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CC performing-cloud-forensics-with-aws-cloudtrail

Investigate AWS account compromise by querying CloudTrail with boto3's LookupEvents or AWS Athena SQL over S3-delivered logs, filtering on suspicious user agents, source IPs, and event names to reconstruct an attacker timeline. Use when tracing unauthorized API calls, S3 data exfiltration, IAM privilege escalation, or credential exposure, and building a forensic report of findings and remediation steps.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 646 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Investigate AWS account compromise by querying CloudTrail with boto3's LookupEvents or AWS Athena SQL over S3-delivered logs, filtering on suspicious user…

As a process C 60/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

IntegrationAWSSecurityAI and agentstype 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%
95
Quality 40%
78
Run on models
none yet
Process rating
C
60/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Risky intent intent-offensive-security references/api-reference.md:98
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | AssumeRole | STS | Lateral movement / privilege escalation |
    • low Secrets in code secret-high-entropy-token scripts/agent.py:22
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "UpdateLoginProfile", "Crea…331", "Upda…1v2",
      quoted
    • low Secrets in code secret-high-entropy-token scripts/agent.py:29
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "Crea…331", "Crea…331",
      detector
    • low Risky intent intent-offensive-security SKILL.md:6
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      data exfiltration, IAM privilege escalation, or credential exposure, and building a forensic
    • low Risky intent intent-offensive-security SKILL.md:42
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - When analyzing S3 data exfiltration or IAM privilege escalation

    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 60/100

    • 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. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 646 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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 406: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 17 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.