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 skill body: executable commands with flags at every step, verified one-level-deep references, and embedded discipline checkpoints (redirects, confidence labels, anecdote flags). The main costs are repetition of the tool descriptions across three sections and a forcing-question library that duplicates anti-pattern and reference content.
Suggestions
Describe each tool once — keep the Scripts table as the canonical summary and let the Workflow steps reference them by name with their command line only, removing the triple description in Purpose/Workflow/Scripts.
Move the forcing-question library's recommended answers and canon citations into a reference file (or inline only the five questions, linking each to its canon entry in references/), since much of it restates the anti-patterns and reference content.
Add an explicit verification step at the end of the Workflow (e.g., re-run insight_synthesizer.py after recoding to confirm clusters clear the threshold, or audit that filed insights carry evidence and confidence tags), giving the sequence a closed feedback loop.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Dense, non-pedantic, and assumes Claude's competence (no explaining what a usability test or thematic analysis is). Not a 5 because of trimmable redundancy: the three tools are described three times (Purpose bullets, Workflow steps, Scripts table), the 'Distinct from' table repeats the 'Do NOT use this skill to' list, and the forcing-question library largely restates anti-patterns and reference-file content. Above level 3, which would imply genuinely unnecessary explanation rather than structural repetition. | 4 / 5 |
Actionability | Fully executable throughout: complete CLI invocations with flags ('study_designer.py --goal {discovery|evaluative|validation} --stage {...} --profile {...}', 'insight_synthesizer.py --input observations.json --min-sources 3'), onboarding and autoresearch commands, and a Quick examples section that states the expected behavior ('promotes "import-confusion" ... flags "wants-slack" as an ANECDOTE'). All referenced scripts exist with --help/--sample. Copy-paste ready, covering the common cases. | 5 / 5 |
Workflow Clarity | A clear five-step sequence with explicit commands and real checkpoints ('Honor the redirect if it routes to experiment-designer', 'Record the confidence label and limits', 'Treat ANECDOTE-flagged clusters as signals to probe, not findings to ship'). Not a 5: there is no explicit verify/recovery loop after synthesis or repository filing (e.g., re-run the synthesizer after recoding, or audit that insights were tagged to the atomic schema), so it sits at 'clear sequence with most checkpoints; minor validation gaps'. | 4 / 5 |
Progressive Disclosure | SKILL.md functions as an overview: three clearly-scoped reference files (methods canon, sampling/saturation, repository/synthesis) verified to exist, each one level deep with no nested references and each paired with its script; details are appropriately split out (saturation math and citations live in references, not the body). The Scripts table and one-line reference annotations make navigation easy. | 5 / 5 |
Total | 18 / 20 Passed |