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BC container-forensics

Forensic investigation of Docker, containerd/CRI-O, and Kubernetes — inventory, escape detection, eBPF runtime monitoring, RBAC and etcd audit. Use when investigating container compromise.

jmagly/aiwg Agent Skills author: jmagly 1 file body ≈ 2 412 tokens Open the sourcegithub.com↗ analyzed 9 h ago

Forensic investigation of Docker, containerd/CRI-O, and Kubernetes — inventory, escape detection, eBPF runtime monitoring, RBAC and etcd audit.

As a process C 64/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerDockerKubernetesInfrastructuretype 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
B
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: container-forensics (jmagly/aiwg)

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:122
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      - Identify bulk `list`/`get` on `secrets` resources (credential harvesting pattern)
      detector

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "namespace"
    • note frontmatter-key unknown frontmatter key "tools"

    Process rating: all ten parameters 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 76 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2412 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (4 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 188: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 76 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.