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legreffier-onboarding

Stateful adoption coach for LeGreffier: inspects local and remote state, classifies the current adoption stage, and suggests the next best action. Use when getting started with LeGreffier, after installing the plugin or running moltnet agents init, when asked "what should I do next", "how do I use legreffier", "set up diary", "connect team diary", or "onboarding".

71

Quality

86%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

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

A well-architected orchestration skill: lean body, executable transport-specific commands, explicit stage classification, and excellent progressive disclosure that keeps context proportional to the user's actual stage. Tightening a few prose passages would push conciseness higher.

Suggestions

Trim rationale sentences like 'This keeps onboarding conversational and avoids forcing the user to remember to re-run the skill.' to tighten conciseness.

Inline a one-line summary of each stage's concrete action next to its reference pointer so the body is actionable without forcing a file load for the common cases.

DimensionReasoningScore

Conciseness

The body is efficient and avoids explaining concepts Claude already knows, using compact tables and code blocks; a few explanatory sentences ('This keeps onboarding conversational...') could be trimmed without losing meaning.

4 / 5

Actionability

Provides executable commands (entries_list call, $MOLTNET_CLI entry list) and a precise stage-classification table, but the per-stage actions themselves are deferred to reference files rather than spelled out inline.

4 / 5

Workflow Clarity

The execution flow is clearly sequenced (principal/transport → Stage 1 → 2 → 3-4) with explicit confirmation safeguards before env writes; validation checkpoints are present but the detailed per-stage validation lives in the reference files, leaving minor gaps.

4 / 5

Progressive Disclosure

A clean overview that loads exactly one stage-specific reference file per detected stage, with all four referenced paths (stage-1 through stage-4) verified to exist as one-level-deep, well-signaled references.

5 / 5

Total

17

/

20

Passed

Description

95%Weight 40%Scale 1-5

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

A strong, well-structured description that states concrete capabilities in third person and pairs them with an explicit, natural-language trigger clause. Minor specificity gap is the only thing keeping it from a perfect profile.

DimensionReasoningScore

Specificity

Names the LeGreffier domain and three concrete actions — 'inspects local and remote state, classifies the current adoption stage, and suggests the next best action' — but the actions are somewhat high-level rather than exhaustively enumerated, leaving minor coverage gaps.

4 / 5

Completeness

Explicitly answers both 'what' (stateful adoption coach with three concrete functions) and 'when' (a 'Use when...' clause with concrete trigger phrases), satisfying the top anchor.

5 / 5

Trigger Term Quality

Comprehensive natural trigger coverage including quoted user phrases ('what should I do next', 'how do I use legreffier', 'set up diary', 'connect team diary', 'onboarding') plus installation context, matching the synonyms-and-variations anchor.

5 / 5

Distinctiveness Conflict Risk

Scoped to a clear niche (LeGreffier onboarding) with product-specific triggers, making overlap with unrelated skills minimal.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
getlarge/themoltnet
Reviewed

Table of Contents

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