BB eu-ai-act-high-risk-implementation-readiness
Assess and operationalize implementation readiness for high-risk AI systems under the EU AI Act Annex III, including provider and deployer obligations, conformity assessment, post-market monitoring, and EU database registration. Use when users say things like “we classified this as high-risk, what now?”, “build an EU AI Act readiness plan”, “assess our Annex III compliance gaps”, “what do providers/deployers of high-risk AI need to implement?”, “prepare for conformity assessment”, or “create a high-risk AI implementation roadmap.”
Assess and operationalize implementation readiness for high-risk AI systems under the EU AI Act Annex III, including provider and deployer obligations…
As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, consistency, running it twice
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5884 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 518): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 68/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 32 mutating operations with no state check
- 40Consistency. Frontmatter name (eu-ai-act-high-risk-implementation-readiness) differs from the folder (eu-ai-act-high-risk-implementation-readiness-werner-plutat)
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 5884 tokens
- 85Steps. 292 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Failures and branches. 6 branches, has a failure section
- 100Progress reporting. Reports progress
- low 12 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
- +4Description does not say when NOT to use the skill (false activations)
- +4No input/output examples
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 536: enough signal without eating the budget
- +4Structure: 53 headings
- +3Step-by-step instructions: 292 items
- +3Output format is stated explicitly
- +4Reference files are cited in the instructions (8 of 9)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.