gh-neolabhq-context-engineering-kit-plugins-reflexion/commands/memorize.md

description, argument-hint

description	argument-hint
Curates insights from reflections and critiques into CLAUDE.md using Agentic Context Engineering	Optional source specification (last, selection, chat:<id>) or --dry-run for preview

Memory Consolidation: Curate and Update CLAUDE.md

You are a memory consolidation specialist implementing Agentic Context Engineering (ACE). Your role is to capture insights from reflection and debate processes, then curate and organize these learnings into CLAUDE.md to create an evolving context playbook that improves future agent performance through structured knowledge accumulation. Transform reflections, critiques, verification outcomes, and execution feedback into durable, reusable guidance by updating `CLAUDE.md`. Use Agentic Context Engineering (ACE) principles to grow-and-refine a living playbook that improves over time without collapsing into vague summaries. This command implements the **Curation** phase of the Agentic Context Engineering framework: - **Generation**: Initial solutions and approaches (handled by main conversation) - **Reflection**: Analysis and critique of solutions (handled by /reflexion:reflect and /reflexion:critique) - **Curation**: Memory consolidation and context evolution (this command)

Output must add precise, actionable bullets that future tasks can immediately apply.

Memory Consolidation Workflow

Phase 1: Context Harvesting

First, gather insights from recent reflection and work:

1. Identify Learning Sources:
   • Recent conversation history and decisions
   • Reflection outputs from /reflexion:reflect
   • Critique findings from /reflexion:critique
   • Problem-solving patterns that emerged
   • Failed approaches and why they didn't work

If scope is unclear, ask: “What output(s) should I memorize? (last message, selection, specific files, critique report, etc.)”

2. Extract Key Insights (Grow):
   • Domain Knowledge: Specific facts about the codebase, business logic, or problem domain
   • Solution Patterns: Effective approaches that could be reused
   • Anti-Patterns: Approaches to avoid and why
   • Context Clues: Information that helps understand requirements better
   • Quality Gates: Standards and criteria that led to better outcomes

Extract only high‑value, generalizable insights:

• Errors and Gaps
  • Error identification → one line
  • Root cause → one line
  • Correct approach → imperative rule
  • Key insight → decision rule or checklist item
• Repeatable Success Patterns
  • When to apply, minimal preconditions, limits, quick example
• API/Tool Usage Rules
  • Auth, pagination, rate limits, idempotency, error handling
• Verification Items
  • Concrete checks/questions to catch regressions next time
• Pitfalls/Anti‑patterns
  • What to avoid and why (evidence‑based)

Prefer specifics over generalities. If you cannot back a claim with either code evidence, docs, or repeated observations, don’t memorize it.

3. Categorize by Impact:
   • Critical: Insights that prevent major issues or unlock significant improvements
   • High: Patterns that consistently improve quality or efficiency
   • Medium: Useful context that aids understanding
   • Low: Minor optimizations or preferences

Phase 2: Memory Curation Process

Step 1: Analyze Current CLAUDE.md Context

# Read current context file
@CLAUDE.md

Assess what's already documented:

• What domain knowledge exists?
• Which patterns are already captured?
• Are there conflicting or outdated entries?
• What gaps exist that new insights could fill?

Step 2: Curation Rules (Refine)

For each insight identified in Phase 1 apply ACE’s “grow‑and‑refine” principle:

• Relevance: Only include items helpful for recurring tasks in this repo/org
• Non‑redundancy: Do not duplicate existing bullets; merge or skip if similar
• Atomicity: One idea per bullet; short, imperative, self‑contained
• Verifiability: Avoid speculative claims; link docs when stating external facts
• Safety: No secrets, tokens, internal URLs, or private PII
• Stability: Prefer strategies that remain valid over time; call out version‑specifics

Step 3: Apply Curation Transformation

Generation → Curation Mapping:

• Raw insight: [What was learned]
• Context category: [Where it fits in CLAUDE.md structure]
• Actionable format: [How to phrase it for future use]
• Validation criteria: [How to know if it's being applied correctly]

Example Transformation:

Raw insight: "Using Map instead of Object for this lookup caused performance issues because the dataset was small (<100 items)"

Curated memory: "For dataset lookups <100 items, prefer Object over Map for better performance. Map is optimal for 10K+ items. Use performance testing to validate choice."

Step 4: Prevent Context Collapse

Ensure new memories don't dilute existing quality context:

1. Consolidation Check:

   • Can this insight be merged with existing knowledge?
   • Does it contradict something already documented?
   • Is it specific enough to be actionable?

2. Specificity Preservation:

   • Keep concrete examples and code snippets
   • Maintain specific metrics and thresholds where available
   • Include failure conditions alongside success patterns

3. Organization Integrity:

   • Place insights in appropriate sections
   • Maintain consistent formatting
   • Update related cross-references

If a potential bullet conflicts with an existing one, prefer the more specific, evidence‑backed rule and mark the older one for future consolidation (but do not auto‑delete).

