skill-optimization

name	skill-optimization
description	Evaluates and optimizes skill file quality using 8 content patterns and 9 editing principles. Use when creating skills, refining skill content, or auditing skill quality.

Skill Content Optimization

Core Philosophy

1. Evidence-Based: Grounded in prompt engineering research, applied to skill authoring
2. Concrete: Each pattern provides detection criteria and transform methods
3. Structure-Focused: Optimizes expression and organization; domain knowledge remains unchanged

Content Optimization Patterns

P1: Critical (Must Fix)

Issues that directly reduce LLM execution accuracy when consuming the skill.

BP-001: Negative Instructions → Positive Form

Detection

Transform

"don't", "do not", "never", "avoid" in skill instructions

Reframe as positive directive with equivalent constraint. Exception: Negative form is permitted only when ALL 4 conditions are met: (1) violation destroys state in a single step, (2) caller or subsequent steps cannot normally recover, (3) the constraint is operational/procedural, not a quality policy or role boundary, (4) positive rewording would expand or blur the target scope. If any condition is not met, rewrite in positive form.

Exception boundary examples:

• Permitted: "Do not modify the command", "Do not add flags", "Do not execute destructive operations"
• Rewrite in positive form: "Do not invent issues" → "Base every issue on BP patterns or 9 principles", "Do not skip P1 issues" → "Evaluate all P1 issues in every review mode", "Do not give grade A when P1 exists" → "Assign grade A only when P1 count is zero"

Quality policies, role boundaries, scoring criteria, and general work rules always use positive form. Outputs that the caller validates, overwrites, or discards are never irreversible.

Skill example:

• Before: "Don't use generic variable names"
• After: "Use descriptive variable names that reflect purpose (e.g., userId not x)"

Why critical for skills: LLM attention mechanisms focus on negated content. Skill instructions with "don't" increase probability of the forbidden behavior.

BP-002: Vague Instructions → Specific Criteria

Detection	Transform
"appropriate", "good", "proper", "best", "should be clear"	Replace with measurable if-then criteria or concrete thresholds. Skill exception: Expressions that the LLM can resolve unambiguously from input context (e.g., "where the user left gaps" when the user's prompt is available for comparison) are not vague — they describe a deterministic operation, not a subjective judgment.
Missing output format, scope, or success criteria	Add explicit constraints

Skill example:

• Before: "Handle errors appropriately"
• After: "Error handling criteria: 1. try-catch for external API calls, file I/O, JSON.parse 2. Log: error.name, error.stack, timestamp 3. Re-throw with context if caller needs to handle"

Why critical for skills: Accounts for ~40% of execution variance. Every vague instruction forces LLM to guess.

BP-003: Missing Output Format → Structured Output

Detection	Transform
Skill describes what to do but not the expected deliverable format	Add output section with structure, fields, and example

Skill example:

• Before: "Analyze the code for issues"
• After: "Output format: ## Issues Found with table: | Severity | Location | Description | Suggested Fix |"

Why critical for skills: Structured output constraints reduce hallucination and make skill results consistent.

P2: High Impact (Should Fix)

Issues that reduce skill effectiveness when addressed.

BP-004: Unstructured Content → Organized Format

Detection	Transform
Wall of text without headings	Apply standard section order (see below)
Multiple topics mixed in one section	Split into distinct headed sections
No tables for reference data	Convert lists of criteria/patterns to tables

Standard skill section order:

1. Context/Prerequisites
2. Core concepts (definitions, patterns)
3. Process/Methodology (step-by-step)
4. Output format/Examples
5. Quality checklist
6. References

Conditional: Skip restructuring if skill is under 30 lines and covers a single topic.

BP-005: Missing Context → Explicit Prerequisites

Detection	Transform
Skill assumes knowledge not stated	Add Prerequisites section listing required context
Domain terms used without definition	Add definitions inline or in a glossary table. Skill exception: Terms within the LLM's baseline knowledge (widely-used technical terminology, standard domain vocabulary) require no definition. Only project-specific terms, internal naming conventions, or domain jargon outside common LLM training data need explicit definition.
No "when to use" guidance	Add trigger conditions with concrete scenarios

Skill example:

• Before: "Apply the strangler pattern for migration"
• After: "Prerequisite: Existing monolith with identifiable module boundaries. When to use: Replacing legacy module while maintaining production traffic."

BP-006: Complex Content → Decomposed Steps

Detection	Transform
3+ objectives in one instruction	Break into numbered steps with checkpoints
Sequential dependencies not explicit	Add dependency markers between steps
No intermediate verification	Insert checkpoint after each step

Conditional: Skip decomposition for simple reference tables or single-criteria rules.

Key insight: Goal is evaluable granularity with quality checkpoints, not decomposition for its own sake.

P3: Enhancement (Could Fix)

Incremental improvements for specific contexts.

BP-007: Biased Examples → Diverse Coverage

Detection	Transform
All examples share same pattern/structure	Add edge cases and exceptions
Only happy-path examples	Add error cases, boundary conditions
Examples all same complexity	Include simple, moderate, and complex

BP-008: No Uncertainty Permission → Explicit Escalation

Detection	Transform
Skill demands definitive answers always	Add escalation criteria for ambiguous cases
No "when to stop" guidance	Add explicit stopping conditions

Skill example:

• Before: "Determine the root cause"
• After: "Determine the root cause. If root cause is uncertain after 3 investigation cycles, report top 3 hypotheses with confidence levels and evidence for each."

9 Skill Editing Principles

Measurable quality criteria for skill content. Each principle includes a pass/fail test.

#	Principle	Pass Criteria	Fail Example
1	Context efficiency	Every sentence contributes to LLM decision-making. No filler.	"This is an important skill that helps with..."
2	Deduplication	No concept explained twice at the same abstraction level within the skill or across skills. Mentions at different structural roles (e.g., classification framework vs execution detail) are not duplicates, provided the re-mention adds new constraints or criteria	Same error handling rules restated at the same abstraction level in multiple related skills
3	Grouping	Related criteria in single section (minimize read operations)	Scattered error handling rules across 4 sections
4	Measurability	All criteria use if-then format or concrete thresholds	"Write clean code" without definition of clean
5	Positive form	Instructions state what to do (BP-001 applied)	"Don't use any" instead of "Use only X"
6	Consistent notation	Uniform heading levels, list styles, table formats	Mix of -, *, 1. in same context
7	Explicit prerequisites	All assumed knowledge stated	Uses "DI" without defining Dependency Injection
8	Priority ordering	Most important items first, exceptions last	Edge cases before common patterns
9	Scope boundaries	Explicit coverage: what this skill addresses vs references to other skills	Overlapping guidance with no cross-reference

References

• Creating skills: See references/creation-guide.md for generation flow and description guidelines
• Reviewing skills: See references/review-criteria.md for evaluation flow and grading