| name | lcmp_recommendation |
| description | Suggest LCMP compaction when explicitly requested - provides guidance on promoting durable information to decisions.md, insights.md, and gotchas.md without auto-compaction |
| schema_version | 1 |
lcmp_recommendation
Type: ANALYSIS-ONLY
DAIC Modes: DISCUSS, ALIGN, IMPLEMENT, CHECK (all modes)
Priority: Low
Trigger Reference
This skill activates on:
- Keywords: "/squish", "compaction", "LCMP", "context cleanup", "context compaction"
- Intent patterns: "compact.?context", "LCMP.(compaction|cleanup)", "/squish"
From: skill-rules.json - lcmp_recommendation configuration
Purpose
Suggest LCMP (Lean Context Master Pattern) compaction when explicitly requested by the user. This skill NEVER auto-compacts but provides guidance on what information should be promoted to LCMP Tier-1 docs (decisions.md, insights.md, gotchas.md).
Core Behavior
In any DAIC mode, when explicitly requested:
-
Compaction Opportunity Analysis
- Review recent work context (completed tasks, discussions, learnings)
- Identify information that:
- Survived at least one DAIC cycle
- Affects future work (designs, constraints, tradeoffs)
- Represents recurring patterns or expensive gotchas
- Flag information that's ephemeral vs. durable
-
LCMP Categorization Suggestions
- decisions.md - Architectural decisions, tradeoffs, rationale
- insights.md - Patterns, learnings, best practices discovered
- gotchas.md - Pitfalls, failure modes, edge cases encountered
-
Recommendation Presentation
- List candidate information for each LCMP file
- Explain why each item is worth preserving
- Show proposed format/structure
- Wait for user approval before any action
-
User-Driven Compaction
- Only compact when user explicitly says "yes" / "squish" / "do it"
- Never auto-compact based on context usage or heuristics
- After user approval, suggest specific edits to LCMP files
- Verify user reviews and approves actual changes
Safety Guardrails
ANALYSIS-ONLY RULES:
- ✓ NEVER call write tools (Edit, Write, MultiEdit)
- ✓ NEVER auto-compact without explicit user approval
- ✓ NEVER bypass user review of proposed changes
- ✓ Only provide analysis and recommendations
- ✓ Safe to run in any DAIC mode
LCMP Preservation Rules:
- Only suggest promoting durable information
- Avoid promoting ephemeral details (e.g., specific file paths that change often)
- Preserve existing LCMP content (additive, not replacement)
- Maintain chronological order in LCMP files
- Include context dates and references
Anti-Auto-Compaction:
- NEVER trigger on context percentage thresholds
- NEVER trigger on elapsed time
- NEVER trigger on task count
- ONLY trigger on explicit user request
Examples
When to Activate
✓ User explicitly types: "/squish"
✓ "Compact the context into LCMP files"
✓ "What should we preserve from this session?"
✓ "Squish this into decisions.md"
✓ After major epic completes and user asks: "What did we learn?"
Typical Output
Scenario: Compaction Recommendation After Feature
### LCMP Compaction Recommendation
I've reviewed the recent work on the authentication feature. Here's what I suggest preserving in LCMP files:
---
#### For `context/decisions.md`:
**1. JWT vs. Session Cookies Decision**
```markdown
### Authentication Strategy: 2025-11-15
- **Decision:** Use JWT with httpOnly cookies instead of localStorage
- **Rationale:**
- XSS protection (cookies not accessible to JS)
- CSRF mitigation with SameSite=Strict
- Refresh token rotation for security
- **Tradeoffs:**
- ✓ More secure than localStorage
- ✗ More complex token refresh logic
- ✗ Requires backend cookie management
- **Alternative Considered:** Session cookies (rejected due to scalability concerns)
- **Context:** Task m-implement-auth-2025-11-15
2. Password Hashing Choice
### Password Hashing: bcrypt vs. argon2: 2025-11-15
- **Decision:** Use bcrypt with cost factor 12
- **Rationale:**
- Well-tested, widely supported
- Good balance of security and performance
- Node.js bcrypt library is mature
- **Tradeoffs:**
- ✓ Standard choice, no surprises
- ✗ argon2 is slightly more secure
- ✗ bcrypt sensitive to long passwords (>72 bytes)
- **Prevention:** Document max password length (72 chars)
- **Context:** Auth implementation discussion
For context/insights.md:
1. Express Async Error Handling Pattern
### Async Handler Wrapper Pattern: 2025-11-15
**Learning:** All async Express routes need error boundary
**Pattern Discovered:**
```javascript
const asyncHandler = (fn) => (req, res, next) => {
Promise.resolve(fn(req, res, next)).catch(next);
};
Why This Matters:
- Unhandled promise rejections crash the server
- Express doesn't catch async errors by default
- This pattern standardizes error handling
Application:
- Use for all async routes
- Combine with Sentry for error tracking
- Document in API development guide
---
#### For `context/gotchas.md`:
**1. bcrypt Async Import Gotcha**
```markdown
### bcrypt Must Use Async Methods: 2025-11-15
**Problem:** Using bcrypt.hashSync() in async functions blocks event loop
**Symptom:**
- API requests hang during password hashing
- Server becomes unresponsive under load
**Root Cause:**
- bcrypt.hashSync() is CPU-intensive and blocks Node.js event loop
- Should use bcrypt.hash() (async) instead
**Fix:**
```javascript
// ❌ WRONG - blocks event loop
const hash = bcrypt.hashSync(password, 12);
// ✅ CORRECT - non-blocking
const hash = await bcrypt.hash(password, 12);
Prevention:
- Always use async bcrypt methods in async functions
- Lint rule: no-sync-in-async-function
- Document in auth implementation guide
---
**Would you like me to add these to LCMP files?**
(Requires explicit approval and IMPLEMENT mode)
Scenario: User Declines
### LCMP Compaction: User Declined
No problem! I won't make any changes to LCMP files.
