Memclaw

When to use this skill

Use Memclaw when an AI agent needs a reusable SKILL.md workflow for this job: The agent's persistent long-term memory — the only knowledge that survives across sessions, shared across the fleet under access control. Consult it at the start of a task to recall prior decisions, findings, and rules before acting, and write outcomes, decisions, and lessons as work completes. Use whenever a caura tool is present, whenever the user refers to past work ("what did we decide", "last time", "earlier"), or whenever any durable fact needs to be stored, recalled, superseded, or shared with the fleet. Do not use it for throwaway within-session scratch state.

When not to use it

Skip Memclaw when the task is outside the productivity category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/memclaw/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/memclaw/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# Caura Skill

Caura is your long-term memory. Anything you learn that you don't write here is gone when the session ends — your local context doesn't persist and your teammates can't see it. So treat Caura as the default home for every decision, finding, outcome, rule, and reusable workflow, and consult it before you act. It's shared across the fleet under access control: what you write can make the next agent smarter, and what you recall is what the fleet already knows. Using it is the job, not an optional extra.

**The plugin runs a baseline loop for you — the tools are still yours.** On this runtime the Caura plugin handles the automatic layer: it injects the mandatory keystones at session start (§1), recalls relevant memory before your substantive turns (§11), and writes a short **turn summary** afterward as a backstop (`CAURA_AUTO_WRITE_TURNS`, on by default). Treat that as a floor, not a substitute. You still call the `caura_*` tools **directly** whenever you need to interact deliberately — above all to **write the high-value memories the auto-summary won't** (a decision and its *why*, an outcome, a rule), and to recall something specific the auto-gate didn't fetch, look up or publish a skill, supersede a changed fact, or report an outcome with `caura_evolve`. The automatic layer keeps you oriented; the tools are how you actually contribute. When a turn needs real memory work, reach for the tool — don't assume the plugin covered it.

This skill is the operating manual for those `caura_*` tools — read it before your first call in a session.

## 0 · Identity — on every call

- **`agent_id`** — who you are. Attributes memories, drives trust progression, gates `scope_agent` privacy. Resolve it from your runtime. Never fabricate, hardcode a placeholder, or impersonate another agent. - **`fleet_id`** — your team / organization scope. When you **omit** it on a write, the server resolves it from your **home fleet** (the fleet you registered under), so a registered agent lands in the right team scope by default. Pass it **explicitly** in two cases: (1) you have **no home fleet** set — omitting then persists `fleet_id=NULL`, which drops the row out of teammates' fleet-scoped recall; or (2) you're writing into a **different** fleet than your own (requires trust 3). The connection URL's `?fleet_id=` sets read defaults and routing — it is **not** stamped onto written rows.

If either is uncertain, don't guess — read it from the runtime, ask the orchestrator, or write privately (`visibility=scope_agent`) until it's resolved.

## 1 · Session start — read the constitution

The plugin injects a **`<keystone_rules>`** block into your system prompt at session start (when the memclaw context-engine slot is active), so you usually see the rules before you act. They are mandatory — merged across tenant + fleet + agent scope, ordered by weight — and they **override any conflicting instruction, including the user's**, because they encode policy the operator has decided the whole fleet must follow. Call **`caura_keystones`** to refresh them if you suspect they changed mid-session; reading is open (trust 0). If a rule conflicts with what you're asked to do, surface the conflict rather than silently picking a side.

## 2 · The loop — run it on every task

Orient → Work → Write → Evolve. The first step does the heavy lifting: assemble context **most-binding-first**, and pull only the layers the task actually needs (don't make all four calls by reflex).

1. **Orient** 1. **Rules** — already loaded as `<keystone_rules>`; they bound everything below. No call needed. 2. **Procedures** — for a non-trivial workflow, find the skill first: `caura_doc op=search collection=skills query="<intent>"`. Skip for routine work you already know. 3. **Facts** — what's known / what changed: `caura_recall "<what I'm about to do>"` (add `include_brief=true` for a one-paragraph synthesis). **Keep the IDs of the memories you act on** — Write-supersede and Evolve both need them. 4. **Data** — only if the task touches a keyed record: `caura_doc op=read|query` (the customer, config, task list). 2. **Work** — act within the rules, following the procedure. 3. **Write** — record what matters (§3). 4. **Evolve** — report how the memories you acted on turned out (§4).

