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CC implementing-aws-iam-permission-boundaries

Configures AWS IAM permission boundaries that cap the maximum permissions an identity-based policy can grant to a user or role, so effective permissions are the intersection of the identity policy and the boundary even if the policy grants AdministratorAccess. Use when letting security teams delegate IAM role and policy creation to developers for self-service while enforcing least-privilege ceilings and preventing privilege escalation.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 8 files · 2 scripts body ≈ 2 052 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Configures AWS IAM permission boundaries that cap the maximum permissions an identity-based policy can grant to a user or role, so effective permissions are…

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureAWSSecuritytype 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
57/100
Has gaps
Result and completion w 14
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/workflows.md:46
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## Privilege Escalation Prevention Workflow
    • low Risky intent intent-offensive-security SKILL.md:8
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ceilings and preventing privilege escalation.
    • low Risky intent intent-offensive-security SKILL.md:38
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      IAM permission boundaries are an advanced AWS feature that sets the maximum permissions an identity-based policy can grant to an IAM entity (user or role). They enable centralized security teams to sa
    • low Risky intent intent-offensive-security SKILL.md:229
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ### Step 4: Prevent Privilege Escalation
    • low Risky intent intent-offensive-security SKILL.md:260
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - [ ] Privilege escalation paths tested and blocked

    Files scanned: 8. 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 57/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 8 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 31 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2052 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)
    • +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
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 439: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (5 code blocks)
    • +1License stated

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