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kaizen

Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.

42

Quality

42%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/kaizen/skills/kaizen/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

42%

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

The skill is highly actionable with excellent, executable TypeScript examples and clear Good/Bad comparisons. However, it severely violates its own principles—it's massively over-engineered for what should be a concise set of guidelines, explaining well-known concepts (YAGNI, guard clauses, Result types) that Claude already understands. The content would be far more effective at 1/3 its current length with detailed examples split into referenced files.

Suggestions

Reduce the main SKILL.md to ~80-100 lines covering the four pillars with one brief example each, and move detailed examples into separate files (e.g., POKA-YOKE-EXAMPLES.md, JIT-EXAMPLES.md)

Remove explanations of concepts Claude already knows (YAGNI definition, what guard clauses are, why early returns are good) and keep only the project-specific conventions and decision criteria

Add explicit validation checkpoints to the iterative refinement workflow (e.g., 'run tests after each iteration', 'verify no regressions before proceeding')

Consolidate the redundant Good/Bad example pairs—many make the same point (e.g., multiple examples showing 'don't over-engineer') and could be reduced to one representative case per principle

DimensionReasoningScore

Conciseness

The skill is extremely verbose at ~400+ lines. It explains concepts Claude already knows well (YAGNI, early returns, guard clauses, Result types, iterative refinement). Many examples are redundant—showing both Good and Bad versions of obvious anti-patterns like premature optimization and over-engineering. The irony is that a skill about avoiding over-engineering is itself over-engineered.

1 / 3

Actionability

The skill provides fully executable TypeScript code examples throughout, with concrete before/after patterns, clear Good/Bad comparisons, and specific guidance on when to apply each principle. The code is copy-paste ready and demonstrates real-world patterns.

3 / 3

Workflow Clarity

The 'In Practice' sections provide reasonable step sequences (e.g., iterative refinement steps 1-5, 'When implementing' checklists), but there are no explicit validation checkpoints or feedback loops. For a skill that involves refactoring and code changes, the absence of verify/test/rollback steps between iterations is a gap.

2 / 3

Progressive Disclosure

Everything is in a single monolithic file with no references to supporting documents. The content would benefit enormously from splitting—e.g., detailed examples for each pillar could be in separate files, with SKILL.md serving as a concise overview. The commands section references /why, /cause-and-effect etc. but doesn't link to where these are defined.

1 / 3

Total

7

/

12

Passed

Description

42%

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 attempts to define when the skill should be used but covers too broad a scope, making it indistinguishable from general software engineering assistance. It lacks concrete specific actions and clear deliverables, and contains a typo ('tehniquest'). The conflation of 'what' and 'when' weakens both aspects.

Suggestions

Narrow the scope to a specific niche (e.g., 'KISS/YAGNI-focused code review') and list concrete actions like 'simplifies complex class hierarchies, removes unnecessary abstractions, suggests incremental refactoring steps'.

Separate the 'what it does' from the 'when to use it' — e.g., 'Provides techniques for simplifying over-engineered code. Use when the user mentions over-engineering, YAGNI, simplifying architecture, or reducing complexity.'

Fix the typo 'tehniquest' → 'techniques' and use third-person voice throughout (e.g., 'Provides techniques...' rather than starting with 'Use when').

DimensionReasoningScore

Specificity

Names a domain (code/systems) and lists some actions like 'implementation and refactoring', 'architecturing or designing systems', 'error handling and validation', but these are broad categories rather than concrete specific actions. 'Provide techniques to avoid over-engineering' is somewhat specific but still vague about what those techniques are.

2 / 3

Completeness

The description starts with 'Use when' which provides trigger guidance, but the 'what' portion is weak — it doesn't clearly state what the skill does (outputs, deliverables). The 'when' triggers are essentially just a list of broad topics rather than explicit situational triggers. The 'what' and 'when' are conflated rather than clearly separated.

2 / 3

Trigger Term Quality

Includes some relevant keywords like 'refactoring', 'error handling', 'over-engineering', and 'iterative improvements' that users might naturally say. However, it's missing many common variations and the terms are fairly broad. Also contains a typo ('tehniquest') which could hurt matching.

2 / 3

Distinctiveness Conflict Risk

The description covers extremely broad territory — code implementation, refactoring, system design, process improvements, error handling — which would overlap with virtually any coding or software engineering skill. There is no clear niche that distinguishes this from general programming assistance.

1 / 3

Total

7

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (734 lines); consider splitting into references/ and linking

Warning

Total

10

/

11

Passed

Repository
NeoLabHQ/context-engineering-kit
Reviewed

Table of Contents

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