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CC cloud-forensics

AWS, Azure, and GCP forensic investigation covering audit logs, IAM review, storage access, network flows, and compute instance forensics

jmagly/aiwg Agent Skills author: jmagly 1 file body ≈ 1 676 tokens Open the sourcegithub.com↗ analyzed 9 h ago

AWS, Azure, and GCP forensic investigation covering audit logs, IAM review, storage access, network flows, and compute instance forensics

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

AnalyzerAWSGoogle CloudAzureInfrastructuretype 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%
98
Quality 40%
73
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: cloud-forensics (jmagly/aiwg)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Risky intent intent-offensive-security SKILL.md:12
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Investigates cloud environments for signs of compromise, data exfiltration, privilege escalation, and persistence. Parameterized by cloud provider. Adapts collection procedures to AWS CloudTrail, Azur
  • low Risky intent intent-offensive-security SKILL.md:140
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - @$AIWG_ROOT/agentic/code/frameworks/forensics-complete/rules/red-flag-escalation.md — Escalate when CloudTrail tampering (StopLogging, DeleteTrail) or active IAM privilege escalation is found

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

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • 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. 59 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1676 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

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 137: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 59 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.