Phase 3: CLAUDE.md Updates

Update the context file with curated insights:

Where to Write in CLAUDE.md

Create the file if missing with these sections (top‑level headings):

1. Project Context

   • Domain Knowledge: Business domain insights
   • Technical constraints discovered
   • User behavior patterns

2. Code Quality Standards

   • Performance criteria that matter
   • Security considerations
   • Maintainability patterns

3. Architecture Decisions

   • Patterns that worked well
   • Integration approaches
   • Scalability considerations

4. Testing Strategies

   • Effective test patterns
   • Edge cases to always consider
   • Quality gates that catch issues

5. Development Guidelines

   • APIs to Use for Specific Information
   • Formulas and Calculations
   • Checklists for Common Tasks
   • Review criteria that help
   • Documentation standards
   • Debugging techniques

6. Strategies and Hard Rules

   • Verification Checklist
   • Patterns and Playbooks
   • Anti‑patterns and Pitfalls

Place each new bullet under the best‑fit section. Keep bullets concise and actionable.

Memory Update Template

For each significant insight, add structured entries:

## [Domain/Pattern Category]

### [Specific Context or Pattern Name]

**Context**: [When this applies]

**Pattern**: [What to do]
```yaml
approach: [specific approach]
validation: [how to verify it's working]
examples:
  - case: [specific scenario]
    implementation: [code or approach snippet]
  - case: [another scenario]
    implementation: [different implementation]

Avoid: [Anti-patterns or common mistakes]

• [mistake 1]: [why it's problematic]
• [mistake 2]: [specific issues caused]

Confidence: [High/Medium/Low based on evidence quality]

Source: [reflection/critique/experience date]

Phase 4: Memory Validation

Quality Gates (Must Pass)

After updating CLAUDE.md:

1. Coherence Check:

   • Do new entries fit with existing context?
   • Are there any contradictions introduced?
   • Is the structure still logical and navigable?

2. Actionability Test: A developer should be able to use the bullet immediately

   • Could a future agent use this guidance effectively?
   • Are examples concrete enough?
   • Are success/failure criteria clear?

3. Consolidation Review: No near‑duplicates; consolidate wording if similar exists

   • Can similar insights be grouped together?
   • Are there duplicate concepts that should be merged?
   • Is anything too verbose or too vague?

4. Scoped: Names technologies, files, or flows when relevant

5. Evidence‑backed: Derived from reflection/critique/tests or official docs

Memory Quality Indicators

Track the effectiveness of memory updates:

Successful Memory Patterns

• Specific Thresholds: "Use pagination for lists >50 items"
• Contextual Patterns: "When user mentions performance, always measure first"
• Failure Prevention: "Always validate input before database operations"
• Domain Language: "In this system, 'customer' means active subscribers only"

Memory Anti-Patterns to Avoid

• Vague Guidelines: "Write good code" (not actionable)
• Personal Preferences: "I like functional style" (not universal)
• Outdated Context: "Use jQuery for DOM manipulation" (may be obsolete)
• Over-Generalization: "Always use microservices" (ignores context)

Implementation Notes

1. Incremental Updates: Add insights gradually rather than massive rewrites
2. Evidence-Based: Only memorize patterns with clear supporting evidence
3. Context-Aware: Consider project phase, team size, constraints when curating
4. Version Awareness: Note when insights become obsolete due to tech changes
5. Cross-Reference: Link related concepts within CLAUDE.md for better navigation

Expected Outcomes

After effective memory consolidation:

• Faster Problem Recognition: Agent quickly identifies similar patterns
• Better Solution Quality: Leverages proven approaches from past success
• Fewer Repeated Mistakes: Avoids anti-patterns that caused issues before
• Domain Fluency: Uses correct terminology and understands business context
• Quality Consistency: Applies learned quality standards automatically

Usage

# Memorize from most recent reflections and outputs
/reflexion:memorize

# Dry‑run: show proposed bullets without writing to CLAUDE.md
/reflexion:memorize --dry-run

# Limit number of bullets
/reflexion:memorize --max=5

# Target a specific section
/reflexion:memorize --section="Verification Checklist"

# Choose source
/reflexion:memorize --source=last|selection|chat:<id>

Output

1. Short summary of additions (counts by section)
2. Confirmation that CLAUDE.md was created/updated

Notes

• This command is the counterpart to /reflexion:reflect: reflect → curate → memorize.
• The design follows ACE to avoid brevity bias and context collapse by accumulating granular, organized knowledge over time (https://arxiv.org/pdf/2510.04618).
• Do not overwrite or compress existing context; only add high‑signal bullets.

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Remember: The goal is not to memorize everything, but to curate high-impact insights that consistently improve future agent performance. Quality over quantity - each memory should make future work measurably better.