pi-decider

Decision backends as a tool call, for pi and omp. The model asks a backend for typed judgments over state you hand it and gets back answers with probabilities instead of prose — so code (or the model) can threshold them.

decide(state, questions[]) -> answers[] with probabilities + confidence

Backends are swappable per call and per question, so one request can combine several of them:

typesafe and openrouter both serve Jev (OpenRouter through its Decisions API, model ~typesafe/jev-latest — see Jev through OpenRouter). llm is a stand-in: it asks an ordinary chat model and enforces the same answer contract around it.

Install

The plugin is a package: package.json declares its extension entry (pi.extensions) and its bundled skill (pi.skills), so a harness that loads the package directory gets both. typebox and the harness API come from the harness itself — there is nothing to npm install.

pi — project mount (.pi/settings.json; paths resolve relative to .pi):

{
  "extensions": ["../plugins/pi-decider"],
  "skills": ["../plugins/pi-decider/skills"]
}

pi mounts extensions by path, so the bundled skill needs the explicit skills line. Project settings load only after the project is trusted: pi asks once, or run pi --approve / save it with /trust.

omp — package mount (omp loads the package, including skills/):

omp config set extensions '["<abs path>/plugins/pi-decider"]'    # persistent
omp -e "<abs path>/plugins/pi-decider"                           # per run

omp's directory scan of <cwd>/.omp/extensions/ loads extension files only — a package that way does not bring its skills/, which is why the mount points at the package directory itself. Global alternative: cp -r plugins/pi-decider ~/.omp/agent/extensions/pi-decider (then the skill needs a root omp scans, e.g. .omp/skills/decide/ or skills.customDirectories).

Load the plugin once per harness. Two copies registering the same tool name make the second one fail to load (Tool "decide" conflicts with …), so drop the global copy when you use a project mount.

Then /reload in a running pi session (omp has no /reload — start a new session).

Skill

skills/decide/SKILL.md ships inside the package and teaches the model when and how to use the tool: the three primitives, the four shapes (single / fan-out / gate / composite), question-design rules, per-question backend routing, threshold and weight handling, configuration commands, and limits.

Declared as "skills": ["./skills"] under pi in package.json, so any harness that loads the package directory discovers it: pi via the skills settings line above, omp directly from the package mount. pi exposes it as /skill:decide and lists it in the system prompt; omp additionally serves it as skill://decide.

Configure

/decide setup walks through it inside the harness: pick the default backend, choose whether a key is an environment reference (recommended) or a literal value, confirm model/base URL from their defaults, then write the file. /decide status shows what resolved and probes the selected backend.

Everything lives in <agentDir>/decider.json and is re-read on every call, so edits need no /reload. The agent directory comes from the harness itself (getAgentDir()), which means:

Each backend block is self-contained — its own credential, endpoint, path, model, prices, limits:

{
  "backend": "auto",
  "typesafe": {
    "apiKey": "$TYPESAFE_API_KEY",
    "baseUrl": "https://api.typesafe.ai",
    "path": "/v1/systemone",
    "model": "jev-latest"
  },
  "openrouter": {
    "apiKey": "$OPENROUTER_API_KEY",
    "baseUrl": "https://openrouter.ai/api",
    "path": "/alpha/decisions",
    "model": "~typesafe/jev-latest"
  },
  "llm": {
    "apiKey": "$DECIDER_LLM_API_KEY",
    "baseUrl": "https://openrouter.ai/api/v1",
    "path": "/chat/completions",
    "model": "",
    "temperature": 0,
    "maxTokens": 2048,
    "jsonMode": "json_object",
    "repairAttempts": 1
  },
  "modelsPath": "/v1/models"
}

"$VAR" (or "${VAR}") resolves the environment at request time, so keys stay out of the file; a literal value is used as-is. Per-backend timeoutMs, maxRetries, costPerMTokInput, and costPerMTokOutput follow their defaults (60s, 2 retries for decisions backends, 0 for chat; Jev is $0.042/Mtok in and free out).

auto resolution: explicit call argument → the backend field → the first usable backend, preferring real Jev (typesafe, then openrouter) over the llm proxy. A backend is usable when it has a credential and, for chat, a model id.

Tool

decide({
  state: "Help! My payouts have been failing for 3 days.",
  questions: [
    { id: "is_urgent",  type: "noul",   instructions: "Does this convey urgency?",
      criteria: { true: "Explicitly time-sensitive", false: "No urgency expressed" } },
    { id: "department", type: "choice", instructions: "Which team should handle this?",
      criteria: { billing: "Payments, invoicing, refunds", technical: "Bugs, outages", none: null } },
    { id: "frustration", type: "score", instructions: "How frustrated is the customer?",
      criteria: ["Calm", "Frustrated", "Very angry"] }
  ]
})

Questions sharing a backend+model are sent as one request; different batches run in parallel. Failures are per-question: one failing batch only marks its own questions ERROR.

Answer text carries one header per batch, then one block per answer:

openrouter · OpenRouter Decisions API · model ~typesafe/jev-latest · provider TypeSafe · 640 ms · tokens 321 in / 57 out · cost $0.000013
is_urgent [noul] 0.87
department [choice] billing · confidence 0.91
  probabilities: billing 0.8 · technical 0.1 · none 0.1
frustration [score] 1.5 · confidence 0.76
  levels: 0 Calm · 1 Frustrated · 2 Very angry
  probabilities: 0 0.33 · 1 0.33 · 2 0.33

Structured results (per-batch endpoint, provider, tokens, cost, plus every question/answer/issue/note) are persisted on the tool result details, and tokens/cost flow into pi's usage totals.

