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CC implementing-siem-correlation-rules-for-apt

Write multi-event correlation rules in Splunk SPL and Sigma format that detect APT lateral movement by chaining Windows authentication events (4624, 4648), process execution (4688, Sysmon Event 1), and network connections (Sysmon Event 3) across hosts within sliding time windows. Use when building SIEM correlation searches to surface multi-stage attack sequences that single-event detections miss, such as pass-the-hash or lateral movement chains.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 570 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Write multi-event correlation rules in Splunk SPL and Sigma format that detect APT lateral movement by chaining Windows authentication events (4624, 4648)…

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

GeneratorSecuritytype 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
88/100
safety, quality, tests
Safety 60%
94
Quality 40%
79
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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • low Risky intent intent-offensive-security references/api-reference.md:51
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## Key Windows Event IDs for Lateral Movement
    • low Risky intent intent-offensive-security SKILL.md:4
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      detect APT lateral movement by chaining Windows authentication events (4624, 4648),
      detector
    • low Risky intent intent-offensive-security SKILL.md:8
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      as pass-the-hash or lateral movement chains.
    • low Risky intent intent-offensive-security SKILL.md:56
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      3. Build Sigma rules in YAML that express multi-step detection logic for lateral movement patterns:
      detector
    • low Risky intent intent-offensive-security SKILL.md:70
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      ### Detect RDP Lateral Movement Chain
      detector
    • low Risky intent intent-offensive-security SKILL.md:78
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      ### Sigma Rule for PsExec Lateral Movement
      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 "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. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 570 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
    • -2localhost URLs: will not work for another user
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 449: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (3 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.