Claude-skill-registry agent-ops-optimize-instructions
Optimize agent instruction files by extracting sections into separate files and referencing them. Reduces context size while preserving information.
git clone https://github.com/majiayu000/claude-skill-registry
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/agent-ops-optimize-instructions" ~/.claude/skills/majiayu000-claude-skill-registry-agent-ops-optimize-instructions && rm -rf "$T"
skills/data/agent-ops-optimize-instructions/SKILL.mdInstruction File Optimization Workflow
Purpose
Optimize agent or instruction files (AGENTS.md, copilot-instructions.md, SKILL.md, etc.) by:
- Identifying logical sections that can be extracted
- Moving verbose content to separate reference files
- Replacing with concise summaries and links
- Reducing total context size while preserving information
This helps keep prompt context within token limits while maintaining comprehensive guidance.
When to Use
- Instruction file exceeds recommended size (~2000 lines)
- File contains large code examples or detailed procedures
- Multiple sections could be shared across files
- Agent consistently runs out of context
- Preparing instructions for smaller context models
Optimization Strategies
Strategy 1: Section Extraction
Extract large, self-contained sections into reference files.
Before:
# AGENTS.md ## API Guidelines {500 lines of detailed API guidelines} ## Testing Patterns {300 lines of testing examples}
After:
# AGENTS.md ## API Guidelines See: [api-guidelines.md](.github/reference/api-guidelines.md) Key points: REST conventions, error handling, versioning. ## Testing Patterns See: [testing-patterns.md](.github/reference/testing-patterns.md) Key points: pytest fixtures, mocking, coverage targets.
Strategy 2: Example Consolidation
Move detailed examples to reference files, keep summaries inline.
Before:
### Example: Complex Query ```python def complex_query(db, filters, pagination, sorting): # 50 lines of example code ...
**After:** ```markdown ### Example: Complex Query See: [examples/complex-query.py](.github/reference/examples/complex-query.py) Pattern: Filter → Paginate → Sort → Execute
Strategy 3: Conditional Loading
Mark sections as "load on demand" for agents that support it.
## Advanced Configuration <!-- LOAD_ON_DEMAND: Only load when user asks about configuration --> See: [advanced-config.md](.github/reference/advanced-config.md)
Strategy 4: Tiered Detail
Keep high-level summary inline, link to detailed version.
## Error Handling **Quick reference:** Try/catch, log, return Result type. **Full guide:** [error-handling.md](.github/reference/error-handling.md)
Procedure
Phase 1: Analysis
- Measure current size: Count lines, estimate tokens
- Identify section boundaries: Headers, code blocks, tables
- Classify sections by extraction potential:
- ✅ Extract: Large (>50 lines), self-contained, reference-style
- ⚠️ Consider: Medium (20-50 lines), may need context
- ❌ Keep: Small (<20 lines), critical, frequently referenced
- Check for duplication: Same content in multiple files?
Phase 2: Extraction Planning
For each section marked "Extract":
- Choose target location:
— General reference docs.github/reference/
— Code examples.github/reference/examples/
— Language-specific.github/reference/lang-{language}.md
- Draft summary (2-5 lines) to replace full content
- Identify key points that must stay inline
- Plan cross-references if content is shared
Phase 3: User Confirmation
Present optimization plan:
## Instruction Optimization Plan: AGENTS.md Current size: 2,450 lines (~98,000 tokens) Target size: ~800 lines (~32,000 tokens) Reduction: 67% ### Proposed Extractions | Section | Lines | Target File | Summary | |---------|-------|-------------|---------| | API Guidelines | 520 | reference/api-guidelines.md | REST conventions, errors | | Python Patterns | 380 | reference/lang-python.md | pytest, typing, logging | | Code Examples | 450 | reference/examples/ | 12 example files | ### Sections to Keep Inline - Core workflow (critical path) - Quick reference tables - Navigation links Proceed? [Y]es / [E]dit plan / [C]ancel
Phase 4: Execution
- Create reference directory if needed
- Extract sections to target files:
- Copy full content
- Add header with source reference
- Format as standalone document
- Update source file:
- Replace with summary + link
- Preserve header structure
- Add "See: file" references
- Validate links: Ensure all references resolve
Phase 5: Verification
- Check file sizes: Source reduced, references created
- Validate markdown: No broken links, proper formatting
- Test with agent: Verify instructions still work
- Report results:
✅ Optimization Complete Before: 2,450 lines (98k tokens) After: 812 lines (32k tokens) Reduction: 67% Created reference files: - .github/reference/api-guidelines.md (520 lines) - .github/reference/lang-python.md (380 lines) - .github/reference/examples/ (12 files) The instruction file now fits comfortably in standard context windows.
Reference File Template
When creating extracted reference files:
--- title: {Section Title} extracted_from: {source file path} extracted_date: {YYYY-MM-DD} --- # {Section Title} {Full content from original section} --- *This file was extracted from [{source}]({path}) to optimize context size.*
Size Guidelines
| File Type | Recommended Max | Action if Exceeded |
|---|---|---|
| AGENTS.md | 1,000 lines | Extract to references |
| SKILL.md | 500 lines | Extract examples |
| copilot-instructions.md | 500 lines | Use skill references |
| Prompt files | 100 lines | Keep focused |
Integration with Project Sections
Use
agent-ops-project-sections to:
- Identify which instruction sections map to which code sections
- Extract language-specific guidance to appropriate reference files
- Scope instructions to relevant project areas
Completion Criteria
- Source file size reduced to target
- All extracted content preserved in reference files
- Links validate (no 404s)
- Summaries capture key points
- Agent tested with optimized instructions
- Size reduction reported
Anti-patterns (avoid)
- ❌ Extracting critical workflow steps (keep inline)
- ❌ Creating too many tiny files (consolidate related content)
- ❌ Losing context between extracted sections
- ❌ Breaking cross-references
- ❌ Removing all examples (keep 1-2 inline for quick reference)
Examples
Example: Optimizing copilot-instructions.md
Before analysis:
- 1,847 lines
- Contains: Python guide (400 lines), TypeScript guide (350 lines), C# guide (380 lines)
After optimization:
- 412 lines (main file)
- Created:
,lang-python.md
,lang-typescript.mdlang-csharp.md - Main file contains summaries + links to language guides
User benefit: Instructions now fit in 16k context window instead of requiring 64k+.