bvandeusen
43a860c3ac
Rules can now belong to either a rulebook topic OR a single project,
enforced by a CHECK constraint (exactly-one of topic_id/project_id).
Adds the create_project_rule MCP tool + REST endpoint, surfaces
project-scoped rules in get_project/get_task/start_planning under a
new project_rules field, and adds a project Rules tab section with an
inline create form so the operator can author project rules from the
UI without rulebook ceremony.
- migration 0059: rules.project_id (FK projects ON DELETE CASCADE),
topic_id now nullable, CHECK ck_rule_topic_xor_project, index on
project_id
- model: Rule gains project_id; to_dict exposes it
- service: create_project_rule with project-ownership guard; list_rules
with project_id filter UNIONs subscription-derived + project-scoped;
get_applicable_rules adds a project_rules field; get_rule / update_rule
/ delete_rule fetch via a shared _fetch_owned_rule that handles both
rulebook and project ownership paths
- trash: project delete cascades to project-scoped rules
- MCP: create_project_rule tool registered; _INSTRUCTIONS mentions both
create_rule and create_project_rule paths
- REST: POST /api/projects/<id>/rules (statement required, title derived
if omitted)
- frontend: Rule type gains nullable topic_id + project_id; createProjectRule
client; ProjectRulesTab.vue gains a "Project rules" section with inline
create form and per-rule expand/delete
- tests: register count → 18; create_project_rule unit tests (required
fields, title derivation, explicit-title pass-through); applicable_rules
shape tests now include project_rules; trash cascade test updated to
expect 5 executions
S1+S2 (always_on flag + Scribe-first prompt) shipped in 658348f.
S4 (enter_project handshake) follows.
Co-Authored-By: Claude Opus 4.7 <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.