BC searxng-websearch
Use this skill whenever the user wants to search the web, do research on a topic, fetch a webpage, or gather information from online sources using a self-hosted SearXNG instance. Triggers include: 'search for', 'look up', 'find online', 'deep research', 'fetch this page', 'web search', or any request that needs current/external information. Also use when the user wants to run websearch.py, deep_research.py, or fetch_page.py scripts. Do NOT use for local file operations, code execution unrelated to search, or tasks that don't require web data.
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, 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 If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail.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-files.envCredential / dotenv files bundled with the skill (1).env
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 100Steps. 4 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1293 tokens
- 100Running it twice. No mutating operations
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
- +3Output format is not stated: the model decides each time
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 548: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 4 items
- +4Has examples (10 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.