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CC managing-intelligence-lifecycle

Manages the end-to-end cyber threat intelligence lifecycle from planning and direction through collection, processing, analysis, dissemination, and feedback to ensure intelligence products meet stakeholder requirements and continuously improve. Use when establishing or maturing a CTI program, defining intelligence requirements with business stakeholders, or building feedback loops between intelligence consumers and producers. Activates for requests involving CTI program maturity, intelligence requirements, PIRs, or intelligence lifecycle management.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 1 338 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Manages the end-to-end cyber threat intelligence lifecycle from planning and direction through collection, processing, analysis, dissemination, and feedback…

As a process C 62/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

ProcedurePersonal productivityPeople 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%
99
Quality 40%
75
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security SKILL.md:62
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
      Example PIR: "Is ransomware group Cl0p currently targeting organizations in our sector using MoveIT or GoAnywhere vulnerabilities?"
      quoted

    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 "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 35 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1338 tokens

    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
    • +4No input/output examples
    • -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
    • +4Description says when NOT to use the skill
    • +3Description length 555: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 35 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.