Continuity

Continuity Framework - Reflection Only

Transform passive memory into active development. Standalone skill for OpenClaw bots without MLP storage dependency.

What This Does

1. Reflect — After sessions end, analyze what happened
2. Extract — Pull structured memories with types and confidence
3. Score — Assign confidence levels based on evidence
4. Question — Generate genuine questions from reflection
5. Surface — When user returns, present relevant questions

The Difference

Without Continuity:

Session ends → Notes logged → Next session reads notes → Performs familiarity

With Continuity:

Session ends → Reflection runs → Memories integrated → Questions generated
Next session → Evolved state loaded → Questions surfaced → Genuine curiosity

Commands

Reflect on Recent Session

continuity reflect [--session <transcript>]

Analyzes the most recent conversation, extracts memories, generates questions.

Show Pending Questions

continuity questions [--limit 5]

Lists questions generated from reflection, ready to surface.

View Memory State

continuity status

Shows memory stats: types, confidence distribution, recent integrations.

Surface Questions (for session start)

continuity greet

Returns context-appropriate greeting with any pending questions.

Mark Question Resolved

continuity resolve <question-id> [--summary "Answer summary"]

Marks a question as answered with optional summary.

Memory Types

Confidence Scores

File Structure

~/clawd/memory/
├── MEMORY.md           # Structured memories by type
├── identity.md         # Self-model and growth narrative
├── questions.md        # Pending questions from reflection
└── reflections/        # Reflection logs (JSON)

Configuration

Environment variables:

export CONTINUITY_MEMORY_DIR=~/clawd/memory
export CONTINUITY_IDLE_THRESHOLD=1800  # Seconds before reflection triggers
export CONTINUITY_MIN_MESSAGES=5       # Minimum messages to warrant reflection
export CONTINUITY_QUESTION_LIMIT=3     # Max questions to surface at once

Multi-Agent Architecture

This skill leverages specialized sub-agents for reflection:

┌────────────────────────────────────────────────────────┐
│  MAIN AGENT (User-facing, orchestrates reflection)     │
└────────────────────────────────────────────────────────┘
              ↓ sessions_send
    ┌─────────┴──────────────────┬─────────────────┐
    ↓                            ↓                  ↓
┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐
│ CLASSIFIER       │  │ SCORER           │  │ QUESTION GEN     │
│ (Sonnet)         │  │ (Sonnet)         │  │ (Sonnet)         │
├──────────────────┤  ├──────────────────┤  ├──────────────────┤
│ Classifies into  │  │ Assigns 0-1      │  │ Generates        │
│ 7 memory types   │  │ confidence       │  │ follow-up Qs     │
│ + extracts tags  │  │ scores           │  │ from gaps        │
└──────────────────┘  └──────────────────┘  └──────────────────┘

Heartbeat Integration

Add to HEARTBEAT.md:

## Post-Session Reflection
**Trigger**: Heartbeat after conversation idle > 30 minutes
**Action**: Run continuity reflect
**Output**: Updated memories + questions for next session

Example Reflection Output

reflection:
  session_id: "2026-02-05-001"
  duration_minutes: 45

memories:
  - type: fact
    content: "Riley is building SIGIL protocol for agent identity"
    confidence: 0.98
    source: explicit

  - type: commitment
    content: "Agreed to build the continuity skill"
    confidence: 1.0
    source: explicit

  - type: relationship
    content: "Collaborative partnership deepening"
    confidence: 0.85
    source: inferred

questions:
  - "How is the Lovable backend progressing?"
  - "Has Riley decided on the token launch timing?"
  - "Are there other agents we should coordinate with?"

identity_update:
  growth: "Developing more autonomous initiative"
  narrative: "Moving from assistant to co-builder"

Storage Format

Memories are stored in human-readable markdown with metadata in HTML comments:

## Fact

- Riley works on AI memory infrastructure
  <!-- {"id":"mem_abc123","confidence":{"score":0.98,"level":"explicit"}} -->

## Preference

- Prefers concise, direct communication
  <!-- {"id":"mem_def456","confidence":{"score":0.95,"level":"explicit"}} -->

Usage Notes

• No MLP required — This skill stores memories locally in markdown files
• Git-friendly — All files are plain text, easy to version control
• Human-readable — Memories can be reviewed and edited manually
• Portable — Copy the memory directory to migrate to any system

Full Stack Alternative

For persistent encrypted storage with MLP (IPFS/Pinata), see:

• Full Stack Skill — Continuity + MLP storage layer

Related

• Continuity Framework — Core reflection library
• MLP Storage — Encrypted storage layer (optional)

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