/cf-remember

The /cf-remember skill captures important project knowledge and saves it for future reference. In subsequent sessions, the AI can read and apply this knowledge, accelerating work.

Usage

/cf-remember [topic]

What It Does

1. Captures Context — Extracts relevant code patterns and decisions
2. Documents Learning — Writes clear explanations of how things work
3. Saves to Memory — Stores in docs/memory/ with proper organization
4. Indexes for Retrieval — Makes knowledge searchable and linkable
5. Enables Cross-Session Reference — Future work uses these docs

Examples

/cf-remember auth flow decisions and implementation

/cf-remember database schema and relationships

/cf-remember error handling patterns used across the codebase

Memory Organization

Knowledge is saved to docs/memory/ with intuitive structure:

docs/memory/
  ├── architecture/
  │   ├── system-overview.md
  │   └── microservices-design.md
  ├── patterns/
  │   ├── error-handling.md
  │   ├── middleware-chain.md
  │   └── state-management.md
  ├── decisions/
  │   ├── why-we-chose-postgres.md
  │   ├── api-versioning-strategy.md
  │   └── caching-approach.md
  └── gotchas/
      └── known-issues.md

Document Format

Each document includes:

• What (behavior or design)
• Why (reasoning and trade-offs)
• How (code examples and implementation)
• Related patterns (cross-references)

When to Use

• After onboarding to understand current patterns
• When completing complex features
• After discovering important design decisions
• When solving challenging problems (for future reference)
• Documenting gotchas and lessons learned

AI Workflow Benefits

With good memory:

• AI understands project patterns faster
• Decisions get applied consistently
• Onboarding new contributors is faster
• Knowledge doesn't get lost between sessions