bvandeusenandClaude Opus 5
f8e53c1c35
Found by reading a live `retrieval_telemetry` readout after milestone 419
deployed, not by inspection. The readout said:
cannot_decline / report_preference — "45 calls, 0 of them returned
nothing. An arm that fires unasked has to be able to say nothing; this one
never has. Check that it applies its floor at all."
And printed, beside it, that arm's band: p10 = p50 = p90 = min = max = 0.791.
FIVE IDENTICAL PERCENTILES IS THE TELL. That is not a ranking, it is one
record at one score on every call — because `report_preference` searches a
fixed string (`reply_preferences.COMPLETION_QUERY`, a module constant, and
deliberately so).
For a fixed query against a stable corpus the top score is a CONSTANT, so the
arm's decline rate is 0% or 100% and never in between; which of the two it is
depends only on where the bar sits relative to that one number. "Never
returned nothing" is therefore arithmetic, not evidence, and the warning's own
remedy — check whether it applies a floor — cannot be answered from it.
The arm already knew this about itself; the warning did not:
"a fixed query makes this arm's score a constant and a floor a hair above
it produces a dead arm no amount of traffic will ever reveal"
— services/reply_preferences.py
THIS CLASS OF BUG ALREADY HAS A GUARD, which is the argument for the shape of
the fix. `Point.logs_unconditionally` exists because of #3497: both rule arms
once logged only their hits, so their zero count was structurally 0 and this
same warning would have fired on a LOGGING property while sending the reader
to move a threshold that was never involved. This is that one step over — a
QUERY-SHAPE property — and gets the same treatment: a declared field on
`Point`, and exclusion rather than trust.
AND THE WARNING THAT WOULD BE INFORMATIVE HERE DID NOT EXIST. For a fixed-query
arm the dangerous state is the mirror image: every call empty, meaning the bar
is above the constant and no further traffic will ever move it. The arm is off
rather than quiet, and nothing in the readout said so — `expects_traffic`
covers an arm with NO calls, not one with calls and a 100% decline rate. That
state is real and reached: `report_preference` once logged 69 consecutive
declines at 0.0006 under its bar.
So `fixed_query_never_clears` sends the reader to `near_miss_samples` and not
to the dial — because that incident is also the one where the statistic and
the correct action pointed opposite ways. Every percentile said lower the
floor; opening the refused record showed it was rule 77 arriving as a false
positive, and lowering it would have delivered that rule on every completion
report ever written.
Guards in tests/test_retrieval_warnings.py, including the falsifier that
matters most here: `cannot_decline` must still fire on an arm whose query
varies, or this change is a disabled check wearing a narrowed one's clothes.
Both boundaries tested from both sides, per that module's own standard.
The new code is documented in the `retrieval_telemetry` tool docstring beside
the others (rule 33) — an undocumented code in a readout is a reader meeting a
verdict with no way to disagree with it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01821k5B3Ysecp9fNYs92Kuy
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.