continuity

name	continuity
description	Memory reflection and continuity for AI agents. Transforms passive logging into active development through asynchronous reflection, structured memory extraction, and genuine question generation.
version	1.0.0

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

Type	Description	Persistence
fact	Declarative knowledge	Until contradicted
preference	Likes, dislikes, styles	Until updated
relationship	Connection dynamics	Long-term
principle	Learned guidelines	Stable
commitment	Promises, obligations	Until fulfilled
moment	Significant episodes	Permanent
skill	Learned capabilities	Cumulative

Confidence Scores

Level	Range	Meaning
Explicit	0.95-1.0	User directly stated
Implied	0.70-0.94	Strong inference
Inferred	0.40-0.69	Pattern recognition
Speculative	0.0-0.39	Tentative, needs confirmation

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)