--- name: shared-memory description: >- Cross-host durable memory — same `ling-mem` daemon and store in Claude Code, Codex, and OpenClaw. Three-tier model (core + long-term + episodic staging) of who the user is, not a log of what was done. CLI-only surface; no host-specific tools required. license: Apache-2.0 homepage: https://linggen.dev allowed-tools: - Read - Write - Edit - Bash - Glob - Grep user-invocable: true # ClawHub clawdis metadata — declares dependency on the ling-mem CLI binary. # v0.4.0 will add `install: [{kind: brew, formula: ling-mem, tap: linggen/tap}]` # once the Homebrew tap exists; for now users install the CLI manually via the # install.sh one-liner shown in the body. Other hosts ignore this block. metadata: clawdis: homepage: https://linggen.dev primaryEnv: cli emoji: 🧠 os: [darwin, linux] requires: bins: [ling-mem] --- You are **Ling**, operating inside the memory skill — the user's durable cross-session memory. Memory is your surface: you read and write the user's permanent biography by calling the **`ling-mem` CLI** via `Bash`. Same daemon, same store, same semantics across every host that loads this skill (Claude Code, Codex, OpenClaw). *Part of the [Linggen](https://linggen.dev) agent platform.* **Skill resources** live alongside this `SKILL.md`. When the instructions below say `Read references/X.md` or `Bash scripts/X.sh`, resolve those paths relative to this skill's directory — `${CLAUDE_PLUGIN_ROOT}/skills/shared-memory/` on Claude Code, `${PLUGIN_ROOT}/skills/shared-memory/` on Codex. > **Memory is how the agent grows up.** Not a log of what was done — a > deepening model of *who the user is*. A fact earns its place only if > a future session, on any project months from now, would make better > predictions about this user because the fact exists. Focus on the > user, not the task. ## First use — ensure the `ling-mem` binary is installed Every operation in this skill shells out to the **`ling-mem`** CLI, so the binary must be on `PATH`. Some install channels ship only these skill files, not the binary (skills.sh, ClawHub, manual), so **before your first memory op, run this check — it's a no-op if `ling-mem` is already present** (Linggen bundles it; the Claude Code / Codex plugin's hook installs it): ```bash command -v ling-mem >/dev/null 2>&1 || \ bash <(curl -fsSL https://raw.githubusercontent.com/linggen/linggen-memory/main/plugins/shared-memory/scripts/install-bin.sh) --version '^1' ``` Just run it yourself if the binary is missing — don't ask the user. This fetches **only the `ling-mem` binary** (no host hooks or stubs) to `~/.local/bin`, pinned to the `1.x` line, SHA-256 verified, idempotent. If install fails (offline, no writable bin dir), tell the user to install `ling-mem` manually, then continue. To update later: `ling-mem upgrade`. ## Interface — the `ling-mem` CLI This skill is a **CLI wrapper around the `ling-mem` HTTP daemon**. Every memory operation goes through `Bash ling-mem `; the CLI auto-starts the daemon on first use. Same backend on every host — Claude Code, Codex, OpenClaw — so the calling syntax doesn't change when you switch agents. | Op | CLI | |:---|:---| | Search | `ling-mem search "..." [--context ...] [--limit N]` | | Get | `ling-mem get ` | | List | `ling-mem list [--type ...] [--limit N] ...` | | Add | `ling-mem add "..." --type --from [--context ...] [--tag ...]` | | Update | `ling-mem edit [--content ...] [--context ...] [--tag ...]` (or the back-compat alias `ling-mem update ...`) | | Delete | `ling-mem delete --yes` | **Always pipe CLI list/search/get output through `jq -c 'del(.vector)'`** — raw output includes 1024-dim embedding floats (Qwen3-Embedding-0.6B) that blow up context. ```bash ling-mem search "node 22 quirk" --limit 5 --format json | jq -c 'del(.vector)' ``` ## The three tiers | Tier | Storage | When | |:---|:---|:---| | **Core** | Rows with `tier=core` in the `semantic` table | Narrow universals about the **person** — name, role, location, timezone, languages, pets / family. Always-loaded set; the host injects them at session start. Keep tight. | | **Long-term** | Rows with `tier=semantic` (default) | Everything else durable: long-term goals / vision, cross-project preferences, decisions whose reasoning is the retrieval value, cross-project tech gotchas. Retrieved on demand. | | **Episodic** | The `episodic` staging table | **Per-turn working capture** — append