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CC performing-ai-driven-osint-correlation

Use AI/LLM-based reasoning with Sherlock, theHarvester, and SpiderFoot to correlate OSINT findings—usernames, emails, social profiles, domain records, breach databases, and dark-web mentions—into unified, confidence-scored intelligence profiles with link analysis. Use when raw OSINT data from multiple sources needs merging into one target profile or resolving identity linkage across platforms.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 3 371 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Use AI/LLM-based reasoning with Sherlock, theHarvester, and SpiderFoot to correlate OSINT findings—usernames, emails, social profiles, domain records, breach…

As a process C 57/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

AnalyzerGitHubAI and agentsPeople and hiringtype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
97
Quality 40%
76
Run on models
none yet
Process rating
C
57/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Exfiltration net-credential-use references/api-reference.md:156
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "hibp-api-key: ${HIBP_KEY}" -H "User-Agent: OSINT-Agent" \
      security skill
    • low Exfiltration net-credential-use SKILL.md:119
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "hibp-api-key: ${HIBP_KEY}" \
      security skill
    • low Risky intent intent-offensive-security SKILL.md:355
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - **Threat Actor Attribution:** Correlate a suspicious username found in a phishing campaign with social media profiles, domain registrations, and breach data to build an attribution profile.
      detector

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "atlas_techniques"
    • note frontmatter-key unknown frontmatter key "nist_ai_rmf"
    • note frontmatter-key unknown frontmatter key "d3fend_techniques"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 57/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 35 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3371 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • -2localhost URLs: will not work for another user
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 396: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 35 items
    • +3Output format is stated explicitly
    • +4Has examples (12 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.