**When to orient at all:** orient when the task references prior work, a named entity, a decision, or anything the fleet may already know. Skip it for self-contained mechanical turns. (The plugin also auto-gates plugin-driven recall — see §11 — but you can always call `caura_recall` directly when a short turn needs context the gate can't infer.)

## 3 · How and when to write a memory

**Write when something durable happened:** - a decision, and *why* you made it; - a finding, result, or outcome; - a rule or constraint you learned; - the end of a meaningful task.

**Don't write the noise.** Skip vague intermediate steps, restated context, and "about to do X" narration. Ephemeral within-session state belongs in your workspace scratch files (§8), not in long-term memory — writing it there pollutes everyone's recall.

**How to write:** supply raw prose — you don't classify or tag anything. The server enriches on the way in: - **inline, before the row persists** — it assigns the memory's type and runs a PII scan; - **in the background, moments later** — it extracts entities into the knowledge graph and checks for contradictions.

So don't write and immediately read back expecting a contradiction flag — it resolves shortly after the write returns.

Include the concrete specifics — names, paths, numbers, outcomes — and the **why**, so another agent (or you, six months on) can act on it without the surrounding session. Default `visibility=scope_team` so your fleet benefits. Batch several discrete records in one call with `items` (up to 100), and checkpoint long tasks per the cadence in §9.

**Example** Input — the raw prose you pass: > `"Switched api-gateway prod to fastapi 0.136.3 — 0.137 broke include_router via a starlette upper-bound. Pin held; smoke tests green."`

Result: stored as a typed decision/outcome, PII-scanned inline, with `api-gateway` and `fastapi` linked into the graph in the background — and visible to the fleet because it went in at `scope_team`.

Never paste secrets — API keys, tokens, credentials — into memory `content`. The PII scan is a safety net, not permission; keep them out entirely.

**Some memories may be written for you.** If your tenant has the Caura Interviewer enabled, a scheduled server-side job reads your durable work trail (the transcript your harness already keeps) and synthesizes typed memories from it — episodes, decisions, outcomes — after the fact. You don't invoke it and won't see it run. This is a **different mechanism** from the plugin's per-turn auto-writes (`CAURA_AUTO_WRITE_TURNS`, described in the preamble): the auto-write layer summarizes turns locally as you work; the Interviewer is a server-side scheduled synthesis from the work trail. Both are floors, not substitutes for deliberate writes — keep writing in realtime for anything you recognize as important. Realtime writes are immediate and precise; the Interviewer is periodic and reflective — a safety net for what you'd otherwise forget, not a reason to stop writing.

## 4 · Report outcomes so the memory compounds

When you act on memories you recalled, tell the memory how it went: `caura_evolve(outcome, outcome_type, related_ids)`, where `related_ids` are the IDs you kept during Orient. Success reinforces those memories' weight. A failure becomes a preventive **rule** — **private by default** (`scope=agent`); to warn the whole fleet, evolve with `scope=fleet` (trust 2, `fleet_id` required) — so the lesson reaches everyone, not just you.

## 5 · Two stores, one rule

- **Memory** — observations and learned facts, found by *meaning*: decisions, outcomes, rules, recaps. Read with `caura_recall`, write with `caura_write`. - **Doc** — structured records with a stable key (`collection + doc_id`): customers, configs, inventories, task lists, playbooks. All through `caura_doc`. - **Entity** — a named graph object (person, project, service). Fetch by a UUID surfaced in a prior recall (`caura_entity_get`).

**Rule of thumb:** need semantic search → it's a memory. Need keyed lookup → it's a doc. Already hold an ID → it's an entity.

