRURdCYQ&limit=1
detector
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: %s
Process rating: all ten parameters 54/100
- 0Tools and files. 1 referenced file(s) missing: %s
- 0Result and completion. Does not say what the result is
- 30Running it twice. 9 mutating operations with no state check
- 40Consistency. Frontmatter name (research-assistant) differs from the folder (research-gif-enricher)
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 8 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 550 tokens
- 100Progress reporting. Reports progress
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 233: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (5 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.