Content
82%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 skill body: every step names a concrete command, scripts are tabulated with profiles, and methodology discipline (triangulate, power per segment, gate segments) is enforced at each step. The main costs are mild redundancy between the forcing-question library, the 'Distinct from' table, and the references, plus a thin error-recovery path for failed triangulation.
Suggestions
Move the forcing-question library's per-question 'Recommended' and 'Canon' lines into a new reference file (e.g. references/grill_questions.md), keeping only the five question stems inline to cut duplication with the References section.
Add one concrete recovery step to the Workflow for failed triangulation (e.g. 'if tam_divergence exceeds X, revisit the serviceable-fraction assumptions in market.json before quoting') so the main path has a fix-and-revalidate loop, not just the opt-in autoresearch bridge.
Trim the 'Distinct from' table's overlap with the frontmatter description and the 'Do NOT use' bullets — the campaign-analytics and demand-acquisition rows restate the same boundary twice.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and operational, assuming domain knowledge (Kotler criteria, FPC, AAPOR) rather than explaining basics, but the 5-question forcing-question library with per-question Recommended/Canon lines and the 'Distinct from' table partially duplicate the references section and the frontmatter description. Efficient with minor instances that could be trimmed, matching the 4 anchor rather than the 5 ('every token earns its place') or the 3 (noticeable padding). | 4 / 5 |
Actionability | Fully executable, copy-paste-ready commands with real flags throughout: 'market_sizer.py --input market.json --method both --profile {b2b-saas|consumer|enterprise|marketplace|hardware|services}', 'sample_size_planner.py --population 62000 --confidence 0.95 --moe 0.05', onboarding and autoresearch invocations, plus a scripts table documenting '--help', '--sample', '--output {human,json}'. Specific examples cover the common cases, matching the 5 anchor. | 5 / 5 |
Workflow Clarity | A clear 5-step workflow (brief → size → plan → score → assemble) with explicit validation checkpoints ('Reconcile the top-down/bottoms-up delta before quoting anything', 'Drop segments failing the substantiality/accessibility gate', 'Lock 1-2 before opening 3-5'). It falls short of the 5 anchor because recovery from a failed triangulation is only implicitly handled — the body says to reconcile the delta and offers the opt-in autoresearch loop, but gives no direct fix-and-revalidate loop in the main workflow. | 4 / 5 |
Progressive Disclosure | The References section clearly signals three one-level-deep files with one-line scope descriptions, all verified to exist, and the scripts table maps each tool to its purpose. It does not reach 5 because the 25-line forcing-question library with canon citations is inline content that belongs in a reference file, keeping the overview slightly heavier than a clean split. | 4 / 5 |
Total | 17 / 20 Passed |