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AC osint-investigator

Deep OSINT (Open Source Intelligence) investigations. Use when the user wants to research, find, or investigate any person, place, organisation, username, domain, IP address, phone number, image, vehicle, or object using publicly available information. Triggers on phrases like "find information on", "investigate", "look up", "who is", "trace this", "dig into", "OSINT search", "background check", or any request to gather open-source intelligence about a target. Performs deep multi-source analysis across web search, social media, DNS/WHOIS, image search, maps, public records, and more — returning a structured intelligence report.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files · 1 script body ≈ 3 843 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Deep OSINT (Open Source Intelligence) investigations.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

ProcedurePDFGitHubData and analyticsMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
95
Quality 40%
94
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Exfiltration net-credential-use SKILL.md:319
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s "https://api.shodan.io/shodan/host/<ip>?key=$SHODAN_KEY"
      security skill
    • low Exfiltration net-credential-use SKILL.md:320
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s "https://api.shodan.io/dns/resolve?hostnames=<domain>&key=$SHODAN_KEY"
      security skill
    • low Exfiltration net-credential-use SKILL.md:325
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s "https://api.hunter.io/v2/domain-search?domain=<domain>&api_key=$HUNTER_KEY"
      security skill
    • low Exfiltration net-credential-use SKILL.md:326
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s "https://api.hunter.io/v2/email-verifier?email=<email>&api_key=$HUNTER_KEY"
      security skill
    • low Exfiltration net-credential-use SKILL.md:337
      Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service; documentation of a security skill)
      curl -s "https://maps.googleapis.com/maps/api/geocode/json?address=<address>&key=$GMAPS_KEY"
      known servicesecurity skill

    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 55/100

    • 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
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 76 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3843 tokens
    • 100Running it twice. Mutating operations check current state
    • low The response is described with custom markup (24 tags): a typed call is more reliable

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -216 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +3Description length 635: enough signal without eating the budget
    • +4Structure: 33 headings
    • +3Step-by-step instructions: 76 items
    • +4Has examples (20 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.