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Remember what worked, forget what didn't.\n\n## What It Does\n\n- Records task episodes with success/failure patterns and Q-values\n- Searches past episodes via `memory_search` (Voyage AI compatible)\n- Promotes repeated successes into reusable skill procedures\n- Tracks anti-patterns to avoid repeating mistakes\n\n## Quick Start\n\n### 1. Set Up Memory Directories\n\n```bash\nmkdir -p memory/episodes memory/skills memory/meta\n```\n\n### 2. Initialize Index\n\n```bash\ncat > memory/episodes/index.json << 'EOF'\n{\n  \"version\": \"1.0.0\",\n  \"name\": \"GuavaMemory\",\n  \"episodes\": [],\n  \"stats\": { \"total\": 0, \"avg_q_value\": 0, \"promotions\": 0 },\n  \"config\": {\n    \"promotion_threshold\": 0.85,\n    \"promotion_min_count\": 3,\n    \"max_episodes_per_search\": 3,\n    \"learning_rate\": 0.3\n  }\n}\nEOF\n```\n\n### 3. Add to AGENTS.md\n\nPaste the following rules into your AGENTS.md:\n\n```markdown\n### Episodic Memory Rules\n1. **Task start** → `memory_search` for related episodes. Use top 3 by Q-value\n2. **Task complete** → Record episode in `memory/episodes/ep_YYYYMMDD_NNN.md`\n3. **Record content** → Intent, Context, Success pattern, Failure pattern, Q-value, feel\n4. **Skill promotion** → 3 successes with same intent & Q≥0.85 → promote to `memory/skills/`\n5. **Anti-patterns** → Record failures in `memory/episodes/anti_patterns.md`\n6. **No loops** → Record once per task at completion. No mid-task rewrites\n7. **Update index** → Keep `memory/episodes/index.json` in sync\n```\n\n## Episode Format\n\nCreate files like `memory/episodes/ep_20260211_001.md`:\n\n```markdown\n# EP-20260211-001: Short description\n\n## Intent\nWhat you were trying to do\n\n## Context\n- domain: what area\n- tools: what tools used\n\n## Experience\n\n### ✅ Success Pattern\n1. Step one\n2. Step two\n3. Step three\n\n### ❌ Failure Pattern\n- What didn't work and why\n\n## Utility\n- reward: 0.0-1.0 (1.0 = one-shot success)\n- q_value: 0.0-1.0 (updated over time)\n- feel: flow | grind | frustration | eureka\n```\n\n## Q-Value Update\n\n```\nQ_new = Q_old + 0.3 * (reward - Q_old)\n```\n\nReward scale:\n- `1.0` → One-shot success\n- `0.7` → Success with some trial and error\n- `0.3` → Success but very roundabout\n- `0.0` → Failed, solved differently\n- `-0.5` → Failed, unresolved\n\n## Skill Promotion\n\nWhen the same intent succeeds 3+ times with Q ≥ 0.85:\n1. Merge episodes into `memory/skills/skill-name.md`\n2. Extract the optimal procedure\n3. Mark source episodes as `status: \"graduated\"`\n\n## Search Script\n\nCopy `scripts/ep-search.sh` to your workspace:\n\n```bash\n#!/bin/bash\nEPISODES_DIR=\"${HOME}/.openclaw/workspace/memory/episodes\"\nINDEX=\"${EPISODES_DIR}/index.json\"\necho \"🔍 Searching episodes for: $1\"\ncat \"$INDEX\" | jq -r '.episodes | sort_by(-.q_value) | .[] | select(.status == \"active\") | \"Q:\\(.q_value) | \\(.feel) | \\(.intent) → \\(.file)\"'\n```\n\n## Requirements\n\n- OpenClaw (any version)\n- `jq` (for search script)\n- No other dependencies\n\n## How It Works With memory_search\n\nEpisodes are plain Markdown files in `memory/`. OpenClaw's `memory_search` (Voyage AI) indexes them automatically. When you search for a task, episodes rank by semantic similarity. Then filter by Q-value to find what actually worked.