analyse

Auto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target

Quality

55%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/kaizen/skills/analyse/SKILL.md

Quality

Content

57%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.

The content is highly actionable with concrete process steps and comprehensive worked examples, but it is long and redundant (selection logic repeated three times), lacks explicit validation checkpoints, and keeps heavy example detail inlined rather than split across reference files.

Suggestions

De-duplicate the method-selection guidance: state it once and have per-method 'When to Use' sections and the Notes section reference it rather than restate it.

Move the full worked examples into separate reference files (e.g. references/gemba-example.md) and keep SKILL.md as a concise overview pointing to them, improving both conciseness and progressive disclosure.

Add explicit validation checkpoints to each method's process (e.g. 'Verify each SURPRISE against the actual code before reporting' or 'Confirm findings are actionable') to lift workflow clarity.

Dimension	Reasoning	Score
Conciseness	The ~500-line body is mostly substantive (concrete examples and processes rather than conceptual padding), but method-selection logic is restated three times (top section, per-method 'When to Use', and Notes) and the three full worked examples are heavy, so it could be tightened.	3 / 5
Actionability	Each method has concrete numbered process steps plus full worked-output templates (SCOPE/ASSUMPTIONS/OBSERVATIONS/SURPRISES/GAPS/RECOMMENDATIONS) covering code, workflow, and waste cases, giving mostly executable guidance with only minor gaps (the template is implicit and method selection is interpretive).	4 / 5
Workflow Clarity	Sequences are clear (an overall 5-step list plus per-method numbered processes), but there are no explicit validate/verify checkpoint steps; the assumption-vs-reality comparison and metrics serve only as implicit checkpoints.	3 / 5
Progressive Disclosure	The in-file section structure is good with clear headers, but no bundle files exist and the long worked examples (50-90 lines each) are inlined in a single 500-line file rather than split into reference files, so content that could be separate remains inline.	3 / 5
	Total	13 / 20 Passed

Description

53%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.

The description is concise and names a clear niche with specific methods, but it omits any explicit 'Use when' trigger guidance and relies on specialized jargon rather than natural user phrases, leaving it solidly mid-range across the dimensions.

Suggestions

Add an explicit 'Use when...' clause with concrete trigger phrases, e.g. 'Use when analyzing code, workflows, or process inefficiencies, or when the user mentions waste, bottlenecks, or Kaizen techniques'.

Broaden trigger terms to include natural-language synonyms users actually say ('analyse code', 'improve workflow', 'find waste/inefficiencies') alongside the domain jargon.

Expand the action list beyond 'auto-selects' to name what the skill actually performs (e.g. 'guides you through Gemba Walk, Value Stream Mapping, or Muda analysis') to lift specificity.

Dimension	Reasoning	Score
Specificity	Names the Kaizen domain and a concrete action ('Auto-selects best Kaizen method') and enumerates three specific named methods (Gemba Walk, Value Stream, Muda), but the action set is limited to selecting rather than listing several distinct actions.	3 / 5
Completeness	It clearly states what the skill does (auto-selects and applies a Kaizen method) but provides no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guidelines.	3 / 5
Trigger Term Quality	Relevant domain keywords are present (Kaizen, Gemba Walk, Value Stream, Muda) but they are specialized jargon, missing the natural everyday phrases a user would actually say ('analyse code', 'improve workflow', 'find inefficiencies').	3 / 5
Distinctiveness Conflict Risk	It carves a clear niche around three named lean-analysis methods with minimal conflict risk, though the body's reference to a sibling '/analyse-problem' (A3) skill signals minor overlap risk with closely related analysis skills.	4 / 5
	Total	13 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (511 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: NeoLabHQ/context-engineering-kit
Path: plugins/kaizen/skills/analyse/SKILL.md
Commit: 23e2428

Reviewed: 7 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.