The description identifies a specific technical domain (state machines/lifecycle validation) but is too terse to be effective for skill selection. It lacks concrete action details, natural trigger terms users would say, and critically missing any 'when to use' guidance that would help Claude select this skill appropriately.

Suggestions

Add a 'Use when...' clause with explicit triggers like 'Use when validating state machines, checking FSM correctness, reviewing lifecycle transitions, or analyzing workflow states'

Expand specific capabilities: 'Validate state machine transitions, detect unreachable states, verify terminal conditions, check lifecycle completeness'

Include natural keyword variations users might say: 'FSM', 'finite state machine', 'state diagram', 'workflow', 'transitions'

Dimension	Reasoning	Score
Specificity	Names the domain (state machines, lifecycle) and one action (validate correctness), but lacks comprehensive concrete actions like 'check transitions', 'verify terminal states', or 'detect unreachable states'.	2 / 3
Completeness	Only addresses 'what' at a high level (validate state machines). Completely missing 'when' guidance - no 'Use when...' clause or explicit trigger conditions.	1 / 3
Trigger Term Quality	Includes relevant terms 'state machine' and 'lifecycle' that users might say, but misses common variations like 'FSM', 'finite state', 'state diagram', 'transitions', or 'workflow validation'.	2 / 3
Distinctiveness Conflict Risk	'State machine' and 'lifecycle' provide some specificity, but 'validate correctness' is generic and could overlap with general code validation, testing, or verification skills.	2 / 3
	Total	7 / 12 Passed

Implementation

100%

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

This is an excellent skill that demonstrates strong technical writing. It clearly defines scope boundaries (what it checks vs. doesn't check), provides executable commands and concrete JSON schemas, and organizes rules by severity with specific review checklists. The detection step with early-exit is a particularly good pattern for efficiency.

Dimension	Reasoning	Score
Conciseness	The content is lean and efficient, with no unnecessary explanations of concepts Claude already knows. Every section serves a clear purpose and the token budget is well-respected.	3 / 3
Actionability	Provides fully executable bash commands for input detection, concrete JSON output schemas, and specific review checklists for each rule. The guidance is copy-paste ready and leaves no ambiguity about what to do.	3 / 3
Workflow Clarity	Clear sequential workflow: detect changes → filter files → check relevance → either early exit or full analysis → categorize findings → output JSON. Explicit validation checkpoints with the detection step providing an early-exit path.	3 / 3
Progressive Disclosure	Well-organized with clear sections (Scope, Input, Detection, Rules by severity, Output). For a self-contained validator skill, the structure is appropriate without needing external file references.	3 / 3
	Total	12 / 12 Passed

Validation

81%

Warnings & errors only

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

Validation — 9 / 11 Passed

Validation for skill structure

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	9 / 11 Passed

Reviewed

2 months ago

Table of Contents

Discovery Implementation Validation