System One decision models

Use a model for a constrained judgment, not for permissions or side effects:

authorized state + trusted typed questions -> validated model answers
-> deterministic policy -> act / wait / review / abstain -> observed outcome

Keep hard business rules, authority checks, thresholds, action execution, confirmation, and rollback in code or human control. A legal response shape does not imply a correct judgment; a high probability is not permission.

Start here

1. Inspect the real application's state source, action boundary, tests, and deployment before changing it. Preserve existing deterministic/no-key behavior unless explicitly changing it is in scope.
2. Fill templates/decision-contract.md: trusted state, question IDs/types, allowed answers, unknown/review lane, side effects, owner, deadlines, and rollback. For tool control, also use templates/action-control-contract.md. New to this model class? Start with references/worked-decision-pilot.md to choose one bounded decision, then use references/concepts-and-patterns.md for primitive semantics and composition.
3. Open only the matching reference below. Keep exact question text and criteria in trusted configuration, not user-supplied state.
4. Validate response IDs, types, option sets, distributions, score rubric, and finite values before policy code; record returned model/version and enforce the pinned deployment identity. Treat malformed, unavailable, stale, or low-evidence results as the specified fallback.
5. Verify on representative held-out data and the actual delivery boundary; record provider/model, question/policy revision, outcome, and failure lane without raw secrets or unnecessary personal data.

Before expanding a cross-model battery, complete templates/decision-battery-design-review.md and review a small varied pilot. Define whether a test counts a distinct scenario, a question, or a request; freeze the answer rubric, comparison contract, and timing conditions before generating more cases. If those definitions or reviewer labels disagree, stop expansion and revise the design. Keep benchmark outputs outside this skill's tracked corpus unless publication is explicitly requested.

Route by task

Task	Read next
Hosted Jev API or SDK integration	references/jev.md; run scripts/decision_demo.py offline first
Laya checkpoints, routing, language, CPU/GPU/MPS	references/laya.md
Fine-tune the English Laya checkpoint on labeled typed decisions	references/laya-fine-tuning.md
Native C++ Laya inference, CUDA/Vulkan, or Jev-compatible HTTP	references/laya-cpp.md
Local or private/VPC Laya service	references/laya-self-hosting.md, then references/hosting-and-troubleshooting.md
Browser/desktop/voice control, agent routing, ranking, guardrails, deadlines	references/use-case-patterns.md
First System One pilot or worked evaluation of a decision, QA runner, or semantic CI gate	references/worked-decision-pilot.md
Learn from the 1,305-build field survey; identify implementation patterns and anti-patterns	references/field-patterns-and-antipatterns.md
Audit original browser, skill-router, supervisory, or moderation implementations	references/implementation-audit.md
Production QA step routing, cached replay, selector repair, or model substitution	references/qa-automation-pattern.md, then references/evaluation-and-calibration.md
Probability, threshold, calibration, model comparison	references/evaluation-and-calibration.md and templates/benchmark-record.md
Compare singleton and batched request quality or calibration	references/request-shape-evaluation.md and templates/benchmark-record.md
Measure router ablations and full fallback economics	references/cascade-economics.md and templates/benchmark-record.md
Determine whether a decision model improves an agent harness	agent-evals-and-observability for paired end-to-end tasks, trajectories, side effects, and cost/latency; keep this skill's model-level contract and calibration checks
Design or run a portable v1 label battery or provisional v2 cross-domain Choice/Noul/Score battery	references/decision-battery.md and templates/decision-battery-design-review.md; run scripts/decision_battery.py only after the pilot review
Synthetic QA pilot for Jev (failure triage, extra-test choice, semantic grading)	references/qa-pilot.md; run scripts/jev_qa_pilot.py offline first
Paired-eval semantic assertion audit in CI	references/qa-pilot.md, then scripts/jev_eval_audit.py; treat its verdicts as advisory and preserve exact grader results
Reproduce, operate, diagnose, or roll back this repository's Jev CI deployment	references/jev-ci-reference-deployment.md; inspect the current workflow before changing secrets or jobs
Screen Jev's advisory eval judgments against real outputs	references/qa-pilot.md and references/evaluation-and-calibration.md; use scripts/jev_eval_calibration.py for a blind packet, then independent labels or scripts/jev_teacher_label.py for model-teacher pseudo-labels
New open model or Jev-style replica	references/ecosystem-radar.md
Fastino GLiNER2.5-Decide local classification	references/gliner25-decide.md
Fine-tune GLiNER2 for Decide-style classification	references/gliner25-decide-fine-tuning.md
Failure, latency, device fallback, upgrade, rollback	references/hosting-and-troubleshooting.md

Run python3 scripts/systemone_probe.py --request examples/request.json for an offline contract check. Add --live only when the user has authorized transmitting that state and incurring cost. For local Laya, scripts/laya_service.py requires a pinned local model directory and a runtime secret; it is a private reference adapter, not a public Internet service.

Cross-cutting limits

• Jev is managed/API-only; do not invent a self-hosted Jev weight download.
• Laya and Jev can share a typed application interface, but not assumed thresholds, calibration, latency, language behavior, or model quality.
• Before enabling Laya caller traffic, keep ingress private and authenticated; define finite, application-specific caps for request bytes, question count, options per Choice, concurrency, queue wait, and total deadline. Readiness waits for the pinned model, tokenizer, actual device, and any calibration artifact the application uses. The bundled adapter's /readyz checks device residency only; extend it to cover every required artifact before routing production traffic.
• For large Laya Choice sets, check tokenized labels against the head-token budget, verifying coverage and truncation; an option-count transport cap does not prove quality. If shortlisting, measure recall and treat probabilities as conditional on exactly the retained candidate set. Prefer other, unknown, or review when labels are not exhaustive. See references/laya.md.
• Independent questions may share one call, but test the exact batched request shape on frozen cases. Dependent questions need another call when the first answer changes their state or candidate set.
• A model cannot replace exact arithmetic, provenance, eligibility, safety reflexes, or irreversible approval. A text-generating model may be a separate bounded stage after a typed route, not an implicit source of authority.

Finish an integration only when its contract, held-out evaluation, failure path, deployment/readiness check, and rollback record exist. For diagnosis, stop after the smallest evidence identifies the boundary and one recheck verifies a fix, or after three non-converging passes with evidence for the owner. Do not generalize from a single demo or vendor benchmark.

When not to use

Use ml-engineering for general training strategy; docker-compose or kubernetes for their serving infrastructure; ai-governance for organization-wide authority design. Use a generative-model skill for prose, open-ended planning, or long reasoning without a typed-decision contract.