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CC performing-cloud-forensics-investigation

Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group configurations, and preserve cloud-native logs (CloudTrail, Activity Log, Audit Log). Use when investigating a suspected breach in AWS, Azure, or GCP, tracing unauthorized access through API logs, or analyzing a compromised VM, container, or serverless function.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 3 243 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Collect and analyze cloud forensic evidence using AWS CLI, Azure CLI, or gcloud to snapshot volumes, capture instance metadata and security group…

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

IntegrationAWSGoogle 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
87/100
safety, quality, tests
Safety 60%
93
Quality 40%
78
Run on models
none yet
Process rating
C
64/100
Has gaps
Failures and branches w 10
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • medium Dangerous commands cmd-persistence SKILL.md:220
      Persistence mechanism (cron / launchd / scheduled task / autorun registry) (documentation of a security skill)
      cp -r /mnt/evidence/etc/crontab /cases/case…001/cloud/persistence/
      security skill
    • low Risky intent intent-offensive-security SKILL.md:256
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      3. Lateral Movement:
    • low Risky intent intent-offensive-security SKILL.md:322
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Collect Kubernetes audit logs and cloud provider logs, analyze pod creation events for privilege escalation attempts, examine node-level logs for container escape evidence, check for unauthorized acce

    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 64/100

    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 11 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3243 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)
    • -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 408: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 11 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)
    • +1License stated

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