lycheemem
Forceful operating rules for using LycheeMem as the primary structured long-term memory path inside OpenClaw.
npx skills add LycheeMem/LycheeMem --skill lycheemem --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LycheeMem ## Mission Use LycheeMem as the default structured long-term memory layer. When the answer depends on prior conversations, historical facts, entity relationships, project background, preferences, procedures, or timeline reconstruction, prefer LycheeMem first. Do not wait for the user to explicitly say `lycheemem`, `memory`, or `smart_search`. ## Default Priority For long-horizon recall, use this order: 1. `lychee_memory_smart_search` 2. answer using the returned `background_context` or retrieval payload 3. `lychee_memory_consolidate` when important durable knowledge was added or clarified Treat `lychee_memory_smart_search` as the primary recall tool. Treat `lychee_memory_search` as a debugging tool, not the normal path. ## When Smart Search Is Expected Call `lychee_memory_smart_search` by default before answering if any of the following is true: - the user asks about earlier dialogue, prior sessions, or historical context - the user asks who/when/where/why/how about a person, relationship, event, preference, or decision that was not stated in the current message - the answer requires reconstructing a timeline or resolving relative dates such as "昨天", "上周", "之前", "上次" -
- Mission
- Default Priority
- When Smart Search Is Expected
- When Not To Skip Smart Search
- How To Use The Result
- Consolidation Rules
- Append Turn Rules
- Benchmark And Evaluation Guidance
- Normal Operating Pattern
- Debugging Path
What does the lycheemem skill do?
Forceful operating rules for using LycheeMem as the primary structured long-term memory path inside OpenClaw.
How do I install it?
Run `npx skills add LycheeMem/LycheeMem --skill lycheemem --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LycheeMem/LycheeMem, a repository with 1,164 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.