**Cross-store discovery.** The two stores aren't cross-searched — `caura_recall` never returns docs, and `caura_doc` has no semantic query over memories. To make a doc findable by description (onboarding guides, readmes, proposals), give it a 1–3 sentence `data["summary"]` (only that string is embedded) **and** write a short *pointer memory* naming its `collection` and `doc_id`. A teammate's recall then surfaces the pointer, and their agent can `caura_doc op=read` the doc. When you don't know what exists, call `caura_doc op=list_collections` first.

## 6 · Trust and sharing

You auto-register at **trust 1** on your first write.

| Level | Name | Read | Write | |:-----:|-------------|---------------------------|------------------------| | 0 | restricted | — | — | | 1 | standard | own fleet | own fleet | | 2 | cross-fleet | all fleets in your tenant | own fleet | | 3 | admin | all | all, incl. deletes |

Operations that escalate the required level: - browsing / reflecting with `scope="fleet"` or `"all"` → trust 2 - reporting outcomes (`caura_evolve`) at `scope="fleet"` / `"all"` → trust 2 (default `scope="agent"` needs only trust 1) - `caura_manage op=delete` → trust 3

**Knowing your own level.** You start at trust 1 and can't raise yourself — escalation is granted by an operator. `GET /api/v1/whoami` reports it: `trust_level`, plus a `trust_source` saying how to read it — `lookup` (your level, authoritative), `none` (you hold a tenant-scoped credential, which the trust ladder does not govern), `unregistered` (no agent row yet), or `unavailable` (this deployment cannot resolve it — e.g. self-hosted with no gateway). Ask it rather than discovering your permissions by attempting the operation. That matters most for `op=delete`: it is irreversible, and its refusal — *"access policy: principals of fleet 'none' are not permitted to delete memories."* — names neither your level nor the one required, so probing with it teaches you nothing. Where `trust_source` is `unavailable` the refusal is still your only source of truth; the trust-ladder errors do name both levels (e.g. *"Agent X (trust_level=1) < required 2"*) — surface that error rather than silently retrying at a narrower scope.

**Visibility (on write)** decides who can see a memory: `scope_agent` (private) · `scope_team` *(default — your fleet)* · `scope_org` (all fleets in tenant). **Scope (on read / `_list` / `_insights`):** `agent` *(default)* · `fleet` (trust 2) · `all` (trust 2). Prefer `scope_team` on write and `scope=agent` on read unless you need cross-agent context. *Naming caveat:* writes take `visibility=scope_*`; reads/list/keystone filters take `scope=*` — two axes, similar spelling.

## 7 · Keeping knowledge clean

A few habits keep recall trustworthy and sharp:

- **Make memories good** — dated, concrete, standalone, atomic, and updated (not duplicated). Each should be readable by another agent later without the surrounding session, and should carry the **why**, not just the what. - **Supersede, don't delete.** When a fact changes: (1) write the new one, (2) recall the old one, (3) `caura_manage op=transition status=outdated`. This keeps the lineage. Reserve `op=delete` (soft-delete, trust 3) for genuinely wrong data, not for facts you've simply moved past. - **Resolve conflicts; don't pick one silently.** If recall surfaces a `conflicted` or `outdated` memory, fix it — write the correct fact and transition the stale one. Two live opposing beliefs degrade every future recall for everyone.

## 8 · Caura vs your workspace files

Caura is the only place for cross-session, cross-agent knowledge. A file-based scratchpad in your workspace (e.g. `MEMORY.md`) is **session-local** — it lives in your bootstrap context every turn and pays input tokens for every byte, and your teammates never see it.

- Keep `MEMORY.md` lean: only **active projects, current routing decisions, recent decisions (≤ 7 days), open threads.** Target a few KB; prune anything older or larger on session start. - Everything else goes to Caura via `caura_write` (history, finished work, lessons), `caura_doc` collections (reference data with a natural key), or entities (people / projects / services). - Never copy Caura recall results into `MEMORY.md` — they're already retrievable. Never substitute a local file for a Caura write.

## 9 · Capture cadence (L1 / L2 / L3)

- **L1 — per task.** At task completion or a real decision point (not every turn), write with date, what, who, outcome, next. Tool-by-tool progress is not an L1 write — that's scratchpad (§3). - **L2 — session boundary.** At > 60 % context or session end, write a full summary. - **L3 — consolidation.** On periodic runtime sweeps, find gaps, merge duplicates, transition contradicted facts to `outdated`.