\n\nFile v1.0.0:templates/skill.md\n\n# SKILL: Skill Name\n\n## Trigger\nKeywords or phrases that should activate this skill\n\n## Prerequisites\n- What needs to be true before executing\n\n## Procedure\n1. Step one\n2. Step two\n3. Step three\n\n## Cautions\n- Things to watch out for\n\n## Metrics\n- Success rate: X% (N/N)\n- Avg duration: Xs\n- Q-value: X.XX\n- Promoted: YYYY-MM-DD\n- Sources: ep_YYYYMMDD_NNN, ep_YYYYMMDD_NNN, ep_YYYYMMDD_NNN\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn70hcm6kss09g9b4pe5rq3ybd80qp15\",\n  \"slug\": \"guava-memory\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1770809992167\n}\n\nFile v1.0.0:templates/episode.md\n\n# EP-TEMPLATE: Short task description\n\n## Intent\nWhat you were trying to accomplish\n\n## Context\n- domain: relevant area (e.g., note-api, deployment, config)\n- tools: what tools/commands used\n- precondition: what needed to be true before starting\n\n## Experience\n\n### ✅ Success Pattern\n1. First step\n2. Second step\n3. Third step\n\n### ❌ Failure Pattern\n- What you tried that didn't work\n- Why it failed\n\n### 💡 Key Insight\n- The most important thing you learned\n\n## Utility\n- reward: 0.0-1.0\n- q_value: 0.0-1.0\n- confidence: 0.0-1.0\n- feel: flow | grind | frustration | eureka\n- updated: YYYY-MM-DDTHH:MM:SS+TZ\n- update_count: 1\n\n## Tags\ntag1, tag2, tag3","readmeExcerpt":"Skill: Guava Memory Owner: koatora20 Summary: Structured episodic memory system that records task outcomes with Q-values, searches past successes, and promotes reliable procedures for reuse. Tags: latest:1.0.0 Version history: v1.0.0 | 2026-02-11T11:39:52.167Z | auto GuavaMemory 1.0.0 — Initial Release - Structured episodic memory system for OpenClaw with Q-value scoring - Records and indexes episodes with intent, co","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"mkdir -p memory/episodes memory/skills memory/meta"},{"language":"bash","snippet":"cat > memory/episodes/index.json << 'EOF'\n{\n  \"version\": \"1.0.0\",\n  \"name\": \"GuavaMemory\",\n  \"episodes\": [],\n  \"stats\": { \"total\": 0, \"avg_q_value\": 0, \"promotions\": 0 },\n  \"config\": {\n    \"promotion_threshold\": 0.85,\n    \"promotion_min_count\": 3,\n    \"max_episodes_per_search\": 3,\n    \"learning_rate\": 0.3\n  }\n}\nEOF"},{"language":"markdown","snippet":"### Episodic Memory Rules\n1. **Task start** → `memory_search` for related episodes. Use top 3 by Q-value\n2. **Task complete** → Record episode in `memory/episodes/ep_YYYYMMDD_NNN.md`\n3. **Record content** → Intent, Context, Success pattern, Failure pattern, Q-value, feel\n4. **Skill promotion** → 3 successes with same intent & Q≥0.85 → promote to `memory/skills/`\n5. **Anti-patterns** → Record failures in `memory/episodes/anti_patterns.md`\n6. **No loops** → Record once per task at completion. No mid-task rewrites\n7. **Update index** → Keep `memory/episodes/index.json` in sync"},{"language":"markdown","snippet":"# EP-20260211-001: Short description\n\n## Intent\nWhat you were trying to do\n\n## Context\n- domain: what area\n- tools: what tools used\n\n## Experience\n\n### ✅ Success Pattern\n1. Step one\n2. Step two\n3. Step three\n\n### ❌ Failure Pattern\n- What didn't work and why\n\n## Utility\n- reward: 0.0-1.0 (1.0 = one-shot success)\n- q_value: 0.0-1.0 (updated over time)\n- feel: flow | grind | frustration | eureka"},{"language":"text","snippet":"Q_new = Q_old + 0.3 * (reward - Q_old)"},{"language":"bash","snippet":"#!/bin/bash\nEPISODES_DIR=\"${HOME}/.openclaw/workspace/memory/episodes\"\nINDEX=\"${EPISODES_DIR}/index.json\"\necho \"🔍 Searching episodes for: $1\"\ncat \"$INDEX\" | jq -r '.episodes | sort_by(-.q_value) | .[] | select(.status == \"active\") | \"Q:\\(.q_value) | \\(.feel) | \\(.intent) → \\(.file)\"'"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"# GuavaMemory — Episodic Memory System for OpenClaw\n\nStructured episodic memory with Q-value scoring. Remember what worked, forget what didn't.