bvandeusen
9eddb8497c
Direction change (operator, see plan task #755 work-log): the plugin must NOT depend on disabling a native Claude function to work. It earns its place by steering behavior, not by toggling autoMemoryEnabled. Memory doctrine (no dual-write): - using-scribe SKILL.md gains "Scribe holds these functions — don't keep a second copy": route rules/recall/planning to Scribe, don't also write them to native auto-memory, never instruct disabling a native function, and accept a "Scribe-shaped hole" if the plugin is removed (recover over time). - mcp/server.py _INSTRUCTIONS: drop the paragraph that told the model to create/refresh a "rules live in Scribe" pointer in CLAUDE.md / ~/.claude memory. That was an active dual-write instruction; the SessionStart hook is the bridge now. Replaced with the no-dual-write / no-settings-dependency doctrine. Supersedes plan #755 Phase 6 ("set autoMemoryEnabled:false"). Project-scope discipline (stop cross-project bleed): - using-scribe SKILL.md gains "Stay inside the active project's scope": pass project_id to every read, only reference/offer work on the in-scope project, ask before switching. - _INSTRUCTIONS scope bullet extended from reads to referencing/offering, and flags get_recent as cross-project. - get_recent docstring gains a scope note steering to scoped list_* when a project is active. plugin.json 0.1.4 -> 0.1.5 so clients' caches actually refresh (re-shipping under the same version does not bust the cache). 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.