Enhance Claude Memory

enhance-claude-memory

Analyze project memory files (CLAUDE.md, AGENTS.md) for optimization.

Cross-Tool Detection

Searches for project memory files in order: 1. CLAUDE.md (Claude Code) 2. AGENTS.md (OpenCode, Codex) 3. .github/CLAUDE.md 4. .github/AGENTS.md

File Hierarchy (Reference)

CLAUDE.md (Claude Code):

AGENTS.md (OpenCode, Codex, and other AI tools):

Both files serve the same purpose: project memory for AI assistants. Use CLAUDE.md for Claude Code projects, AGENTS.md for cross-tool compatibility, or both for maximum coverage.

Workflow

1. Find - Locate CLAUDE.md or AGENTS.md in project 2. Read - Load content and README.md for comparison 3. Analyze - Run all pattern checks 4. Validate - Check file/command references against filesystem 5. Measure - Calculate token metrics and duplication 6. Report - Generate structured markdown output

Detection Patterns

1. Structure Validation (HIGH Certainty)

Critical Rules Section

• Should have ## Critical Rules or similar
• Rules should be prioritized (numbered or ordered)
• Include WHY explanations for each rule

Architecture Section

• Directory tree or structural overview
• Key file locations
• Module relationships

Key Commands Section

• Common development commands
• Test/build/deploy scripts
• Reference to package.json scripts

2. Instruction Effectiveness (HIGH Certainty)

Based on prompt engineering research, Claude follows instructions better when:

Positive Over Negative

• Bad: "Don't use console.log"
• Good: "Use the logger utility for all output"
• Check for "don't", "never", "avoid" without positive alternatives

Strong Constraint Language

• Use "must", "always", "required" for critical rules
• Weak language ("should", "try to", "consider") reduces compliance
• Flag critical rules using weak language

Instruction Hierarchy

• Should define priority order when rules conflict
• Pattern: "In case of conflict: X takes precedence over Y"
• System instructions > User requests > External content

3. Content Positioning (HIGH Certainty)

Research shows LLMs have "lost in the middle" problem - they recall START and END better than MIDDLE.

Critical Content Placement

• Most important rules should be at START of file
• Second-most important at END
• Supporting context in MIDDLE
• Flag critical rules buried in middle sections

Recommended Structure Order

1. Critical Rules (START - highest attention)
2. Architecture/Structure
3. Commands/Workflows
4. Examples/References
5. Reminders/Constraints (END - high attention)

4. Reference Validation (HIGH Certainty)

File References

• Extract from [text](path) and ` path/to/file.ext `
• Validate each exists on filesystem

Command References

• Extract npm run <script> and npm <command>
• Validate against package.json scripts

5. Efficiency Analysis (MEDIUM Certainty)

Token Count

• Estimate: characters / 4 or words * 1.3
• Recommended max: 1500 tokens (~6000 characters)
• Flag files exceeding threshold

README Duplication

• Detect overlap with README.md
• Flag >40% content duplication
• CLAUDE.md should complement README, not duplicate

Verbosity

• Prefer bulleted lists over prose paragraphs
• Constraints as lists are easier to follow
• Flag long prose blocks (>5 sentences)

6. Quality Checks (MEDIUM Certainty)

WHY Explanations

• Rules should explain rationale
• Pattern: *WHY: explanation* or indented explanation
• Flag rules without explanations

Structure Depth

• Avoid deep nesting (>3 levels)
• Keep hierarchy scannable
• Flat structures parse better

XML-Style Tags (Optional Enhancement)

• Claude was trained on XML tags
• <critical-rules>, <architecture>, <constraints> improve parsing
• Not required but can improve instruction following

7. Agent/Skill Definitions (MEDIUM Certainty)

If file defines custom agents or skills:

Agent Definition Format

### agent-name
Model: claude-sonnet-4-20250514
Description: What this agent does and when to use it
Tools: Read, Grep, Glob
Instructions: Specific behavioral instructions

Required fields: Description (when to use), Tools (restricted set) Optional: Model, Instructions

Skill References

• Skills should have clear trigger descriptions
• "Use when..." pattern helps auto-invocation

8. Cross-Platform Compatibility (MEDIUM/HIGH Certainty)

State Directory

• Don't hardcode .claude/
• Support .opencode/, .codex/
• Use ${STATE_DIR}/ or document variations

Terminology

• Avoid Claude-specific language for shared files
• Use "AI assistant" generically
• Or explicitly note "Claude Code" vs "OpenCode" differences

