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.
A strong, highly actionable body: copy-paste-ready commands verified against real scripts, a clear five-step workflow, and well-organized one-level-deep references. Its weakness is redundancy — the exclusion list and the core assumption-block rule are each stated multiple times, and the grilling-question library is inline content that belongs in a reference file.
Suggestions
State the 'assumption block is non-optional' hard rule once (Assumptions section) and reference it elsewhere instead of repeating the 'theatre' formulation four times across Step 2, Step 5, Assumptions, Anti-patterns, and the grilling library.
Merge the 'Do not use for' list and the 'Distinct from' section into one boundary section — the second currently restates the first with only marginal added rationale.
Move the 7-question forcing-question library into a references file (e.g., references/forcing_questions.md) and keep a one-line pointer plus the sequencing rule in SKILL.md, matching the pattern used for the canon files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and free of explanations of concepts Claude already knows, but with clear tightening opportunities: the skill-exclusion list appears twice ('Do not use for:' in When to use, then restated across 'Distinct from'), and the 'assumption block is theatre' rule is repeated at least four times ('The forecast becomes theatre', 'the slide is theatre', 'A forecast without a disclosed assumption block is theatre', 'No assumption block = the slide is theatre'). Not anchor 2 — the body is not padded with generic explanation and each section carries domain judgment — but more than the 'minor trim' of anchor 4. | 3 / 5 |
Actionability | Fully executable, copy-paste-ready commands for all three workflows ('scripts/bookings_forecaster.py --input intake.json --profile saas --output markdown', etc.), verified against the actual scripts — '--help' and '--sample' work on all three as claimed, and the intake template bridges to 'a single JSON file that feeds all three scripts' with concrete field examples. The commands cover the common cases end to end. | 5 / 5 |
Workflow Clarity | Five clearly sequenced steps (intake → bookings forecast → cohort projection → funnel confidence → deck assembly), each naming its script and expected output, plus an explicit ordering rule in the grilling library ('After all 7 are answered, invoke bookings_forecaster.py → cohort_arr_projector.py → funnel_confidence_scorer.py in sequence'). Falls short of anchor 5 because there are no explicit validation checkpoints on the outputs (e.g., verifying intake.json parses before running, or confirming the pipeline-coverage ratio before assembling the deck) — the safety signals are surfaced by the scripts but not gated by the workflow. The destructive/batch cap does not apply since these are read-only analyses. | 4 / 5 |
Progressive Disclosure | Good structure: a References section lists three one-level-deep files (verified: they exist and contain no nested references), scripts are inventoried, and 'See forecast_anti_patterns.md' links one inline. Short of anchor 5 because the ~30-line 'Forcing-question library (Matt Pocock grill discipline)' is inlined in SKILL.md where the skill's own pattern (canon material → references/) suggests it belongs in a reference file, and the Assumptions/Anti-patterns detail could be similarly split. | 4 / 5 |
Total | 16 / 20 Passed |