Commands

/decide                      status: resolved config, per-backend state, live probe (default)
/decide add [backend]        guided: key, model, base URL for one backend
/decide set [t] [f] [v]      one validated field; interactive when a part is missing
/decide unset [t] [f]        drop a field, or a whole backend block (confirms first)
/decide question [text]      ask your own question (interactive; Enter through = smoke test)
/decide models [b] [text]    models a backend catalogue reports, optionally filtered
/decide setup                guided setup incl. the default-backend choice
/decide help
/jev                         alias for /decide

Aliases: setup = init, unset = remove = rm, question = ask.

set/unset targets are root (plus the shorthand set backend llm) and the backend ids. Writable fields: apiKey, baseUrl, path, model, timeoutMs, maxRetries, costPerMTokInput, costPerMTokOutput, and for llm also temperature, maxTokens, jsonMode, repairAttempts. Arguments are optional everywhere — a bare /decide set walks you through target, field (showing current values), and value, and literal credentials are masked in the report. Everything is also scriptable: /decide set openrouter apiKey $OPENROUTER_API_KEY.

Question shapes

/decide question covers the documented usage shapes rather than a single canned probe. Enter through the prefills to get a smoke test; answer the dialogs to ask anything.

Intent routing needs no separate shape: it is a choice question whose answer selects a handler in the code that calls the tool.

Jev through OpenRouter

OpenRouter serves Jev on its Decisions API, not chat completions:

POST https://openrouter.ai/api/alpha/decisions
Authorization: Bearer $OPENROUTER_API_KEY
{ "model": "~typesafe/jev-latest", "state": "...", "questions": { "is_urgent": { "type": "noul", "instructions": "..." } } }
→ { "model": "~typesafe/jev-latest", "provider": "TypeSafe", "answers": { ... }, "usage": { "inputTokens": ..., "outputTokens": ... } }

Verified against the live service on 2026-09-18:

• GET https://openrouter.ai/api/v1/models/~typesafe/jev-latest/endpoints → modality: "text->decisions", output_modalities: ["decisions"]; POST /api/alpha/decisions without a key returns 401 (the route exists).
• Pricing matches TypeSafe direct: $0.042/Mtok input, $0/Mtok output.
• Only the ~typesafe/jev-latest family alias exists (pinned ids such as ~typesafe/jev-1.13.0 return 404), and it is absent from the public /v1/models listing — /decide status falls back to the model-route lookup above.
• Usage keys are camelCase on OpenRouter and snake_case on TypeSafe; both are accepted, and provider is reported when the response carries it.

Contract enforcement

The plugin owns the input and output shapes for every backend:

• Questions are validated and canonicalized before any call (normalizeQuestions); aliases (yes_no, rating, classify), option arrays, and level maps are normalized, while duplicate ids, single-option choices, and single-level scores are rejected.
• Answers are validated against the question they belong to (conformAnswers): noul in [0,1], choice must be one of the criteria keys, distributions must cover every option/level and are renormalized to sum 1, score must lie inside the level range.
• The llm backend additionally demands a single JSON object (response_format: json_object, retried without it if the endpoint rejects the parameter), recovers fenced or prose-wrapped output, and retries once with the validation errors fed back before reporting a per-question ERROR.
• A question that names an unusable backend (no credential, or a chat backend without a model id) is reported as that question's ERROR; the rest of the call still runs. Only an unusable call-level default is fatal, because then no question has a backend left.
• A chat model that spends its entire output budget before writing JSON (reasoning models do) is retried once with a budget raised 4× (capped at 16k). A batch that still cannot answer fails with the budget and the reasoning tokens in the message, and the tokens/cost of every attempt — including the rejected ones — are counted in the report.
• confidence is only ever relayed, never invented; cost appears per backend: computed from configured Jev prices, taken from the response when a chat endpoint reports it, otherwise omitted.

Benchmark

test/jev-suite/ is a scored benchmark for the backends, not a smoke test: 100 items over 8 self-contained state documents, on two axes — domain (reasoning, arithmetic, news, law, medicine, engineering, meta) × category (judgment, calibration, abstention, arithmetic, architecture, meta, no-answer).

Every item carries ground truth — a computed value, a normative best option, a forbidden assertion — or an invariant, so a run reports pass/fail per item, Brier over the noul items, and pair assertions: language / option-order items must return the same pick, and a changed constraint (w1 vs w2, l1 vs l2) must flip it. The law and medicine documents are fictional and only ever test applying the clauses/protocol stated in the document.

bun run test/jev-suite/run.ts                                    # live; backend from decider.json
bun run test/jev-suite/run.ts --backend openrouter --repeat 2     # + per-pass stability
bun run test/jev-suite/run.ts --json out.json                     # record a run as a reference
bun run test/jev-suite/run.ts --replay out.json                   # re-score offline, no network
bun test test/jev-suite                                           # suite integrity + scorer only

test/jev-suite/reference/jev-1.13.json is a recorded run (~typesafe/jev-latest, 2026-09-18): 100/100 answered, 15.3k in / 4.1k out, $0.00064, Brier 0.079 over 37 noul items.

Two measured properties the suite pins down:

• Framing matters. The same fact scores very differently when the candidate answer sits in state: num_span_claim 0.21 ("is 1098.58 h correct?") versus num_span_value picking 1098.58 outright. Put candidates in criteria, keep state to raw facts.
• No-answer options only help when the model is unsure. With four wrong options and an escape hatch, opt_span_escape and opt_ledger_escape take the hatch, while opt_comb_escape walks past it at confidence 0.92 (its own wrong value looks right). A confidence threshold is the only reliable guard.

Limits

Jev takes text only (convert images/audio first), has a 64k request budget (32k for state plus the longest question), streams nothing, and returns decisions rather than text or tool calls — which is why this is a tool and not a pi model provider. A llm backend answer is a general model's best effort, not a calibrated System One answer.