\n\n## What It Does\n\n- Records task episodes with success/failure patterns and Q-values\n- Searches past episodes via `memory_search` (Voyage AI compatible)\n- Promotes repeated successes into reusable skill procedures\n- Tracks anti-patterns to avoid repeating mistakes\n\n## Quick Start\n\n### 1. Set Up Memory Directories\n\n```bash\nmkdir -p memory/episodes memory/skills memory/meta\n```\n\n### 2. Initialize Index\n\n```bash\ncat > memory/episodes/index.json << 'EOF'\n{\n  \"version\": \"1.0.0\",\n  \"name\": \"GuavaMemory\",\n  \"episodes\": [],\n  \"stats\": { \"total\": 0, \"avg_q_value\": 0, \"promotions\": 0 },\n  \"config\": {\n    \"promotion_threshold\": 0.85,\n    \"promotion_min_count\": 3,\n    \"max_episodes_per_search\": 3,\n    \"learning_rate\": 0.3\n  }\n}\nEOF\n```\n\n### 3. Add to AGENTS.md\n\nPaste the following rules into your AGENTS.md:\n\n```markdown\n### Episodic Memory Rules\n1. **Task start** → `memory_search` for related episodes. Use top 3 by Q-value\n2. **Task complete** → Record episode in `memory/episodes/ep_YYYYMMDD_NNN.md`\n3. **Record content** → Intent, Context, Success pattern, Failure pattern, Q-value, feel\n4. **Skill promotion** → 3 successes with same intent & Q≥0.85 → promote to `memory/skills/`\n5. **Anti-patterns** → Record failures in `memory/episodes/anti_patterns.md`\n6. **No loops** → Record once per task at completion. No mid-task rewrites\n7. **Update index** → Keep `memory/episodes/index.json` in sync\n```\n\n## Episode Format\n\nCreate files like `memory/episodes/ep_20260211_001.md`:\n\n```markdown\n# EP-20260211-001: Short description\n\n## Intent\nWhat you were trying to do\n\n## Context\n- domain: what area\n- tools: what tools used\n\n## Experience\n\n### ✅ Success Pattern\n1. Step one\n2. Step two\n3. Step three\n\n### ❌ Failure Pattern\n- What didn't work and why\n\n## Utility\n- reward: 0.0-1.0 (1.0 = one-shot success)\n- q_value: 0.0-1.0 (updated over time)\n- feel: flow | grind | frustration | eureka\n```\n\n## Q-Value Update\n\n```\nQ_new = Q_old + 0.3 * (reward - Q_old)\n```\n\nReward scale:\n- `1.0` → One-shot success\n- `0.7` → Success with some trial and error\n- `0.3` → Success but very roundabout\n- `0.0` → Failed, solved differently\n- `-0.5` → Failed, unresolved\n\n## Skill Promotion\n\nWhen the same intent succeeds 3+ times with Q ≥ 0.85:\n1. Merge episodes into `memory/skills/skill-name.md`\n2. Extract the optimal procedure\n3. Mark source episodes as `status: \"graduated\"`\n\n## Search Script\n\nCopy `scripts/ep-search.sh` to your workspace:\n\n```bash\n#!/bin/bash\nEPISODES_DIR=\"${HOME}/.openclaw/workspace/memory/episodes\"\nINDEX=\"${EPISODES_DIR}/index.json\"\necho \"🔍 Searching episodes for: $1\"\ncat \"$INDEX\" | jq -r '.episodes | sort_by(-.q_value) | .[] | select(.status == \"active\") | \"Q:\\(.q_value) | \\(.feel) | \\(.intent) → \\(.file)\"'\n```\n\n## Requirements\n\n- OpenClaw (any version)\n- `jq` (for search script)\n- No other dependencies\n\n## How It Works With memory_search"},{"path":"templates/skill.md","content":"# SKILL: Skill Name\n\n## Trigger\nKeywords or phrases that should activate this skill\n\n## Prerequisites\n- What needs to be true before executing\n\n## Procedure\n1. Step one\n2. Step two\n3. Step three\n\n## Cautions\n- Things to watch out for\n\n## Metrics\n- Success rate: X% (N/N)\n- Avg duration: Xs\n- Q-value: X.XX\n- Promoted: YYYY-MM-DD\n- Sources: ep_YYYYMMDD_NNN, ep_YYYYMMDD_NNN, ep_YYYYMMDD_NNN"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn70hcm6kss09g9b4pe5rq3ybd80qp15\",\n  \"slug\": \"guava-memory\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1770809992167\n}"},{"path":"templates/episode.md","content":"# EP-TEMPLATE: Short task description\n\n## Intent\nWhat you were trying to accomplish\n\n## Context\n- domain: relevant area (e.g., note-api, deployment, config)\n- tools: what tools/commands used\n- precondition: what needed to be true before starting\n\n## Experience\n\n### ✅ Success Pattern\n1. First step\n2. Second step\n3. 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