Content
67%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-structured, concise overview with a clearly sequenced workflow, real decision checkpoints, and a functioning one-level-deep reference bundle. The main gap is actionability: the central tool's input format (CSV schema, inline format, scoring method) is undocumented in SKILL.md, forcing the reader into the script source to use it correctly.
Suggestions
Document the assumptions.csv schema next to the command — required columns (assumption, category, risk, certainty), that risk/certainty are 0-1 floats, and an example row — so the script is usable without reading its source.
Add one example inline invocation, e.g. python3 scripts/assumption_mapper.py --assumption "Users will pay for this | viability | 0.8 | 0.3", to demonstrate the non-CSV path and the category vocabulary the tool understands.
State the prioritization rule the script implements (priority = risk x (1 - certainty)) or point to it explicitly, and drop the generic OST structure section from the body in favor of the already-existing version in references/discovery-frameworks.md to remove duplication.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and directive throughout ("Prototype before build", "Measure behavior, not only stated preference", "Reject weak opportunities early"), with only minor over-explanation: the OST section restates the generic Outcome/Opportunities/Solutions/Experiments structure that is duplicated in references/discovery-frameworks.md. This fits anchor 4 (efficient, minor instances that could be trimmed) rather than anchor 5's every-token-earns-its-place. | 4 / 5 |
Actionability | Concrete guidance exists — the executable command "python3 scripts/assumption_mapper.py assumptions.csv", quantified checks ("At least 3 distinct opportunities", "At least 2 experiments per top opportunity"), and a day-by-day sprint plan — but key details are missing: the assumptions.csv schema (columns assumption, category, risk, certainty with [0,1] values) is documented nowhere in SKILL.md and is only discoverable by reading the script, no example invocation of the inline --assumption format is given, and "Score assumptions by risk and certainty" says nothing about how. This matches anchor 3 (some concrete guidance but incomplete, missing key details) rather than anchor 4, where gaps would be minor; not anchor 2 because there is genuinely usable concrete guidance. | 3 / 5 |
Workflow Clarity | The six-step Core Discovery Workflow is clearly sequenced with real checkpoints: "Daily evidence reviews", "End with decision: proceed, pivot, or stop", "Reject weak opportunities early", plus explicit evidence thresholds. This is not a destructive/batch operation so no cap applies; it fits anchor 4 (clear sequence, most checkpoints, minor validation gaps) rather than anchor 5 because steps like "Conduct interviews and behavior analysis" lack explicit stopping criteria and error-recovery loops for contradictory evidence are thin. | 4 / 5 |
Progressive Disclosure | Verified against the actual bundle: both scripts/assumption_mapper.py and references/discovery-frameworks.md exist and are referenced from the body ("See references/discovery-frameworks.md for framework details"), one level deep and clearly signaled, with the script documented in a dedicated Tooling section. Fits anchor 4 (good structure, references mostly clear, minor organization gaps) rather than anchor 5 because the generic OST structure is duplicated between the body and the reference file, and the detail sections (validation techniques, 10-day plan) could largely live in the reference. | 4 / 5 |
Total | 15 / 20 Passed |