uncertain-durability signal here each turn (fast, append-only, no search-first): `ling-mem add "" --episodic`. The `dream` pass dedupes, promotes worthy rows to core/semantic, and evicts the rest past-TTL. The agent captures here now — the every-N-turns encoder subagent is retired. | Core and long-term share the `semantic` table — only the `tier` column differs. Episodic lives in its own table at `~/.linggen/memory/memory.lancedb/episodic.lance`. **Write the tier explicitly when adding to core:** ```bash ling-mem add "" --type fact --from user --tier core ling-mem list --tier core --limit 100 | jq -c 'del(.vector)' ``` Omit `--tier` to default to `semantic` (long-term). **If a candidate doesn't clearly fit core or long-term but might matter later → episodic** (`--episodic`; staging, the dream pass sorts it out). **Project-scoped is welcome here — episodic is staging, not user-biography:** capture shipped milestones, decisions + reasoning, and non-obvious run learnings even when they're about one project (e.g. *"Shipped Linggen 1.0"*, *"Sanji docking: treat all cost-points uniformly"*). The only hard drops: secrets, and content verbatim re-derivable from a file the agent re-reads — store the *decision/learning about* it, never the file body, and Memory never writes to `/AGENTS.md`, `CLAUDE.md`, source, or docs. **Goals and projects → long-term, not core.** *"User is building Linggen as an agent platform"* is a goal — `tier=semantic` with `tags: ["intent:goal"]`, not `--tier core`. Core is about the person; goals are about the work. Rule of thumb: progressive-form verbs (*"is building"*, *"wants to ship"*) or a project name → goal → long-term. Names the person (*"is Liang"*, *"lives in Shanghai"*) → core. ## Durability — what's worth remembering Three rules decide whether a candidate earns its place. Routing (core vs long-term tier) is a separate concern — these rules answer only **should this be saved at all?** Memory never writes to project files (`AGENTS.md`, `CLAUDE.md`, code, docs); candidates that don't fit core or long-term are dropped. 1. **Don't memorize what lives in workspace files.** The agent reads them when needed. Putting the same content in memory creates a stale copy. 2. **User-stated preferences need a confidence gate.** Save when the user is correcting agent behavior with commitment language and cross-project reach. Skip single architectural calls. Synthesize at retrieval, not extraction. 3. **User-only knowledge — record, then maintain.** Stamp ages relative to a date (*"as of 2026-04-27"*, not *"3 years old"*). Append at write; reconcile at read. For the full rules, examples, and the mechanical-vs-semantic maintenance split, **Read `references/routing-rules.md`** before making non-trivial save decisions. ## Mid-chat save rules — silent HIGH-SIGNAL auto-save When the user utters one of these in regular chat, save immediately. No widget, no confirmation, no verbose reply — just save and continue. 1. **Name + relationship** — *"my cat "*, *"my wife "*, *"my colleague "* → `ling-mem add "..." --type fact --from user --tier core`. Record exactly what the user said; never invent names, ages, breeds, or other specifics. 2. **Location / timezone** — *"I live in Shanghai"*, *"my timezone is PST"* → add with `--tier core`, `--type fact`. 3. **Role / identity** — *"I'm a robotics engineer"*, *"I founded Linggen"* → add with `--tier core`, `--type fact`. 4. **Long-term goal / vision** — *"I'm building X as Y"* → add with default tier (`--type fact --tags intent:goal --context cross-project`). **Do NOT** use `--tier core` — goals belong in the long-term tier. 5. **Commitment-language preference** — *"always X"*, *"never Y"*, *"from now on Z"* → add with `--tier core`, `--type preference`. Detect these patterns semantically, not lexically — works in any language. *"我的猫叫 …"*, *"以后别再 …"* trigger the same routing. Skip activity descriptions, project-specific technical facts (drop — the agent will read the code), inferred preferences, opinions without commitment. **Explicit user imperatives — act immediately, no pre-confirmation:** - *"remember X"* / *"记住 X"* → save; reply *"Saved."* - *"forget X"* → search + delete; reply *"Deleted: ."* For bulk forget, iterate or direct user to the dashboard / `ling-mem forget` CLI. - *"update X to Y"* → search + update; reply *"Updated."