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AC incident-response-network

Network forensics evidence collection and analysis during security incidents. Guides volatile evidence preservation, lateral movement detection via flow records and ARP/MAC/CAM table analysis, and read-only containment verification across Cisco IOS-XE/NX-OS, Juniper JunOS, and Arista EOS. Scoped to network artifacts only — packet captures, flow data (NetFlow/sFlow/IPFIX), forwarding tables, routing state, and device logs. Not general incident response, endpoint forensics, or malware analysis.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 4 806 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Network forensics evidence collection and analysis during security incidents.

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers

AnalyzerInfrastructuretype 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
90/100
safety, quality, tests
Safety 60%
91
Quality 40%
89
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Failures and branches w 10
50
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 references/forensics-workflow.md:35
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      protocol analysis, malware payload extraction, credential harvesting evidence
    • low Risky intent intent-offensive-security references/forensics-workflow.md:47
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - **Forensic value:** High — reveals communication patterns, lateral movement
      detector
    • low Risky intent intent-offensive-security references/forensics-workflow.md:53
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Best for:** Lateral movement mapping, communication pattern analysis,
    • low Risky intent intent-offensive-security references/forensics-workflow.md:202
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Phase (reconnaissance, initial access, lateral movement, objective,
    • low Risky intent intent-offensive-security SKILL.md:5
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Guides volatile evidence preservation, lateral movement detection via flow
    • low Risky intent intent-offensive-security SKILL.md:10
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Not general incident response, endpoint forensics, or malware analysis.
    • low Risky intent intent-offensive-security SKILL.md:25
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      800-61), endpoint forensics, malware analysis, or organizational
    • low Risky intent intent-offensive-security SKILL.md:29
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      evidence: preserve volatile data → triage scope → detect lateral movement →
      detector
    • low Risky intent intent-offensive-security SKILL.md:45
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Lateral movement investigation** — tracing attacker movement between

    Files scanned: 4. 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 57/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4806 tokens
    • 85Steps. 55 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (10 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

    • +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
    • +2Single-language instructions
    • +3Description length 497: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 55 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.