intelligence

name	intelligence
description	Local-first intelligence layer that learns from past AI coding sessions, does RAG over your codebase, and cuts token waste. Trigger on "learn from my sessions", "index my code", "retrieve context", "/learn", "/index-code", "/retrieve", or "intelligence layer".

AutoClaw Intelligence — Learning & Retrieval Layer

A local-first "second brain" for AutoClaw. It ingests past AI coding sessions from any tool (plus AutoClaw's own logs), distills what was kept vs. discarded, indexes your real codebase, and serves that context back to cut token waste.

Status: core loop + Wave A implemented. The module skeleton, configuration, on-disk contract, and packaging are in place. The four core-loop commands /learn, /index-code, /retrieve, and /search plus the Wave A commands /sources, /rag-generate, /scaffold, and /metrics are now implemented and wired into activation and the @autoclaw chat surface. Universal session ingestion (Claude Code, Claude Desktop, Kiro, Gemini), git-validated kept signals, the metrics dashboard, and the pluggable Postgres/Ollama backends are live. The /service watch command arrives in a later phase. Do not claim a command ran when it is marked Planned; say it is not available yet.

Operating Rules (read before any sub-command)

1. Use file tools, not shell, for directories and files. Create folders and files with the host's file/write tool (e.g. Write, create_file, edit_file). Do NOT use mkdir -p, touch, New-Item, or shell redirection — they fail across the Bash/PowerShell/cmd.exe mix you may be running on. If you must shell out, detect the platform first.
2. Always use forward slashes in paths (e.g. .autoclaw/vector/config.json). Node, git, and every supported shell accept them.
3. Be idempotent. Creating a contract directory that already exists is a no-op. Never overwrite .autoclaw/kdream/memory/MEMORY.md or preferences.json — append or merge only.
4. Output discipline. When confirming an action, output ≤3 short lines: what was done, current counts, next step. Do not narrate your reasoning, repeat headings, or invent style rules. No emojis unless the user asked.
5. Never invent files, sessions, learnings, or metrics. Only report what you actually read from disk. If a store is empty or a command is unimplemented, say so plainly.
6. Local-only and consent-first. Third-party session sources are opt-in; AutoClaw-native logs are on by default. Redact secrets/PII before embedding, storing, or logging.

On Invocation

Determine the sub-command from the user's message and route to the matching section. If the command is marked Planned, tell the user it is not implemented yet and point to what foundation provides (config + on-disk contract).

Command surface

Command	Status	Purpose
/learn	Implemented (core-loop)	Sweep discovered sessions, distill kept-vs-discarded learnings, write stores. Also enriches the Knowledge Graph: consensus decisions, mined successful workflow patterns, and review findings are recorded as durable, deduped KG thoughts (kind decision/finding). Args: --last N (limit to the most recent N sessions), --focus "area" (bias distillation toward a topic). Output: counts of sessions swept and learnings written.
/index-code	Implemented (core-loop)	Chunk + embed the workspace codebase into the vector store. Args: --force (re-index everything, ignoring the last-index watermark). Output: files indexed and chunk count.
/retrieve	Implemented (core-loop)	Retrieve the most relevant code/learning chunks for a query. Usage: /retrieve <query>. Output: the top matching chunks with their source paths.
/search	Implemented (core-loop)	Semantic search over distilled learnings. Usage: /search <query> [--limit N] (cap results at N). Output: ranked learnings with scores.
/sources	Implemented (universal-ingestion)	List every discovered session source, its tier, availability, and enabled state; opt-in/disable third-party sources (consent-gated). Discovers Claude Code, Claude Desktop, Kiro, Gemini + AutoClaw-native.
/scaffold	Implemented (signal-and-rag)	Emit your learned agent-style.md (optionally --focus "area") to prepend to a new agent task. Copied to the clipboard.
/rag-generate	Implemented (signal-and-rag)	Build a grounded RAG prompt for a task from retrieved code + learnings + style + memory. Degrades gracefully without a vector backend. Copied to the clipboard.
/metrics	Implemented (metrics-dashboard)	Show learning-run counts, kept-rate, and token usage (real vs estimated). A live dashboard view is available under the AutoClaw activity-bar container.
/service	Planned (automation-reach)	Run the continuous watch service that ingests new sessions as they land.

Knowledge Graph

Distinct from the vector stores above, the Knowledge Graph (.autoclaw/kg/kg.db) holds agent- and task-attributed facts — decisions, findings, observations, and learned patterns — connected by typed relations, with bi-temporal validity. It is fed automatically by the orchestrator (dispatch/completion), /learn (consensus decisions + mined workflow patterns + review findings), and the kg.record MCP tool. Agents recall from it with the kg.search / kg.traverse MCP tools; humans browse and visualize it (searchable list + force-directed graph) via the AutoClaw: Knowledge Graph — Browse & Visualize command (autoclaw.kg.browse).

Configuration

Single configuration surface at .autoclaw/vector/config.json. When absent, the layer uses validated defaults without writing a file:

• backend: sqlite-vec (default) or postgres.
• embedding: provider transformers (default, Xenova/nomic-embed-text-v1.5, 768-dim), ollama, or none. The none provider is the always-available degraded fallback that needs no native modules.
• rag, search, tokenLogging, and per-sources enablement.

Invalid fields fall back to their default with a warning rather than failing.

On-disk contract

The layer owns only paths under .autoclaw/ and never collides with .cursor/, CLAUDE.md, or other tools:

.autoclaw/vector/      config.json, db.sqlite, last-index.json
.autoclaw/learnings/   distilled learnings
.autoclaw/metrics/     token/usage metrics
.autoclaw/history/     per-source extraction watermarks
.autoclaw/.locks/      advisory file locks
.autoclaw/kdream/memory/MEMORY.md   owned by KDream — appended, never overwritten

Generated data (db.sqlite, .locks/, history/) is gitignored.

Reuse, don't fork

This layer builds on existing AutoClaw subsystems rather than duplicating them: src/runners for session discovery, src/llm (cost ledger) for token logging, src/memory for memory records, src/mcp for retrieval exposure, and the autoclaw-kdream activity-bar container for any UI. Learnings feed the KDream dream pipeline.