Trajectory

trajectory — verified memory and Quality Compass

This skill has two deliberately separate lanes:

The Quality Compass is the default path for new QA and routing work. It gives a simple k-nearest-neighbor baseline before another micro-NN is considered.

One-command flow

botte qa                                      # status + one next step
botte qa "summarize this failing test log"   # shadow advice shortcut
botte qa advise "summarize this log" --task-type summary --json
botte qa record "summarize this log" \
  --route local --verdict pass --verified-by tests:pytest \
  --duration-ms 180 --tokens 140 --evidence pytest:test_logs

botte qa <task> is intentionally equivalent to botte qa advise <task>. Advice always says why it suggested, abstained, or invoked a human gate. It never executes the route.

Python API

from skills.trajectory import advise_route, quality_status, record_verified

record_verified(
    "summarize parser failures",
    project_root=".",
    route="local",
    verdict="PASS",
    verified_by="tests:pytest",
    evidence_refs=("pytest:test_parser",),
)

advice = advise_route("summarize another parser failure", project_root=".")
print(advice.to_dict())
print(quality_status("."))

Execution paths should use the bounded envelope adapter instead of calling the verified ledger directly:

from skills.trajectory import emit_outcome

emit_outcome(
    "summarize parser failures",
    project_root=".",
    execution_id="ci-run-42/job-3",  # hashed, never stored verbatim
    source="ci",
    route="local",
    status="PASS",
    verified_by="ci:pytest",
    evidence_refs=("pytest:test_parser",),
    harness="local-extract-v1",
)

The envelope accepts partial facts and lifecycle states (PARTIAL, FAIL, UNCERTAIN, PASS, PASS_ROBUST, ABSTAINED, ESCALATED, and APPROVAL_REQUIRED). It writes a private, idempotent status row. A verdict is promoted to the k-NN support ledger only when both an allowed external verifier and at least one evidence reference are present. Replaying the same execution and outcome returns the existing row and cannot add another label.

The local harness, auto-router, bounded Codex/Hermes run manifests, and primary GitHub CI matrix are live adapters. Deterministic schema, grounding, and citation checks can produce verified PASS, FAIL, or UNCERTAIN labels; gates, abstentions, and escalation remain explicit without treating the final unverified model answer as evidence. CI writes the private envelope only inside the ephemeral job workspace and uploads a separate sanitized three-line summary; run IDs, fingerprints, paths, and evidence details are not published.

Kanboard/Odin and other authenticated task planes can consume the passive botte.task-quality-status/v1 contract with botte qa task-status or MCP tool qa_task_status. It exports an opaque task reference, status, next safe action, and references from independently verified evidence only. It never sends task text or fingerprints, never marks work terminal, and never authorizes a task transition. See docs/integrations/kanboard-quality-status.md.

Codex can call the MCP tool qa_agent_run; Hermes can use the same MCP tool or the botte_qa_agent_run function bridge. Both accept the strict botte.agent-run/v1 contract in docs/schemas/agent-run-manifest.schema.json. Unknown fields such as raw responses or stdout are rejected instead of being persisted.

Quality contract

• Valid verdicts: FAIL, UNCERTAIN, PASS, PASS_ROBUST.
• Valid routes: deterministic, local, cloud, human.
• Labels require an external verifier such as tests, schema validation, deterministic roundtrip, replay, human review, independent review, or a benchmark. A model/backend self-report is rejected.
• Raw task text is never written to the quality ledger. Botte stores a SHA-256 fingerprint and a normalized 128-dimensional sparse hashed feature vector.
• Repeated copies of the same task/route do not inflate grounding progress or k-NN support.
• High/critical risk bypasses k-NN and retains a human approval gate.
• The baseline is uncalibrated and shadow-only. It cannot activate routing.
• At 2,000 non-duplicated verified outcomes, the next step is evaluation: temporal holdout, calibration, ablation, drift, and rollback — not automatic deployment.

The k-NN chooser selects the least expensive observed route whose similar support clears the quality floor. If evidence is sparse or conflicting, it abstains and leaves the deterministic router in control.

Leakage-resistant routing benchmark

Compare the deterministic rule, Quality Compass k-NN, and the existing binary_router micro-NN on one oldest-train/newest-holdout split:

python -m skills.trajectory.cli benchmark --project . --json
python -m skills.trajectory.cli benchmark \
  --missions private/sanitized-routing-missions.jsonl \
  --code-ref "$(git rev-parse HEAD)" --output benchmark.json --json

Without a mission file, the command inventories observable evidence and emits a machine-readable collect_more_data gap report. A mission set must conform to docs/schemas/quality-routing-mission.schema.json, use independent evidence, contain only sanitized task text, and keep task families disjoint across the temporal boundary. Fixtures exercise the harness but can never rank candidates. The output contract is docs/schemas/quality-routing-benchmark.schema.json.

The benchmark measures routing-oracle accuracy, coverage, abstention, disagreement, confidence intervals, decision latency, Python allocation peak, and calibration where confidence exists. Model-answer and harness-execution quality remain separate and explicitly unobserved unless independently verifiable outcomes are supplied. It never executes a model, trains weights, changes a route, or grants ACT authority.

MCP tools

Local state

Project-local .botte/ data is operational state and must not be committed. The legacy capture, search, load, and get_stats API remains available for existing deterministic solver integrations; do not put secrets in that legacy store.

Why k-NN comes before another micro-NN

k-NN updates immediately when a verified example arrives, has no training job, and can show the exact neighboring outcome IDs behind a suggestion. A compact micro-NN becomes a candidate only when the support set is large, diverse, and stable enough to beat this baseline on a temporal holdout without quality loss.