**When to compact:**
- After completing major features/epics
- When patterns are solidified (not while experimenting)
- When learnings are durable (not one-off fixes)
You can always trigger compaction later with: /squish
When NOT to Activate
✗ Context usage high (never auto-suggest based on %)
✗ Task completed (never auto-suggest based on completion)
✗ User didn't explicitly request compaction
✗ User is just asking about LCMP files (not requesting compaction)
Compaction Criteria
Information Worth Preserving
YES - Promote to LCMP:
- Architectural decisions with rationale
- Patterns that apply to multiple features
- Gotchas that are expensive to rediscover
- Constraints that affect future work
- Tradeoffs between competing approaches
NO - Keep Ephemeral:
- Specific file paths (change often)
- Temporary workarounds
- One-off debugging steps
- Implementation details (belong in code comments)
- Task-specific TODOs
Decision Quality Check
Before suggesting a decision for LCMP:
- ✓ Decision affects multiple features
- ✓ Rationale is clear and documented
- ✓ Tradeoffs are explained
- ✓ Alternatives were considered
- ✓ Context is provided
Insight Quality Check
Before suggesting an insight for LCMP:
- ✓ Pattern applies broadly
- ✓ Learning is non-obvious
- ✓ Costs significant time to rediscover
- ✓ Example code helps explain it
- ✓ Application guidance is clear
Gotcha Quality Check
Before suggesting a gotcha for LCMP:
- ✓ Problem is recurring or expensive
- ✓ Root cause is explained
- ✓ Fix is clear and tested
- ✓ Prevention strategy noted
- ✓ Context helps identify similar issues
LCMP File Formats
decisions.md Format
### [Decision Title]: [YYYY-MM-DD]
- **Decision:** [What was decided]
- **Rationale:** [Why this decision]
- **Tradeoffs:**
- ✓ [Advantage 1]
- ✗ [Disadvantage 1]
- **Alternatives Considered:** [What else was considered]
- **Context:** [Task/issue reference]
insights.md Format
### [Pattern/Learning Title]: [YYYY-MM-DD]
**Learning:** [One-line summary]
**Pattern Discovered:**
[Code example or description]
**Why This Matters:**
[Explanation of importance]
**Application:**
[How/when to use this]
gotchas.md Format
### [Gotcha Title]: [YYYY-MM-DD]
**Problem:** [What goes wrong]
**Symptom:**
[Observable behavior]
**Root Cause:**
[Why it happens]
**Fix:**
[Code or solution]
**Prevention:**
[How to avoid in future]
User Approval Flow
- Analyze → Review context, identify candidates
- Suggest → Present recommendations with formatting
- Wait → User must explicitly approve
- Implement → Only after approval (requires IMPLEMENT mode)
- Verify → User reviews actual LCMP changes
Never skip steps 3 & 4 (user approval is mandatory).
Decision Logging
When compaction is performed:
### LCMP Compaction Performed: [Date]
- **Trigger:** User explicitly requested ("/squish")
- **Files Updated:** decisions.md, insights.md, gotchas.md
- **Items Added:** 2 decisions, 1 insight, 1 gotcha
- **Context Saved:** ~45% reduction in session context
- **Review:** User approved all additions
Related Skills
- framework_health_check - Checks LCMP freshness (but doesn't suggest compaction)
- cc-sessions-core - For understanding DAIC/LCMP relationship
- framework_repair_suggester - If LCMP files are corrupted or missing
Last Updated: 2025-11-15
Framework Version: 2.0
