Content
82%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-engineered operational skill body: copy-paste commands for three decisions, four clearly sequenced workflows with a dedicated pre-deployment audit checklist, and dense Article-cited detail that is genuinely non-general knowledge. Weaknesses are modest: disclaimers duplicated from the description, an inline keyword dump, no error-recovery feedback in workflows, and reference/script paths that cannot be verified against the bundle as submitted.
Suggestions
Trim the three "This skill is NOT..." paragraphs in the body to a single boundary line (the description already carries all three disclaimers), and remove or relocate the 40-term Keywords section so the body stays lean.
Add explicit feedback loops to the workflows: e.g., in Workflow 1, what to do when the classifier output is disputed or confidence is low (escalate per Output Standards "Your Decision"), and in Workflow 2, a re-validation step after updating Annex IV documentation.
Include (or verify) the referenced bundle files — references/*.md and scripts/*.py are cited throughout but absent from the submitted bundle, making the skill's navigation and Quick Start commands unresolvable as delivered.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with Act-specific operational data (tier table, Article 6(3) carve-outs, per-role obligation matrix, 2025→2027 phasing) that is not general knowledge, so most tokens earn their place. Minor trimmable padding remains: the three "This skill is NOT..." paragraphs duplicate the description's disclaimers, and the ~40-term Keywords section is inline keyword stuffing. Matches anchor 4 (efficient with minor instances that could be trimmed), not 3 since there is no concept-teaching filler. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready commands with embedded samples for every decision ("python scripts/ai_system_risk_classifier.py path/to/systems.json"), plus four numbered workflows and a concrete Output Standards template. Specific runnable guidance covering the common cases matches anchor 5; the only unshown detail is the systems.json input schema, which the embedded-sample invocations cover. | 5 / 5 |
Workflow Clarity | Workflows 1–4 are numbered with goals, durations, and conditional branching ("3. If high-risk: run planner"), and Workflow 3 is an explicit pre-deployment checkpoint list. However, no error-recovery feedback loops exist (e.g., what to do when a classification is disputed or a cross-check fails), so it matches anchor 4 (clear sequence, most checkpoints) rather than anchor 5's explicit feedback loops. Not a destructive/batch operation, so no cap applies. | 4 / 5 |
Progressive Disclosure | Structure is good: sections are well-organized, and references are one level deep and clearly signaled ("See references/eu_ai_act_titles.md for the full Article-by-Article walkthrough" plus a References section with per-file descriptions). However, the bundle provides no references/, scripts/, or assets/ directories, so every cited path is unverifiable, and the keyword dump plus full tier/role tables are inlined in the overview. Anchor 4 (good structure, references mostly clear, minor organization gaps) fits better than anchor 5, which would require a verified, clean one-level-deep bundle. | 4 / 5 |
Total | 17 / 20 Passed |