---
name: system-one
description: >-
  Design, integrate, evaluate, self-host, and troubleshoot typed System One
  decision models including TypeSafe Jev and Convai Innovations Laya. Use for
  Choice/Score/Noul judgments inside deterministic software, app-control loops,
  routing, ranking, guardrails, calibration, or private Laya inference. Do not
  use for open-ended generation, exact rules or authorization, or generic LLM
  serving without a bounded decision contract.
license: MIT
compatibility: Current provider/model documentation needs network access; local Laya operation needs Python 3.10+, PyTorch, Transformers, and model-weight storage.
metadata:
  author: system-one contributors
  version: "1.1"
---

# System One decision models

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

```text
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`.
   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` |
| Probability, threshold, calibration, model comparison | `references/evaluation-and-calibration.md` and `templates/benchmark-record.md` |
| 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. Dependent questions need another
  call only 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.