Caught on the first live read after deploying #3670. The readout contradicted itself: pre_tool_rule top_score.min 0.7204 the lowest score ever RETURNED near_misses.max 0.7457 "rejected", but scored higher `best_available_score` is measured pre-threshold, which is right, but for the rule arms it is also PRE-EXCLUSION, which is not. The note arms pass `exclude_ids` into semantic_search_notes so their score is already post-exclusion and clean; `semantic_search_rules` takes no such parameter, so the rule arms filter in Python after the search and a rule that cleared the bar and was dropped as a repeat still reported its score on a zero-result row. That is #3497's distinction — a ranker decline versus a reader already ahead of it — reintroduced one level up, inside the field built to replace a tautology. The population now also requires `suppressed_count IS NULL OR = 0`. The NULL arm is principled rather than permissive: null means the caller filtered INSIDE the search, which is exactly the case where the reported score cannot be contaminated. Deliberately conservative — a call carrying both a repeat and a lower genuine miss is dropped whole, losing that point. It undercounts; it cannot corrupt, which is the right way round for a number read against a bar. It also makes `near_misses.max < threshold` true BY CONSTRUCTION rather than by fixture: an above-bar candidate nobody excluded would have been returned, so its call is not in the population at all. THE TEST DID NOT CATCH THIS, and that is the part worth keeping. The assertion `nm["max"] < 0.72` was already there, with exactly the right intent. It passed because the fixture contained no suppressed call — the guard held because the breaking shape was absent, not because the code was right. Rule 167's stated failure mode, in a test written while citing rule 167. The fixture now builds that shape: a 0.9 hit dropped as a repeat, which lands in the population and drags `max` above the threshold unless the predicate excludes it. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011cPyzNnegXHr5iRMzzy5KJ
Fabled Scribe
A self-hosted work system-of-record for software projects, built to be driven by Claude Code. Notes, tasks, issues, projects, milestones, rules, and stored processes — reachable from Claude via a built-in MCP endpoint and a bundled Claude Code plugin, with a clean web UI for humans. No in-app LLM; Claude is the sole assistant.
Features
Notes and tasks with a Markdown editor, sub-tasks, milestones, issues, and kanban project workspaces. Stored processes, an engineering rulebook system (with an inception step that decides what each project inherits), and semantic search with proactive knowledge-injection into Claude's context. A knowledge graph, per-user/group sharing, and a built-in MCP server (/mcp) plus a bundled Claude Code plugin so Claude can record and recall your work directly.
Quick Start
Prerequisites: Docker and Docker Compose. No GPU or local model needed — Claude is the sole assistant, reached over MCP.
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. To connect Claude, create an API key under Settings → API Keys and install the Claude Code plugin — see API Keys & MCP.
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.