Content
52%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 highly actionable — real commands, input schemas, and well-sequenced workflows with expected outputs — but it is a padded, monolithic catalog. It duplicates its own frontmatter, re-documents eight other skill packages inline, and buries the orchestration guidance in KPI dashboards and decorative scripts instead of acting as a lean overview that points to the referenced skill packages.
Suggestions
Cut the Purpose section, the Success Metrics KPI dashboard, Time Estimate fields, and the decorative emoji bash examples — they restate the description or provide business metrics an agent cannot act on.
Replace the inline Python Tools and Knowledge Bases catalogs (13 entries) with one-line pointers to each skill package's own SKILL.md, keeping only the 1-2 primary tools (rice_prioritizer.py, customer_interview_analyzer.py) with usage examples.
Add error-recovery checkpoints to the workflows: verify the analyzer produced valid JSON before aggregating across interviews, and specify what to do when a RICE input CSV is malformed or a script exits with an error.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The ~675-line body is heavily padded: the Purpose section restates the frontmatter description nearly verbatim, the "All Orchestrated Skills" table duplicates the "Python Tools" entries, 13 external knowledge-base files are cataloged inline, and aspirational KPIs (">90% of PRDs require no clarification"), "Time Estimate" fields, and decorative emoji echo-scripts add hundreds of tokens an agent cannot act on. Not 1 because it never tutors Claude on basic concepts; not 3 because the duplication and catalog volume go well beyond "some unnecessary explanation". | 2 / 5 |
Actionability | Concrete, copy-paste-ready commands appear throughout (rice_prioritizer.py with --capacity flags, CSV input schemas with example rows, cat commands for reference files, full workflow bash scripts). Not 5 because input formats for several tools (epic.yaml, research-data.json, config.yaml, theme.json) are unspecified and no expected script output is ever shown. | 4 / 5 |
Workflow Clarity | All 7 workflows use numbered steps with embedded commands and "Expected Output" statements, and several include verification checkpoints ("Validate Solutions — test hypotheses before building", "Verify each story meets INVEST criteria"). Not 5 because no error-recovery feedback loops exist for failure cases such as malformed CSV input or an analyzer returning no pain points. | 4 / 5 |
Progressive Disclosure | No bundle files exist, and the SKILL.md body is a monolithic catalog that inlines over 400 lines of material — a 9-row skill table, 9 tool documentation entries, and 13 knowledge-base descriptions — that clearly belongs in separate reference files. The clearly-signaled external paths keep it above 1, but the inline volume far exceeds the 3 anchor's "could be in a separate file" threshold, matching the 2 anchor's defining trait. | 2 / 5 |
Total | 12 / 20 Passed |