bvandeusen
33f9a0a4d4
#755 Phase 4. Saved Scribe Processes (DRY pass, Drift Audit, …) now surface as auto-triggered Claude Code skills instead of pull-only get_process calls. Design correction vs the plan: stubs live in the USER's ~/.claude/skills/, NOT plugin/skills/_instance/. The plugin is git-cloned and identical per install, so instance-specific generated files can't ride in it; personal skills are live-detected within the session (verified via claude-code-guide). MCP prompts were the alternative but are pull-only (no relevance auto-surface), so skills are the right primitive. - backend: GET /api/plugin/processes manifest (services/plugin_context. build_process_manifest) — {name, slug, description} per Process; description is the auto-surface trigger (title + preview); slugs deduped, blanks skipped. - plugin: scribe_sync_processes.sh writes ~/.claude/skills/scribe-proc-<slug>/ SKILL.md (body = "call get_process(name), follow verbatim") and PRUNES stale scribe-proc-* stubs. Fail-open + silent; a transient fetch failure never wipes existing stubs. Runs as a 2nd SessionStart hook + via the /scribe:sync command. - plugin.json 0.1.7 → 0.1.8; README updated. - tests: build_process_manifest (render, slug dedupe, blank-title skip, preview truncation). Sync script's write+prune validated in isolation (plugin/** is not CI-covered): correct stubs created, stale pruned, unrelated skills untouched. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Fabled Scribe
A self-hosted second brain and project management application with integrated LLM capabilities. Write, organise, and act on your notes and tasks with the help of a local AI assistant — all running on your own hardware.
Features
Notes and tasks with a Markdown editor, sub-tasks, milestones, and kanban project workspaces. AI chat with streaming responses, RAG over your notes, and tool use (web search, calendar, weather). A daily briefing that digests your tasks, RSS feeds, and weather on a schedule. Knowledge graph, per-user/group sharing, PWA with push notifications, and an MCP server for external AI clients.
Quick Start
Prerequisites: Docker and Docker Compose. 8 GB+ RAM recommended for LLM inference.
Download docker-compose.quickstart.yml from this repo, then:
# Optional but recommended — set a secret key
export SECRET_KEY=your-random-secret-here
docker compose -f docker-compose.quickstart.yml up -d
Open http://localhost:5000. The first user to register becomes admin. Go to Settings → General to pull an LLM model — qwen3:8b or llama3.1:8b are good starting points.
GPU: Ollama runs CPU-only by default. See the comments in
docker-compose.quickstart.ymlto enable NVIDIA GPU passthrough.
Development: To build from source, see Development.
Documentation
| Doc | Contents |
|---|---|
| Architecture | Stack, design decisions, data models, key services |
| Configuration | Environment variables, Docker Compose, production setup, security |
| Features | Detailed feature breakdown and keyboard shortcuts |
| Development | Dev workflow, CI/CD, migrations, release process |
| API Keys & MCP | API key management and Fable MCP install guide |
| SSO / OAuth | OIDC setup for Authentik, Keycloak, and other providers |
| API Reference | All REST API endpoints |
License
This project is privately maintained.