Content
80%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 concise, well-structured overview with verified executable commands and excellent token efficiency. The main gap is workflow clarity: the multi-step, cost-incurring batch pipeline lacks explicit validation checkpoints (e.g., a recommended dry-run-first step and spec review before full execution).
Suggestions
Add an explicit validation checkpoint to the workflow, e.g., "Run with --dry-run first and review the generated spec/scenarios before spending tokens on full execution; adjust and re-run if the spec misreads the rule."
State where reports are written (output path/format) and how to open them, so users can act on the results immediately.
Briefly note the exit behavior or how compliance scores should be interpreted (e.g., what counts as a failing scenario), turning report contents into actionable output.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The ~52-line body is lean throughout: a terse 6-item pipeline list, short bullets, three commands, and a one-sentence concept section ("Measures whether a skill/rule is followed even when the prompt doesn't explicitly support it"). It assumes Claude's competence (e.g., references "claude -p" and "stream-json" without explanation) and contains no padded explanations, matching anchor 5; at most one editorial sentence in "Advanced (optional)" could be trimmed, which does not drag it to anchor 4. | 5 / 5 |
Actionability | The Usage section provides three copy-paste-ready commands (full run, "--dry-run (no cost, spec + scenarios only)", and "--gen-model haiku --model sonnet"), and the referenced flags and `scripts.run` module were verified to exist in the bundle. It stops short of anchor 5 because minor details are missing: where reports are written, how to locate/open them, and no reference for the full option set. | 4 / 5 |
Workflow Clarity | The numbered 1-6 pipeline (spec generation → scenario generation → running → classification → temporal ordering → reports) gives a clear sequence, but validation checkpoints are only implicit — the dry-run option exists yet is never framed as a verify-spec-first step, and there is no feedback loop (check generated spec → adjust → re-run). Since running agents across multiple scenarios is a batch operation with real cost, the rubric's batch-operation cap holds workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | The body is a well-organized overview with clear sections (Supported Targets, When to Activate, Usage, Key Concept, Report Contents, Advanced) and nothing inlined that belongs in a separate file. Its only external content — the 11-file scripts/ bundle — is correctly surfaced through a single, clearly signaled entry point (`uv run python -m scripts.run`, verified present in the bundle), so navigation is trivial and structure matches anchor 5. | 5 / 5 |
Total | 17 / 20 Passed |