Content
80%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable skill body with executable commands and lean prose. It falls short on explicit validation checkpoints in the workflow and on inline signposting of its reference files.
Suggestions
Add explicit validation checkpoints to the workflow, e.g. after stagnation detection ('verify triggers in pivot_diagnosis_*.json before generating pivots') and before export ('validate exportable YAML against references/pivot_proposal_schema.md').
Signpost references inline at the relevant workflow step, e.g. in step 3 add 'For techniques, see references/pivot_techniques.md; for archetypes, see references/strategy_archetypes.md' rather than only listing them at the end.
Add a brief error-recovery feedback loop, e.g. 'If no stagnation is detected, stop and report; if generated pivots fail schema validation, fix and re-run generate_pivots.py.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and efficient: a short overview, a bulleted When-to-Use/Prerequisites/Output set, and copy-paste commands, with no padding or explanation of concepts Claude already knows. | 3 / 3 |
Actionability | Quick Commands provides three fully executable bash invocations with real script paths, flags, and arguments that are copy-paste ready. | 3 / 3 |
Workflow Clarity | The seven steps are sequenced and include one conditional ('If stagnation detected'), but there are no explicit validation checkpoints or error-recovery feedback loops (e.g., verify the diagnosis or validate generated YAML against the schema before exporting). | 2 / 3 |
Progressive Disclosure | Content is appropriately split into real one-level-deep references and a Resources list, but references are not contextually signposted within the workflow, so a reader does not know which reference to consult at which step. | 2 / 3 |
Total | 10 / 12 Passed |