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rp-why

Load when reflecting on AI collaboration quality or tracking growth over time. Analyzes session history to surface how deeply you think with AI tools and whether your orchestration sophistication matches your cognitive depth. Provides actionable nudges to push toward more strategic work.

63

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

50%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured and conceptually clear, but it is verbose and duplicates content that already lives in unreferenced bundle files, undermining token efficiency and progressive disclosure. The analytical methodology is under-specified relative to the scripts that implement it.

Suggestions

Replace the inline 'Growth Nudges by Zone', Orchestra Model tiers, and DOK level tables with one-level-deep links to the existing references/ files (e.g., nudges-by-zone.md, orchestra-model.md, dok-levels.md) to remove duplication and satisfy progressive disclosure.

Reference the scripts (rp_why_core.py, rp_why_baseline.py, growth_nudge.py) and summarize the DOK/compression classification method concretely so the report templates become reproducible rather than schematic.

Collapse the four near-identical ASCII report-format blocks into a shared template plus per-command deltas, and add a verification checkpoint (e.g., confirm sessions.db exists and classification counts sum to the prompt total before emitting a report).

DimensionReasoningScore

Conciseness

The ~420-line body repeats full ASCII report-format templates with bracket placeholders for every command and restates dimension/zone tables inline, which is token-heavy padding despite an accurate core. It is mostly efficient but could be tightened considerably.

2 / 3

Actionability

Concrete command table, 'when to use' cadence, and natural-language examples are actionable, but the analytical engine (rp_why_core.py, rp_why_baseline.py, growth_nudge.py) is never referenced, so how reports are actually produced is only sketched ('DOK keyword classification, compression detection'). Guidance is concrete at the command level yet incomplete on methodology.

2 / 3

Workflow Clarity

Commands are sequenced with a clear first-time/weekly/monthly cadence and a backward-compatibility translation table, but there are no validation or verification checkpoints (e.g., confirming classification accuracy or handling missing sessions.db), leaving checkpoints implicit.

2 / 3

Progressive Disclosure

Bundle files exist in references/ and scripts/ (dok-levels.md, growth-zones.md, nudges-by-zone.md, orchestra-model.md, intent-compression.md, rp_why_core.py, etc.) but none are referenced from the body, while matching content (zone nudges, orchestra tiers, DOK levels) is duplicated inline rather than split out and signaled.

2 / 3

Total

8

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, trigger-rich, and explicitly states both what the skill does and when to load it, hitting the top anchor on every dimension. Third-person voice is maintained throughout, satisfying the voice guideline.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Analyzes session history,' 'surface how deeply you think with AI tools,' 'orchestration sophistication matches your cognitive depth,' 'Provides actionable nudges' — matching the score-3 anchor of multiple specific concrete actions.

3 / 3

Completeness

Explicitly answers both 'what' (analyzes session history, surfaces cognitive depth and orchestration match, provides nudges) and 'when' via the 'Load when reflecting on AI collaboration quality or tracking growth over time' trigger clause.

3 / 3

Trigger Term Quality

Includes natural terms a user would say — 'reflecting on AI collaboration quality,' 'tracking growth over time' — giving good coverage of trigger phrasing; not stuck on technical jargon.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (AI-collaboration self-reflection/growth tracking) with distinct triggers unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
block/agent-skills
Reviewed

Table of Contents

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