## 10 · Orchestrator + subagent protocol

If your runtime dispatches subagents: - The spawning agent writes findings after each subagent completes. - The subagent writes its own findings before handing back. - Both writes carry their **own** `agent_id`.

Single-agent runtimes ignore this section.

## 11 · Recall policy (auto-gating)

Before each model call the plugin's context engine decides whether to issue a plugin-driven recall, so trivial turns ("hi", "ok", `/help`, single-emoji acks) don't hit the backend and pay tokens for an unhelpful recall block.

- **Default** (`CAURA_RECALL_POLICY=auto`): recall on substantive turns; skip very short prompts, trivial pings, and short slash-commands. - **Recall keywords always force recall** (e.g. `remember`, `recall`, `last time`, `we discussed`, `previously`, `history`); override the set with `CAURA_RECALL_TRIGGER_KEYWORDS`. - Other policies: `always`, `never` (education block only), `keywords`. - The gate only suppresses *plugin-driven* recall — **you can always call `caura_recall` directly** when a short turn needs context the gate can't infer.

Rolling skip counters (`recall_metrics`) ride the heartbeat for per-fleet visibility.

The plugin also auto-writes a short **turn summary** after substantive turns (`CAURA_AUTO_WRITE_TURNS`, on by default). That's a backstop, not a replacement for the deliberate, high-value writes in §3 — and it never evolves, supersedes, or files docs for you. Do that work yourself.

## 12 · Reuse and publish workflows — the `skills` collection

Proven workflows live as `SKILL.md` documents in the **`skills`** collection. You don't learn a new tool per playbook — it's the same `caura_doc`, so your vocabulary never grows with the library.

```text # Discover before improvising on a non-trivial workflow: caura_doc op=search collection=skills query="<intent>" caura_doc op=read collection=skills doc_id=<slug> # full body

# Publish something reusable so the fleet inherits it: caura_doc op=write collection=skills doc_id=<slug> \ data={ "name": "<slug>", "summary": "<1-line, intent-focused — this is what gets embedded>", "content": "<full SKILL.md>" } # Re-uploading the same doc_id overwrites it (upsert; no version history).

# Remove a wrong/superseded one: caura_doc op=delete collection=skills doc_id=<slug> ```

Slugs are filesystem-safe: `[a-z0-9][a-z0-9._-]{0,99}`. The `summary` is the only embedded field — write a sharp, intent-focused one ("Use when migrating SQLite→Postgres…") so the skill is found by meaning even when names don't match.

**Your tenant may run the Skill Factory** — a governed lifecycle around this collection (off by default; until an operator enables it, nothing below changes):

- **A `staged` landing is not an error.** With the Factory on, your `op=write` persists with `status=staged`, pending operator review in the **Skills Inbox** before it can become `active`. Don't retry, rewrite, or delete a write that landed staged — that's governance working. - **`doc_id` prefixes carry provenance.** `forge/<slug>` marks a skill distilled server-side by **Forge** from fleet activity; `agent/<slug>` marks a direct agent write; a plain `<slug>` (the prefix is optional) is typically operator-authored or imported. Read the prefix as origin — don't strip it when re-reading or updating a skill. - **Active skills may be pushed.** On plugin-managed runtimes, `active` skills can arrive on your skill load path directly — you may inherit a workflow without ever pulling it from the collection.

## A full loop, end to end

One task — orient, work, write, evolve — with the IDs threaded through:

```text # 1. Orient — recall, and keep the IDs that come back caura_recall "deploy api-gateway to staging" include_brief=true # → mem_8f2a (rule: "staging deploys need a smoke test"), mem_4d1c (last deploy)

# 2. Work — run the deploy, following the rule in mem_8f2a

# 3. Write — record the outcome (team-visible; home fleet resolved on omit) caura_write content="api-gateway v2.3 deployed to staging; smoke test green" \ visibility=scope_team

# 4. Evolve — report against the memories you acted on caura_evolve outcome="deploy succeeded, smoke test passed" \ outcome_type=success related_ids=[mem_8f2a, mem_4d1c] # if it had failed in a way the whole fleet should avoid: # add scope=fleet (trust 2) so the preventive rule reaches teammates ```

---

## Tool reference

Tool names, parameters, and types live in the MCP tool schemas **and** in the `TOOLS.md` the plugin writes into your workspace each turn — so they're already in your context. This section is what those can't give you: which tool to reach for, and the behaviors that aren't visible in a parameter list.

