Content
86%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 body: a deterministic five-step workflow with named scripts, flags, thresholds, and a copy-paste example, backed by a clean one-level-deep bundle whose referenced files all exist. The gaps are minor — no explicit error-recovery loop after input validation, and light over-explanation in the Purpose and forcing-question sections that could be trimmed for token efficiency.
Suggestions
Add one error-recovery line to the workflow, e.g. 'If process_documenter.py rejects the JSON, fix the stage entries and re-run before proceeding', to close the validation feedback loop.
Compress the forcing-question library citations (author + concept only) and drop the 'Distinct from' section, which duplicates boundary guidance already stated in the description.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and nearly every section earns its place — the workflow, scripts, quick example, references, and anti-patterns are all operationally relevant with no padding about concepts Claude already knows. Minor instances could be trimmed: the Purpose section narrates three failure modes, the forcing-question library carries full book/year citations ('Goldratt 1984', 'Reinertsen 2009'), and 'Distinct from' repeats boundary content already in the description. This fits 'Efficient; minor instances of over-explanation that could be trimmed' (4); it is not the 5 anchor because a few tokens (canon citations, restated distinctness) do not strictly earn their place. | 4 / 5 |
Actionability | Fully executable guidance throughout: a five-step workflow naming exact scripts and flags ('process_documenter.py --sample', 'bottleneck_detector.py --profile saas/services/manufacturing/healthcare'), a concrete JSON intake schema with field names ('name', 'owner', 'type', 'duration_minutes_p50', 'duration_minutes_p90'), explicit verdict thresholds (VA% > 25% = HEALTHY, 10–25% = TYPICAL, < 10% = WASTE-HEAVY), explicit detection rules (P50 > 2× mean, wait % > 40%, rework % > 15%), and a copy-paste bash example. This matches 'Fully executable; copy-paste ready code or commands; specific examples cover the common cases'. | 5 / 5 |
Workflow Clarity | The five-step sequence (Intake → Map → Measure → Detect → Recommend) is clearly ordered with tool invocation order restated ('invoke process_documenter.py → bottleneck_detector.py → cycle_time_analyzer.py in sequence') and sequencing discipline for the forcing questions ('Don't open question 4 before 1-3 are answered'). Validation is mentioned (process_documenter.py 'validates' the JSON) and the Assumptions section covers data quality, but there is no explicit error-recovery loop for what to do when validation fails — e.g., 'fix the JSON and re-run'. This fits 'Clear sequence with most checkpoints present; minor validation gaps' (4) rather than the 5 anchor, which requires explicit feedback loops for error recovery. The destructive/batch cap does not apply since the operations are read/analyze only. | 4 / 5 |
Progressive Disclosure | SKILL.md is a genuine overview: three reference files, three scripts, and one asset are each referenced one level deep with a one-line description of their contents ('references/lean_six_sigma_canon.md — TIMWOOD wastes, value-stream mapping, ...'; 'bottleneck_anti_patterns.md — Seven specific anti-patterns ...'), all verified to exist, and the reference files point back to scripts rather than to further nested documents. A quick-start example is inline, and bulk detail (canon, BPMN notation, anti-patterns) is correctly split out. This matches 'Clear overview with well-signaled one-level-deep references; content appropriately split; easy navigation'. | 5 / 5 |
Total | 18 / 20 Passed |