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BC ai-act-readiness

EU AI Act readiness assessment and sprint playbook. Use when preparing for the high-risk AI system deadlines (Dec 2027 / Aug 2028), when a notified-body conformity assessment is scheduled, or when GPAI (general-purpose AI) obligations apply.

borghei/Claude-Skills Agent Skills author: borghei 5 files · 2 scripts body ≈ 2 219 tokens Open the sourcegithub.com↗ analyzed 8 h ago

EU AI Act readiness assessment and sprint playbook.

As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureOperations and projectsWriting and documentstype 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
B
94/100
safety, quality, tests
Safety 60%
95
Quality 40%
92
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 Secrets in code secret-high-entropy-token scripts/ai_act_readiness_score.py:146
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "Art1…ion": {
      quoted
    • low Secrets in code secret-high-entropy-token scripts/ai_act_readiness_score.py:156
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "Art1…ers": {
      quoted
    • low Secrets in code secret-high-entropy-token scripts/ai_act_readiness_score.py:168
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "Art1…ber": {
      quoted
    • low Secrets in code secret-high-entropy-token scripts/ai_act_readiness_score.py:180
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "Art7…ing": {
      quoted
    • low Secrets in code secret-high-entropy-token scripts/ai_act_readiness_score.py:186
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "Art7…ing": {
      quoted

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 41 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2219 tokens
    • low 11 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)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 241: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 41 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.