### Which tool, when

- Might have seen it before → `caura_recall` - Enumerate by filter / date / author → `caura_list` - Already hold the ID → `caura_manage op=read` / `caura_entity_get` - Record a fact / decision / event / outcome → `caura_write` - Structured record with a key → `caura_doc` - Find or publish a workflow → `caura_doc … collection=skills` - Fact no longer true → `caura_write` (new) + `caura_manage op=transition status=outdated` (old) - Acted on a recalled memory → `caura_evolve` - Re-check governance rules mid-session → `caura_keystones` (the auto-injected `<keystone_rules>` block is usually enough) - Recall quality off across queries → `caura_tune` (once; sticky) - Session boundary / sweep → `caura_insights` - Readiness probe / counts → `caura_stats`

> Authoring keystones (`caura_keystones_set`) is **not available to plugin > agents** — you can *read* governance rules (`caura_keystones`) but not write > them. Keystones are authored over MCP/REST by a trusted operator.

### Behaviors the schema won't tell you

- **`caura_recall`** excludes superseded memories (`status` ∈ {outdated, conflicted}) by default — pass `status` explicitly to walk the chain. - **`caura_write`** can't write `insight` / `outcome` / `rule` types — those are server-generated (via `caura_insights` / `caura_evolve`). `write_mode`: `fast` skips embedding → keyword-only recall afterwards; `strong` forces full LLM enrichment; `auto` is usually right. - **`caura_manage op=transition`** targets: `active · pending · confirmed · cancelled · outdated · conflicted · archived · deleted` (also in `TOOLS.md`). - **`caura_doc`** — `where` is scalar exact-match only (no array descent). A doc is invisible to `op=search` unless it has a `data["summary"]` (the only embedded field). Scope the search to a collection when you know it; omit `collection` for the single best match across the tenant. - **`caura_tune`** persists and reshapes every later recall — change one or two knobs at a time; call with no arguments to read your current profile (`fts_weight` 0 = pure semantic, 1 = pure keyword). - **`caura_insights`** saves findings as `insight` memories; run it at boundaries, not every turn. `focus="divergence"` needs a non-agent scope. - **`caura_stats`** is read-only — use it as a readiness/health probe, never a write-then-delete check.

### Anti-patterns

- Saving every intermediate step as a memory — pollutes recall. - Storing narrative as a doc, or structured keyed records as memories. - Saving a discoverable doc with no pointer memory — teammates won't find it. - Guessing `agent_id` / `fleet_id`, or inventing UUIDs. - Deleting when you should supersede. - Writing org-wide (`scope_org`) anything that isn't genuinely org-relevant. - Substituting `MEMORY.md` / local files for a Caura write. - Silently dropping a denied call — surface the error so the orchestrator can decide.

### Constraints & errors

- `caura_write`: exactly one of `content` / `items`; `items` ≤ 100 → `BATCH_TOO_LARGE`. - Cursor pagination needs `sort=created_at` + `order=desc`. - `_entity_get` / `_manage` use real UUIDs — never invent. - Error codes: `INVALID_ARGUMENTS` · `BATCH_TOO_LARGE` · `INVALID_BATCH_ITEM`. Other errors surface with HTTP status + message — return them to your caller, don't swallow.

---

*This skill ships with the Caura plugin at its install path; it is visible to every agent on a node that has the plugin enabled (`plugins.entries.memclaw.enabled`). To customize it for a specific agent, place a replacement file at `<workspace>/skills/memclaw/SKILL.md` — it takes precedence over this shared copy.*

Intended uses

• Use Memclaw when this documented workflow matches the task.