Skill: zcyynl/memory-distiller (version 1.0.1)

--- name: memory-distiller version: 1.0.0 description: "OpenClaw's subconscious. Automatically distills conversation insights, corrections, and preferences into durable memory. The agent that learns from every session — so you never have to repeat yourself." author: zcyynl --- # memory-distiller 🧠 > **The subconscious of your OpenClaw agent.** Automatically distills fleeting conversation moments into permanent wisdom. Most agents wake up blank. memory-distiller changes that — it automatically identifies what's worth remembering during each conversation, writes it to persistent memory files, and creates a true learning loop. Your agent gets smarter every session. --- ## Why You Need It OpenClaw natively has `MEMORY.md` and `memory/YYYY-MM-DD.md`, but updating them requires manual effort. The problem: **You correct the agent, it says "got it", and makes the same mistake next session.** memory-distiller closes this loop: - Automatically scans conversations for "worth remembering" signals - Applies a quality gate to filter out one-time, temporary information - Writes structured entries to memory files automatically --- ## How It Fits With proactive-agent | Skill | Role | Responsibility | |-------|------|----------------| | **proactive-agent** | Butler | Real-time detail capture (WAL), proactive behaviors, Heartbeat | | **memory-distiller** | Historian | Post-conversation reflection, distilling lessons into long-term memory | One line: **proactive-agent owns the present. memory-distiller owns the future.** --- ## Trigger Conditions ### Auto-Triggers (scan every user message for these signals) | Type | Signal Words | Example | |------|-------------|---------| | 🔴 **Correction** | "wrong", "not right", "no, I meant", "actually", "stop doing" | "No, that command is wrong" | | 💚 **Preference** | "I prefer", "always use", "don't use", "from now on", "by default" | "Always send reports as attachments" | | 💡 **Insight** | "the issue was", "turns out", "the key is", "got it", "solved" | "Turns out Feishu doesn't render Markdown" | | 📌 **Explicit** | "remember this", "save this", "note that", "write this down" | "Remember this config" | ### Manual Trigger When the user says any of the following, immediately distill the session: ``` "remember this" / "save this" / "note that" "write down what we just learned" / "distill this session" ``` --- ## Quality Gate (All 4 Must Pass) Before writing anything, check these 4 gates: 1. **Durability** — Will this still be valuable in 24 hours? (Skip one-time context) 2. **Generality** — Is this a reusable rule, or a one-off special case? (Prefer rules) 3. **Novelty** — Does `MEMORY.md` already contain this? (Avoid duplicates; update if stale) 4. **Actionability** — Can this guide future behavior? (Skip vague impressions; only concrete rules) **Don't record (examples):** - "Today we researched LangChain" → one-time, no guidance value - "User is in Shanghai" → already in USER.md, duplicate **Do record (examples):** - "`clawhub install` only accepts slugs, not GitHub URLs" → actionable rule - "Feishu chat does NOT render Markdown — long reports must be sent as attachments or doc links" → prevents repeated mistakes --- ## Memory Write Format ### Where to Write | Content Type | Target File | |-------------|-------------| | Today's new discoveries, lessons | `memory/YYYY-MM-DD.md` | | Important rules, persistent preferences | `MEMORY.md` (relevant section) | | User personal info / preferences | `USER.md` | ### Entry Format ```markdown ### 🧠 Auto-Learned [YYYY-MM-DD HH:MM] - **Type:** Correction / Preference / Insight / Explicit - **Trigger:** One sentence explaining what triggered this - **Rule:** Specific, actionable rule that can directly guide future behavior ``` **Example:** ```markdown ### 🧠 Auto-Learned [2026-03-02 00:30] - **Type:** Correction - **Trigger:** User corrected the install command format - **Rule:** `clawhub install` only accepts slugs (e.g. claw-multi-agent), not GitHub URLs ``` --- ## Execution Flow ``` User message arrives ↓ Scan for trigger signals (Correction / Preference / Insight / Explicit) ↓ Signal detected? ├─ No → Reply normally, no action └─ Yes → Apply quality gate ↓ All 4 gates pass? ├─ No → Discard, reply normally └─ Yes → Distill into structured memory entry ↓ Write to target file ↓ Reply normally (silent unless explicitly triggered) ``` --- ## Behavioral Rules ### When to Notify the User - **Auto-triggered** → Write silently, do NOT say "I've noted that" — don't interrupt the flow - **Explicitly triggered** → Confirm with one line: "✅ Noted." + brief summary of what was recorded ### When NOT to Record - User says "suppose...", "hypothetically...", "for example..." → hypothetical, skip - System errors, network timeouts → environment issues, not learnable rules - Content already fully documented in `MEMORY.md` → don't duplicate; update if stale ### Privacy Filter **Never** write the following to any memory file, even if the user asks: - Passwords, tokens, API keys - Personal identification info (ID numbers, bank accounts, etc.) - Sensitive information about third parties --- ## Installation ```bash npx clawhub@latest install memory-distiller ``` Zero configuration. Restart your OpenClaw session after installation. --- ## Recommended Pairing | Skill | Purpose | |-------|---------| | **proactive-agent** | Real-time WAL protocol + proactive behaviors | | **memory-distiller** | Post-conversation automatic experience distillation (this skill) | Together: never lose present details, never repeat past mistakes. --- ## Design Philosophy Inspired by [Claudeception](https://github.com/disler/claudeception) (a Claude Code self-learning plugin), redesigned from scratch for OpenClaw's architecture and memory model. Core belief: > **An agent should never be tripped up by the same problem twice.** Every correction is a learning opportunity. memory-distiller ensures none of them go to waste. Academic foundation: Voyager (2023), CASCADE (2024), SEAgent (2025), Reflexion (2023) — all point to the same conclusion: agents that persist and reflect on their learning dramatically outperform those that start fresh every time. --- *Designed with 🐝 [claw-multi-agent](https://github.com/zcyynl/claw-multi-agent) — 3 parallel agents, 28s*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)