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CC performing-cloud-native-threat-hunting-with-aws-detective

Investigate AWS security incidents using Amazon Detective's behavior graphs, built from CloudTrail, VPC Flow Logs, GuardDuty, and EKS audit logs, to trace entity timelines and profile IAM users, roles, EC2 instances, and IP addresses for lateral movement. Use when triaging GuardDuty findings, investigating a suspected AWS compromise, or reconstructing an attacker's activity timeline across AWS accounts.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 6 files · 1 script body ≈ 1 280 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Investigate AWS security incidents using Amazon Detective's behavior graphs, built from CloudTrail, VPC Flow Logs, GuardDuty, and EKS audit logs, to trace…

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerAWSPeople and hiringSecuritytype 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
87/100
safety, quality, tests
Safety 60%
95
Quality 40%
75
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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 assets/template.md:14
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - [ ] Privilege escalation attempts documented
    • low Risky intent intent-offensive-security references/standards.md:6
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **TA0004** Privilege Escalation: T1078, T1484 (Domain Policy Modification)
    • low Risky intent intent-offensive-security references/workflows.md:12
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      4. Check for privilege escalation patterns (CreateAccessKey, AttachPolicy)
    • low Risky intent intent-offensive-security references/workflows.md:15
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      1. Trace lateral movement via AssumeRole chains
    • low Risky intent intent-offensive-security SKILL.md:40
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      AWS Detective automatically collects and analyzes log data from AWS CloudTrail, VPC Flow Logs, GuardDuty findings, and EKS audit logs to build interactive behavior graphs. These graphs enable security
      detector

    Files scanned: 6. 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 56/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1280 tokens
    • 100Running it twice. No mutating operations
    • 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)
    • +3Output format is not stated: the model decides each time
    • -43 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: 12 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (6 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.