Skill Content Optimization

Core Philosophy

• Evidence-Based: Grounded in prompt engineering research, applied to skill authoring

• Concrete: Each pattern provides detection criteria and transform methods

• 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

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

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:

• Context/Prerequisites

• Core concepts (definitions, patterns)

• Process/Methodology (step-by-step)

• Output format/Examples

• Quality checklist

• 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

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 within the skill or across skills Same error handling rules in both coding-standards and typescript-rules

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