Content
75%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.
The body is a highly actionable operator runbook — concrete CLI commands, explicit input schemas, real bundle scripts and references, and a well-sequenced 5-step workflow with documented degradation paths. It loses points on conciseness (concept re-explanations and an inlined question library that belongs in references) and lacks explicit review checkpoints between workflow steps.
Suggestions
Move the forcing-question library (with its per-question canon citations) to a file like references/forcing_questions.md and keep only the question list plus 'Lock 1-4 before 5-7' guidance in SKILL.md, cutting roughly 25 lines of inlined citation detail.
Delete explanations of known concepts — e.g., the Goldratt Theory of Constraints sentence in Step 3 can shrink to 'flags categories with cycle time > 2x the cross-category median as bottlenecks (Theory of Constraints)' — and trim the rationale tails on anti-pattern entries to one clause each.
Add an explicit checkpoint between steps, e.g., after Step 2: 'Review categorized.md for mis-categorized line items (supplier-name-driven categorization is the #1 error) before running the consolidation planner.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly operational and skill-specific, but several passages explain concepts Claude already knows or add padded rationale: 'This is Goldratt's Theory of Constraints applied to procurement: the system throughput is set by the slowest step', the explanatory tails on each anti-pattern ('Cost savings buy nothing if the consolidated supplier disappears'), and a ~30-line inlined forcing-question library with per-question canon citations that belongs in a reference file. These could be tightened or pushed out of the main file. | 3 / 5 |
Actionability | Guidance is fully executable: exact commands with flags ('scripts/spend_categorizer.py --input spend.json --profile <profile> --output categorized.md'), precise input record schemas ({supplier, description, category_hint, annual_spend, frequency, currency}), enumerated profiles, --sample and --help flags, a copy-paste quick example, and a 20-minute intake template asset. All three referenced scripts and the asset are real bundle files. | 5 / 5 |
Workflow Clarity | The 5-step workflow is clearly sequenced, each step naming its script, input format, and output artifact, and the Assumptions section documents graceful degradation when optional inputs are absent. It is analytical rather than destructive, so the validation cap does not apply, but there are no explicit checkpoints (e.g., review categorized.md for mis-categorizations before planning consolidation) — the only gating is 'Lock 1-4 before opening 5-7' in the question library. | 4 / 5 |
Progressive Disclosure | Good structure overall: an overview body with a Scripts table, a clearly signaled one-level-deep References section (all three reference files exist), and an intake template asset. The main gap is that the 7-question forcing library with canon citations is inlined in SKILL.md rather than split into a reference like the canon material, leaving minor organization gaps. | 4 / 5 |
Total | 16 / 20 Passed |