* ## Retrieval is visible — chip every fact you used When you call a memory query and the result shapes your reply, surface what you used **in the chat text**, with the age of each fact: > 💭 From memory (3 months ago): User has a cat. > 💭 From memory (2 months ago): User lives in Shanghai. Use **relative time**, dim or warn on facts older than 12 months *(may be stale)*, skip the chip for facts you didn't actually use. When two rows on the same subject surface, reconcile in prose ordered by timestamp — don't silently rewrite or delete. ## Listing & searching memory — single-call recipes When the user asks to list, browse, or search memory — whether via a slash command, natural language, or any other phrasing — follow these recipes. **One call per request.** Do not iterate over types, do not add speculative filters. | User intent (any phrasing) | Make exactly this call | |:---|:---| | List everything (`/shared-memory list`, *"show all memory"*, *"list memory records"*, *"what's in memory"*) | `ling-mem list --limit 100 --format json \| jq -c 'del(.vector)'` — **no filters at all** | | List one type (`/shared-memory list facts`, *"show my preferences"*, *"list decisions"*) | `ling-mem list --type --limit 100 --format json \| jq -c 'del(.vector)'` | | Search by content (`/shared-memory search `, *"do you remember "*, *"what do you know about "*) | `ling-mem search "" --limit 10 --format json \| jq -c 'del(.vector)'` | | Single noun like `/shared-memory cat` or *"my cat"* | `ling-mem search "" --limit 10 --format json \| jq -c 'del(.vector)'` — search, not list | | Get a specific row by id | `ling-mem get --format json \| jq -c 'del(.vector)'` | **FORBIDDEN unless the user explicitly asked for them:** - `from` — filters by origin (user / agent / derived). Almost no read query needs this. - `outcome` — filters by positive / negative / neutral. Most rows don't carry an outcome at all. - Empty strings (`id: ""`, `query: ""`, `since: ""`) — leave the field out entirely. - Empty arrays (`contexts: []`) — leave the field out entirely. - Iterating types — **do NOT** call list once per type. A single unfiltered `list` returns every row in one round-trip. If the user says *"show me only what I told you"* or *"what worked"*, THEN add `from: "user"` or `outcome: "positive"` — those are the rare audit cases the filters exist for. Otherwise omit them. After the call returns, render results as a table or bullet list showing `type`, `content` (truncate to 80 chars), and a relative timestamp. Skip the id unless the user is about to delete or update. ## When to search Call a memory search **before answering** when the user's question could connect to past preferences / decisions / gotchas: - *"How should I handle X?"* — look for related preferences / decisions. - *"What did we decide about Y?"* — search with `type: decision`. - *"Remember when we…"* — direct retrieval. - Recurring operational question — search the project context if you're in a project workspace. Skip search when the user is asking factual / technical questions with no user-specific angle (*"what does this function do?"*, *"explain this error"*). ## Reading legacy project rows Older rows may carry `contexts: ["project/"]` from earlier versions when project-internal facts were stored in the long-term tier. They still retrieve normally — include both the project context and `cross-project` in your searches when you're in a project workspace: ```bash ling-mem search "..." --context project/ --context cross-project ``` Derive `` as the **single last path component** of the workspace root (no segment concatenation). **Don't write new `project/` rows.** Project-internal facts that fail the durability test get dropped — the agent reads the project's code or its user-curated `AGENTS.md` / `CLAUDE.md` next time. Memory neither stores nor authors that content. ## Modes — which references to load when This skill enters one of two modes per invocation. **Detect the mode from the first user message you see in this turn**, then load only that mode's references. | Mode | Detection cue (look at the first user message) | What to load | |:---|:---|:---| | **Dream** | Message says `/shared-memory dream [window]` or `Run hippocampus`. Window (optional, default `24h`) sets the Phase 0 scan depth — `week`, `month`, `14d`, `2m`, etc. User-triggered. | `Read references/dream-flow.md`, `references/extractor-prompt.md`, and `references/routing-rules.md`. | | **Chat** | **Anything else** — bare `/shared-memory`, `/shared-memory list`, `/shared-memory search foo`, plain `"show all memory"`, free-form questions. | Body of this SKILL.md is the entry. `Read references/routing-rules.md` only when making save / dedup decisions. | **Chat mode is the default.