Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is a strong, highly actionable workflow document with excellent sequencing and validation checkpoints and clean progressive disclosure to real reference files. Its main weakness is conciseness: conversational filler and repeated restatements of the core loop add tokens without adding guidance.
Suggestions
Remove conversational filler ('Cool? Cool.', the 'plumbers... grandparents' anecdote, 'we are trying to create billions a year in economic value here!') and the trailing re-emphasis of the core loop to reduce token load.
Consolidate the repeated statements of the create→test→review→improve loop (it appears in the intro, the 'How to think about improvements' section, and the closing recap) into a single canonical statement.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient and largely avoids explaining concepts Claude already knows, but it carries a fair amount of chatty padding ('Cool? Cool.', 'If you haven't heard (and how could you, it's only very recently that it started)...', 'we are trying to create billions a year in economic value here!') and redundant restatements of the core loop that could be tightened. | 2 / 3 |
Actionability | It provides concrete, executable guidance throughout — exact commands (python -m scripts.aggregate_benchmark, generate_review.py with full flags), copy-paste-ready subagent task templates, and specific JSON field names ('text', 'passed', 'evidence') — matching the fully executable anchor. | 3 / 3 |
Workflow Clarity | The eval/iterate loop is laid out as an explicitly sequenced five-step process with validation checkpoints (capture timing as notifications arrive, grade against assertions, run the analyst pass), feedback loops (fix and re-validate), and clear termination criteria, matching the level-3 anchor. | 3 / 3 |
Progressive Disclosure | SKILL.md is an overview that clearly signals one-level-deep references to real bundle files (agents/grader.md, agents/comparator.md, agents/analyzer.md, references/schemas.md) with guidance on when to read them, and the referenced files exist in the bundle (references/schemas.md is present), matching the well-signaled reference anchor. | 3 / 3 |
Total | 11 / 12 Passed |