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BD auditclaw-grc

AI-native GRC (Governance, Risk, and Compliance) for OpenClaw. 97 actions across 13 frameworks including SOC 2, ISO 27001, HIPAA, GDPR, NIST CSF, PCI DSS, CIS Controls, CMMC, HITRUST, CCPA, FedRAMP, ISO 42001, and SOX ITGC. Manages controls, evidence, risks, policies, vendors, incidents, assets, training, vulnerabilities, access reviews, and questionnaires. Generates compliance scores, reports, dashboards, and trust center pages. Runs security header, SSL, and GDPR scans. Connects to AWS, Azure, GCP, GitHub, and identity providers via companion skills.

modbender/skill-library-mcp Agent Skills author: modbender MIT 40 files body ≈ 2 363 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AI-native GRC (Governance, Risk, and Compliance) for OpenClaw.

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubAWSAzureGoogle CloudInfrastructureSecurityData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
67
Run on models
none yet
Process rating
D
39/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

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 assets/policy_templates/access-control.md:175
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - Privilege escalation events
  • low Risky intent intent-offensive-security assets/policy_templates/incident-response.md:55
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | **1** | Critical | Active data breach, system compromise, ransomware | Immediate (< 1 hour) | Data exfiltration, ransomware deployment, active attacker in network |

Files scanned: 40. 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 "homepage"
  • note frontmatter-key unknown frontmatter key "source"

Process rating: all ten parameters 39/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 5 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 60Steps. 38 steps, 6 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2363 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 14 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (6 tags): a typed call is more reliable

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
  • -39 of 18 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 558: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.