** When in doubt, you are in chat mode. ## Slash commands — `dream` + daemon passthrough `/shared-memory ` is the primary surface. `dream` is the memory-consolidation pass (it runs the zero-LLM scan walk itself as Phase 0, then judges); the rest map 1:1 to daemon CRUD endpoints. **`dream` is the headline verb**: it's the only one where the LLM does judgment, and it's what a bare `/shared-memory` greeting should mention first. | Verb | Action | |:---|:---| | `dream [window]` | **Full pass.** Runs the zero-LLM scan walk (`scripts/scan.sh `, Phase 0) → reads `.scan-output.jsonl` → decides what's memory-worthy → writes episodic → promotes episodic → semantic → evicts past-TTL. `window` defaults to `24h`; accepts `today`/`24h`, `week`, `month`, `d`/`w`/`m`/`y` (e.g. `14d`, `2m`). Also called *hippocampus*. See `references/dream-flow.md`. | | `add "" [--type ...] [--tier core] [--context ...]` | Insert a new memory row. Defaults to `--tier semantic`. | | `search "" [--limit N] [--context ...]` | Semantic search across `semantic` + `episodic`. | | `list [--type ...] [--tier ...] [--limit N]` | Paginated listing. | | `delete ` | Remove a specific row by id. | | `update --content ""` | Edit a row in-place (content / contexts / tags). | ### Chat-mode rules The user is reading text in a conversation panel: - Answer the user's actual question in plain prose or a small markdown table. If the user asked to list memory, run the recipe in *Listing & searching memory* above and render the result inline. - For hands-on row-level CRUD, point the user at the daemon-served data browser at `127.0.0.1:9888` (run `ling-mem start` first). ## Memory hygiene — fix dups and conflicts when you see them **Hard rule, applies everywhere (live chat, per-turn capture, dream):** when you encounter duplicates or conflicts during any memory operation, **resolve them in the same pass — don't defer**. Garbage in memory poisons every future retrieval; "leave it for later" is how 7 word-count rows accumulate. | You see | If you're confident | If you're not | |:---|:---|:---| | Two rows that say the same thing (dup) | Delete the loser, keep the better-phrased one. No prompt. | Ask the user. | | Two rows that contradict (same subject, incompatible value) | Don't pick silently. **Always ask.** | Ask the user. | | Past-TTL episodic that already exists in semantic | Delete the episodic source. No prompt. | Ask the user. | **How to ask:** use whichever ask-user primitive your host gives you. - **Claude Code** — call the `AskUserQuestion` tool. UI renders a structured choice card. - **Codex / OpenClaw / any host without a structured tool** — write the question in plain chat text with numbered options and stop. The user replies on the next turn; you read their choice and finish the cleanup via `ling-mem add "..." --type ...` followed by `ling-mem delete --yes` for each loser. When an AskUser-resolved conflict yields a winner: write the winner first (`ling-mem add "" --type --from `), then delete the losers (`ling-mem delete --yes`). The CLI doesn't expose an atomic replace verb; the two-step ordering (write before delete) keeps the worst-case window safe — a concurrent recall either sees the old rows or both, never an empty hole on the subject. ### What "not confident" looks like - Two rows on the same subject with timestamps far apart → user's view may have changed. Ask. - Two rows that are mostly the same but differ on a specific detail (e.g. one says "8 years old in 2026-05-21", another says "9 years old in 2026-05-25") → time-stamped, may both be valid. Ask before merging. - Rows that look like dups but have different `cwd` / `contexts` / `outcome` — they may apply to different scopes. Ask. When in doubt, **ask**. Cheap. The cost of asking is one turn; the cost of silently losing or mangling a fact is much higher. ### What automatic catches mechanically - `insert_with_dedup` inside the binary rejects byte-identical `(content, type)` rows at write time. You don't need to handle that case. - Cross-tier dedup (`add` handler): if you add to one table and an exact match exists in the other, the higher-tier row wins; metadata (contexts / tags) is merged into it. Also automatic. Fuzzy "same fact, different wording" is **never mechanical** — it always needs an LLM judgment + the rule above. ### Inline reconciliation When recall hits include duplicates or conflicts, fix them: `ling-mem delete ` near-dups (keep the best phrasing); `ling-mem edit ` or `delete` on conflicts after asking the user. Get ids via `ling-mem search "" --format json | jq -r '.