BC local-search
Local web search skill that runs entirely on the user's machine. Scrapes DuckDuckGo / Bing / Google HTML directly with automatic engine fallback (DDG -> Bing -> Google). Use whenever the user needs real-time web information, latest news, or content beyond the knowledge cutoff. Returns structured results (url / name / snippet / host_name / rank / date / favicon) plus source_engine, raw_html, score extensions. Supports --num, --recency-days, --locale (BCP-47), --json, --output. No API key, no SDK, no cloud hop.
Local web search skill that runs entirely on the user's machine.
As a process C 60/100 · Has gaps — weak spots: result and completion, 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 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
- 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 Exfiltration
intent-browser-credential-storesrc/engines/bing.ts:92Accesses a browser credential / cookie storeconst edgeCookies = `_EDGE_CD=m=${lang}-${countryLower}&u=${lang}-${countryLower}; _EDGE_S=mkt=${localeNorm}&ui=${localeNorm}`;
Files scanned: 20. 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 60/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4779 tokens
- 100Steps. 71 steps
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 18 top-level sections: this looks like several domains in one skill
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
- -31 of 2 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 514: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 71 items
- +4Has examples (17 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.