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BC Agent Compliance & Security Assessment

Comprehensive compliance and security self-assessment for AI agents. 14-check framework producing a structured threat model + compliance report with RED/AMBER/GREEN ratings across security, governance, EU AI Act readiness, oversight quality, and NIST alignment domains. Includes automation bias detection, audit trail reasoning checks, extraterritorial scope assessment, and Zero Trust posture evaluation. Designed for the August 2026 EU AI Act deadline.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 6 259 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Comprehensive compliance and security self-assessment for AI agents.

As a process C 56/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

AnalyzerSecurityAI and agentsPeople and hiringtype 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
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
58
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Consistency w 8
40
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6259 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 56/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (Agent Compliance & Security Assessment) differs from the folder (agent-self-assessment)
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 6259 tokens
  • 100Steps. 171 steps
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low 22 top-level sections: this looks like several domains in one skill

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)
  • -258 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 454: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 171 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: 58.