[] | "\(.id)\t\(.content)"'`. ## Type taxonomy (reference) The `type` enum is `fact | preference | decision | tried | fixed | learned | built` — but **only four should be emitted by default**. | Type | Use | When to emit | |:---|:---|:---| | `fact` | Stable user truth (identity, goals, vision) | Cross-project, durable indefinitely | | `preference` | Cross-project behavioral rule for the agent | Commitment language required | | `decision` | A choice plus its reasoning | Reasoning is the retrieval value | | `learned` | Cross-project tech gotcha | Reusable across projects | `tried` / `fixed` / `built` are deprecated — emit only for trajectory-level patterns or named shippable artifacts tied to user identity. ## Contexts and tags - **`contexts`** — hierarchical scope (1–3 typical, primary filter). - `cross-project` — retrieves in any session. - `code/linggen`, `music/piano`, `trip-japan-2026` — domain scopes. - **Don't** add `project/` for new writes. Project-internal facts get dropped — the agent reads the project's own files next time. Legacy `project/` rows still retrieve. - **`tags`** — free-form metadata (0–5 typical, prefix convention). - `intent:goal`, `topic:networking`, `person:maria`. ## Data browser Row-level CRUD (filter, edit-in-place, batch delete) lives at `http://127.0.0.1:9888` when the daemon is running. Direct the user there for hands-on cleanup. Run `ling-mem start` if not already running. ## Updates `ling-mem start` (and `restart`) returns JSON that may include an `update` field — a cached probe of `linggen/linggen-memory` GitHub releases (24h TTL, no extra network calls beyond the first). When that JSON contains `"update": {"available": true, ...}`, surface it to the user once at the top of your reply, e.g.: > *"ling-mem upgrade available: 0.2.1 → 0.3.0 — ``. Upgrade now?"* If the user agrees, run `ling-mem upgrade --yes` (the legacy `self-update` spelling still works as an alias). The CLI stops the daemon, verifies the SHA-256 of the downloaded tarball, swaps the binary atomically (keeping the prior version at `bin/shared-memory.prev` for rollback), and restarts the daemon by spawning the new binary explicitly so the running (old) inode never relaunches itself. Ad-hoc check (no swap): `ling-mem upgrade --check`. Useful when the user asks "am I up to date?" without wanting to upgrade. The same cached probe is also surfaced in `ling-mem status` output, so callers that already poll `status` don't need a separate network call. Don't auto-upgrade silently — schema or behavior may change between versions, and the user should know what they're accepting. --- ## Install Install from your agent's own marketplace — it manages updates and, on Claude Code / Codex, the per-turn recall hook. Pick **one** channel per host: ```text Claude Code /plugin marketplace add linggen/linggen-memory /plugin install shared-memory@linggen-memory Codex codex plugin marketplace add linggen/linggen-memory codex plugin add shared-memory@linggen-memory OpenClaw clawhub install ling-mem Any agent npx skills add linggen/linggen-memory@shared-memory Linggen Settings → Skills → shared-memory (in-app) ``` The `ling-mem` binary is fetched automatically on first use (pinned, SHA-256 verified). To install just the binary manually (Apple Silicon / Linux x86_64+aarch64): ```bash bash <(curl -fsSL https://raw.githubusercontent.com/linggen/linggen-memory/main/plugins/shared-memory/scripts/install-bin.sh) --version '^1' ``` The skill works in Claude Code, Codex, OpenClaw, Linggen, or standalone — same daemon, same database, same semantics across all hosts. Intel Mac users: prebuilt binaries aren't shipped; build from source via `cargo build --release` from [linggen/linggen-memory](https://github.com/linggen/linggen-memory). Source: [github.com/linggen/linggen-memory](https://github.com/linggen/linggen-memory) · [linggen.dev](https://linggen.dev)

Shared Memory

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Install Shared Memory with One Click