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CC analyzing-threat-actor-ttps-with-mitre-attack

Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework, build technique coverage heatmaps with the ATT&CK Navigator, identify detection gaps, and produce actionable threat intelligence reports across the Enterprise, Mobile, and ICS matrices. Use when analyzing threat actor TTPs, correlating IOCs to specific ATT&CK techniques, or assessing defensive detection coverage against adversary behavior.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 8 files · 2 scripts body ≈ 2 270 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Systematically map threat actor behavior and observed IOCs to the MITRE ATT&CK framework, build technique coverage heatmaps with the ATT&CK Navigator…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerGitHubPeople and hiringPersonal productivitytype 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
85/100
safety, quality, tests
Safety 60%
91
Quality 40%
75
Run on models
none yet
Process rating
C
51/100
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 · 9

    ✓ No critical or high findings

    Medium and low: 9
    • low Risky intent intent-offensive-security assets/template.md:35
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Privilege Escalation | | |
    • low Risky intent intent-offensive-security assets/template.md:39
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Lateral Movement | | |
    • low Risky intent intent-offensive-security assets/template.md:41
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Command and Control | | |
    • low Risky intent intent-offensive-security references/api-reference.md:69
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Privilege Escalation | TA0004 |
    • low Risky intent intent-offensive-security references/api-reference.md:73
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Lateral Movement | TA0008 |
    • low Risky intent intent-offensive-security references/api-reference.md:75
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Command and Control | TA0011 |
    • low Risky intent intent-offensive-security references/standards.md:16
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      6. **Privilege Escalation** (TA0004): Gaining higher-level permissions
    • low Risky intent intent-offensive-security references/standards.md:20
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      10. **Lateral Movement** (TA0008): Moving through the environment
    • low Risky intent intent-offensive-security references/standards.md:22
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      12. **Command and Control** (TA0011): Communicating with compromised systems

    Files scanned: 8. 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 "d3fend_techniques"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 51/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 20 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2270 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 431: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (5 code blocks)
    • +1License stated

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