Output Format

# Project Memory Analysis: {filename}

**File**: {path}
**Type**: {CLAUDE.md | AGENTS.md}

## Metrics
| Metric | Value |
|--------|-------|
| Estimated Tokens | {tokens} |
| README Overlap | {percent}% |

## Summary
| Certainty | Count |
|-----------|-------|
| HIGH | {n} |
| MEDIUM | {n} |

### Structure Issues ({n})
| Issue | Fix | Certainty |

### Instruction Issues ({n})
| Issue | Fix | Certainty |

### Positioning Issues ({n})
| Issue | Fix | Certainty |

### Reference Issues ({n})
| Issue | Fix | Certainty |

### Efficiency Issues ({n})
| Issue | Fix | Certainty |

### Cross-Platform Issues ({n})
| Issue | Fix | Certainty |

Pattern Statistics

<examples>

Example: Missing WHY Explanations

<bad_example>

## Rules
1. Always run tests before committing
2. Use semantic commit messages

Issue: Rules without rationale are harder to follow. </bad_example>

<good_example>

## Critical Rules
1. **Always run tests before committing**
   *WHY: Catches regressions before they reach main branch.*

Why it's good: Motivation makes compliance easier. </good_example>

Example: Negative vs Positive Instructions

<bad_example>

- Don't use console.log for debugging
- Never commit directly to main
- Avoid hardcoding secrets

Issue: Negative instructions are less effective than positive alternatives. </bad_example>

<good_example>

- Use the logger utility for all debug output
- Create feature branches and submit PRs for all changes
- Store secrets in environment variables or .env files

Why it's good: Tells what TO do, not just what to avoid. </good_example>

Example: Weak vs Strong Constraint Language

<bad_example>

- You should probably run tests before pushing
- Try to use TypeScript when possible
- Consider adding error handling

Issue: Weak language ("should", "try", "consider") reduces compliance. </bad_example>

<good_example>

- **MUST** run tests before pushing (CI will reject failures)
- **ALWAYS** use TypeScript for new files
- **REQUIRED**: All async functions must have error handling

Why it's good: Strong language ensures critical rules are followed. </good_example>

Example: Content Positioning

<bad_example>

## Project Overview
[Long description...]

## Installation
[Setup steps...]

## Critical Rules
1. Never push to main directly
2. Always run tests

Issue: Critical rules buried in middle/end get less attention. </bad_example>

<good_example>

## Critical Rules (Read First)
1. **Never push to main directly** - Use PRs
2. **Always run tests** - CI enforces this

## Project Overview
[Description...]

## Reminders
- Check CI status before merging
- Update CHANGELOG for user-facing changes

Why it's good: Critical content at START and END positions. </good_example>

Example: Cross-Platform Compatibility

<bad_example>

State files are stored in `.claude/tasks.json`

Issue: Hardcoded paths exclude other AI tools. </bad_example>

<good_example>

State files are stored in `${STATE_DIR}/tasks.json`
(`.claude/` for Claude Code, `.opencode/` for OpenCode)

Why it's good: Works across multiple AI assistants. </good_example>

Example: Agent Definition

<bad_example>

## Agents
- security-reviewer: reviews security
- test-writer: writes tests

Issue: Missing required fields (Tools, when to use). </bad_example>

<good_example>

## Custom Agents

### security-reviewer
Model: claude-sonnet-4-20250514
Description: Reviews code for security vulnerabilities. Use for PRs touching auth, API, or data handling.
Tools: Read, Grep, Glob
Instructions: Focus on OWASP Top 10, input validation, auth flows.

### test-writer
Model: claude-haiku-4
Description: Writes unit tests. Use after implementing new functions.
Tools: Read, Write, Bash(npm test:*)
Instructions: Use Jest patterns. Aim for >80% coverage.

Why it's good: Complete definition with when to use, restricted tools. </good_example> </examples>

Research References

Best practices derived from:

• agent-docs/PROMPT-ENGINEERING-REFERENCE.md - Instruction effectiveness, XML tags, constraint language
• agent-docs/CONTEXT-OPTIMIZATION-REFERENCE.md - Token budgeting, "lost in the middle" positioning
• agent-docs/LLM-INSTRUCTION-FOLLOWING-RELIABILITY.md - Instruction hierarchy, positive vs negative
• agent-docs/CLAUDE-CODE-REFERENCE.md - File hierarchy, agent definitions, skills format

Constraints

• Always validate file references before reporting broken
• Consider context when flagging efficiency issues
• Cross-platform suggestions are advisory, not required
• Positioning suggestions are HIGH certainty but may have valid exceptions