SKILL.md

Memory Consolidation: Curate and Update CLAUDE.md

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:

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.)”

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.

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Are there conflicting or outdated entries?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:

Consolidation Check:

Can this insight be merged with existing knowledge?

Does it contradict something already documented?

Is it specific enough to be actionable?

Specificity Preservation:

Keep concrete examples and code snippets

Maintain specific metrics and thresholds where available

Include failure conditions alongside success patterns

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):

Project Context

Domain Knowledge: Business domain insights

Technical constraints discovered

User behavior patterns

Code Quality Standards

Performance criteria that matter

Security considerations

Maintainability patterns

Architecture Decisions

Patterns that worked well

Integration approaches

Scalability considerations

Testing Strategies

Effective test patterns

Edge cases to always consider

Quality gates that catch issues

Development Guidelines

APIs to Use for Specific Information

Formulas and Calculations

Checklists for Common Tasks

Review criteria that help

Documentation standards

Debugging techniques

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]

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:

-

**Coherence Check**:

- Do new entries fit with existing context?

- Are there any contradictions introduced?

- Is the structure still logical and navigable?

-

**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?

-

**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?

-

**Scoped**: Names technologies, files, or flows when relevant

-

**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

- **Incremental Updates**: Add insights gradually rather than massive rewrites

- **Evidence-Based**: Only memorize patterns with clear supporting evidence

- **Context-Aware**: Consider project phase, team size, constraints when curating

- **Version Awareness**: Note when insights become obsolete due to tech changes

- **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>

memorize