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

36

Quality

33%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./plugins/kaizen/skills/analyse/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

27%

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

This skill is comprehensive in scope but severely over-extended for a single SKILL.md file. The detailed examples are well-structured and show clear output formats, but the sheer volume (~400+ lines) makes it a poor use of context window. The content would benefit enormously from splitting into separate method files and condensing the main skill to method selection logic with brief summaries.

Suggestions

Split each method (Gemba Walk, VSM, Muda) into separate referenced files (e.g., methods/gemba.md, methods/vsm.md, methods/muda.md) and keep only the method selection logic and brief summaries in SKILL.md.

Reduce examples to one per method (or move examples to separate files), as two full examples per method is excessive for context window efficiency.

Condense the 7 waste types to a brief table or list rather than full paragraph descriptions—Claude already understands these Lean concepts.

Add validation steps to the workflow, such as confirming method selection with the user before proceeding, and verifying findings against actual code/data before presenting recommendations.

DimensionReasoningScore

Conciseness

Extremely verbose at ~400+ lines. The extensive examples (authentication Gemba Walk, CI/CD Gemba Walk, Feature Development VSM, Incident Response VSM, full Muda analysis) are illustrative but massively bloat the skill. Much of this is teaching Claude concepts it already knows (what the 7 wastes are, what a value stream map is). The method selection logic and waste type definitions could be dramatically condensed.

1 / 3

Actionability

The skill provides detailed examples showing expected output formats, which is useful. However, there is no executable code or concrete commands—everything is template/pseudocode output examples. The actual 'Steps' section (5 steps) is vague ('Understand what's being analyzed', 'Guide through the analysis'). The skill describes what outputs should look like rather than giving Claude precise instructions on how to produce them.

2 / 3

Workflow Clarity

Each method has a numbered process, and the overall steps are listed. However, there are no validation checkpoints or feedback loops. The top-level 5-step process is generic and lacks specificity. The method-specific processes are clearer but still lack verification steps (e.g., how to confirm the right method was selected, how to validate findings before presenting).

2 / 3

Progressive Disclosure

This is a monolithic wall of text with all three methods fully detailed inline. The three methods with their extensive examples should be split into separate files (e.g., GEMBA.md, VSM.md, MUDA.md) with the main SKILL.md providing an overview and method selection logic. No bundle files exist to support this, and no references to external files are made for the detailed content.

1 / 3

Total

6

/

12

Passed

Description

40%

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 identifies a clear niche in Kaizen methodology selection and names specific methods, giving it good distinctiveness. However, it lacks a 'Use when...' clause, doesn't describe what the skill actually produces or does after selection, and misses natural language trigger terms that users would commonly use when seeking process improvement help.

Suggestions

Add a 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about process improvement, continuous improvement, lean manufacturing, waste reduction, or operational efficiency.'

Describe the concrete output or actions beyond selection, e.g., 'Auto-selects and applies the best Kaizen method (Gemba Walk, Value Stream Mapping, or Muda analysis) to analyze a target process, then generates an improvement report with actionable recommendations.'

Include common synonyms and natural language variations like 'lean', 'continuous improvement', 'waste elimination', 'process optimization' to improve trigger term coverage.

DimensionReasoningScore

Specificity

Names the domain (Kaizen) and lists three specific methods (Gemba Walk, Value Stream, Muda), but doesn't describe what concrete actions the skill performs beyond 'auto-selects.' It doesn't explain what happens after selection—does it generate a report, run an analysis, produce recommendations?

2 / 3

Completeness

Describes a partial 'what' (auto-selects a Kaizen method) but completely lacks a 'when' clause. There is no 'Use when...' or equivalent trigger guidance, and per the rubric, a missing 'Use when' clause caps completeness at 2, but the 'what' is also weak since it doesn't explain the output or full action, warranting a 1.

1 / 3

Trigger Term Quality

Includes domain-specific terms like 'Kaizen', 'Gemba Walk', 'Value Stream', and 'Muda' which are relevant but fairly technical. Missing natural user language like 'continuous improvement', 'lean manufacturing', 'waste reduction', 'process improvement' that users might actually say.

2 / 3

Distinctiveness Conflict Risk

The combination of Kaizen methodology selection with specific named methods (Gemba Walk, Value Stream, Muda) creates a very clear niche that is unlikely to conflict with other skills.

3 / 3

Total

8

/

12

Passed

Validation

81%

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

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

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

9

/

11

Passed

Repository
NeoLabHQ/context-engineering-kit
Reviewed

Table of Contents

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