AC infoseek-en
Deep web information search and archival skill for comprehensive research on persons, organizations, or products. Uses multiple search engines (Baidu, Tavily, Multi-Search-Engine) plus browser-based content extraction. Features URL-normalized deduplication with SQLite database, structured file storage with complete metadata, ad/content filtering, and customizable output formats (md/json/txt/csv/xlsx/html/docx). Automatically creates organized folders per subject. Enforces strict deletion policy (recycle bin only, user confirmation required). Use when: user needs comprehensive information gathering, background research, due diligence, media monitoring, competitive intelligence, or building information archives about specific subjects. Triggers: infoseek, deep search, information gathering, background check, media monitoring, archive search, research person, research company, competitive analysis, dossier building.
Deep web information search and archival skill for comprehensive research on persons, organizations, or products.
As a process C 62/100 · Has gaps — weak spots: when it triggers, consistency, running it twice
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 214): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 62/100
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 17 mutating operations with no state check
- 40Consistency. Frontmatter name (infoseek-en) differs from the folder (infoseek)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 44 steps
- 100Execution cost. Instruction body is 1679 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 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)
- +3Description length 926: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +4Structure: 21 headings
- +3Step-by-step instructions: 44 items
- +3Output format is stated explicitly
- +4Has examples (10 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.