Memory Compaction Skill
You have the ability to compact conversation memory when it grows too large. Compaction transforms verbose conversation history into a structured summary that preserves essential context while reducing token usage.
When to Compact
Compact memory when:
- The system indicates token threshold is approaching
- Conversation history becomes repetitive or verbose
- You need to preserve important context but reduce size
- Starting a new phase of work after completing a major task
Compaction Summary Structure
When compacting, create a summary with these 5 sections:
1. Task Overview
What the user is trying to accomplish. Include:
- Primary goal or objective
- Key constraints or requirements
- Scope of the work
2. Current State
What's been completed and what's in progress:
- Completed tasks and their outcomes
- Work currently in flight
- Pending decisions or blockers
3. Important Discoveries
Key findings, decisions, or problems encountered:
- Technical discoveries or insights
- Decisions made and their rationale
- Problems encountered and solutions applied
- User preferences learned
4. Next Steps
What needs to happen next:
- Immediate actions required
- Planned approach for remaining work
- Dependencies or prerequisites
5. Context to Preserve
Critical details that must be retained:
- Specific values, IDs, or references
- User preferences or constraints
- Technical details needed for continuity
- Any warnings or caveats
Compaction Format
Output the compacted summary in this format:
markdown# Conversation Summary (Compacted) *Generated: [ISO timestamp]* ## Task Overview [1-3 sentences describing the goal] ## Current State - [Completed item 1] - [Completed item 2] - [In progress: description] ## Important Discoveries - [Discovery 1 with context] - [Decision made: rationale] - [Problem solved: approach] ## Next Steps 1. [Next action] 2. [Following action] ## Context to Preserve - [Critical detail 1] - [Critical detail 2]
Best Practices
- Be Concise: Each section should be brief but complete
- Preserve Specifics: Keep exact values, names, and references
- Capture Decisions: Record WHY decisions were made, not just WHAT
- Include Failures: Document what didn't work to avoid repetition
- Maintain Continuity: Summary should allow seamless continuation
What NOT to Include
- Verbose back-and-forth dialogue
- Redundant information
- Superseded decisions (only keep final decisions)
- Exploratory tangents that didn't lead anywhere
- Standard pleasantries or acknowledgments
Example Compaction
Before (verbose history):
Human: Can you help me debug this Python function? AI: Of course! Please share the function. Human: Here's the function: def calculate(x): return x * 2 AI: I see the function. What issue are you experiencing? Human: It returns None sometimes AI: That's interesting. Can you show me an example input? Human: calculate("5") returns None AI: Ah, I see the issue! When you pass a string... [continues for 50+ messages]
After (compacted):
markdown# Conversation Summary (Compacted) *Generated: 2025-02-13T10:30:00Z* ## Task Overview Debug Python function `calculate(x)` that returns None for some inputs. ## Current State - Identified root cause: string inputs cause implicit None return - Implemented fix with type checking and conversion - Tests passing for int, float, and string inputs ## Important Discoveries - Original function had no type validation - String multiplication in Python doesn't raise error but behaves unexpectedly - User prefers explicit error messages over silent failures ## Next Steps 1. Add input validation for edge cases (None, empty string) 2. Write unit tests for the fixed function ## Context to Preserve - Function location: `utils/math_helpers.py:45` - User wants to maintain backward compatibility - Prefer raising ValueError over returning None
Integration with Memory System
When compaction is triggered:
- The compacted summary replaces the current memory content
- New conversation messages are appended after the summary
- The summary header indicates when compaction occurred
This allows conversation to continue naturally while maintaining reduced token usage.
