dsh-typesafe-ask

English | 中文

Give your DeepSeek Harness agent a decision oracle: typesafe_ask calls TypeSafe Jev, a System One model that evaluates a state against typed questions and returns structured answers — a choice from a list, a score on a rubric, or a probability that a statement is true — with calibrated probabilities attached.

Use it when the agent needs a judgment rather than a sentence: route a request to the right handler, classify a ticket, score how urgent or how frustrated something is, rank candidates, or gate an action behind a confidence threshold.

What you get

Surface	What it does
Tool typesafe_ask	Sends state + a map of typed questions to POST /v1/systemone and returns the answers in the conversation
Settings section	Settings → TypeSafe / Jev — paste your API key once; it is stored through the harness credential store, never in a profile config file

Install

From the plugin market: Settings → Plugin Market → search typesafe-ask → Install. Or by hand:

dsh plugin --profile <your-profile> add dsh-typesafe-ask

Then restart or refresh the harness, open Settings → TypeSafe / Jev, and paste a key from console.typesafe.ai/keys.

Use

Once the key is set, just ask the agent in natural language ("which team should own this ticket?") or let it call the tool itself. The tool takes:

Parameter	Type	Required	Meaning
state	string | object | array	yes	What to evaluate: the text, record, or application state
questions	object	yes	Map of question id → typed question (see below)
model	string	no	Defaults to jev-latest

Three question types:

{
  "department": {                     // choice — pick one option
    "type": "choice",
    "instructions": "Which team should handle this",
    "criteria": {
      "billing": "Payment or subscription issues",
      "technical": "Bugs or integration problems",
      "sales": "Pricing or account questions"
    }
  },
  "frustration": {                    // score — position on an ordered rubric
    "type": "score",
    "instructions": "How frustrated the customer appears",
    "criteria": ["Calm, just stating facts", "Frustrated but civil", "Very angry, strong language"]
  },
  "is_urgent": {                      // noul — probability the statement is true
    "type": "noul",
    "instructions": "The message conveys urgency or time-sensitivity"
  }
}

Answers come back keyed by your question ids:

department: choice=technical (confidence=0.78) [technical=0.85, billing=0.15, sales=0]
frustration: score=1=Frustrated but civil (confidence=1)
is_urgent: noul=0.99

Designing good questions

• Ask independent questions together. Every question in one call is evaluated in parallel against the same state and cannot see the other answers. Send a second call only when an earlier answer is needed to build new state or new options.
• One narrow, coherent judgment per question. Split independent dimensions; do not destroy the relationship being judged. Question ids are for your code and are never sent to the model, so the full meaning must live in instructions.
• noul has no separate confidence. If several labels can hold at once, ask one noul per label instead of forcing a choice.
• score levels must stand on their own as concrete situations, not "low/medium/high". The returned score is a probability-weighted position and may be fractional.
• Low confidence means the options overlap, not that the model is broken. For choice, go back and make criteria mutually exclusive rather than lowering a threshold.
• Thresholds belong to your data. Calibrate them on real cases; noul ≈ 0.5 means the yes/no split is even, not "medium intensity".

Configuration

Defaults live in the plugin (apiKeyRef → TYPESAFE_API_KEY, model → jev-latest, endpoint and timeout as documented above), so the plugin row itself is a bare insert — which is what lets the market hot-mount it without a restart. To change a default, target the row from a later patch layer, i.e. your profile's own cordis.patch.yml:

- id: dsh-typesafe-ask
  config:
    apiKeyRef: MY_TYPESAFE_KEY                           # credential reference name
    model: jev-latest
    baseUrl: https://api.typesafe.ai/v1/systemone
    timeoutMs: 60000

Boundaries

• Jev does not generate text, write code, stream, or call tools. It cannot replace the chat model behind your agent — TypeSafe says so explicitly in Jev with coding agents.
• The key is a server-side secret: it stays in the credential store and is only read on the host when a tool call is made.
• Requires network access to api.typesafe.ai.

Development

The plugin is plain ESM with no build step — what is in src/ is what ships.

# make the dsh packages resolvable for the offline tests (symlink, not installed)
ln -s "$DSH_NODE_MODULES" node_modules   # e.g. the dsh dependency's node_modules

npm test          # host + client + live client-module checks

• test/host.test.mjs — mounts the plugin on a stub context, registers the tool through the real defineTool, runs a live /v1/systemone call, and covers the config override, the missing-key path, invalid questions, and a deployment with no credential store.
• test/client.test.mjs — loads src/client.js through the same window.__ModuleLoader__.load({ id, factory }) contract the dsh web shell uses, asserts the bundle requires only baseline modules, mounts it on a stub context, checks the settings.section registration and the remote.credentials call shapes, and renders the page with real React.
• test/smoke.mjs — the API client alone, against a real key ($TYPESAFE_API_KEY, or a path as the first argument).

Test the whole loop against a local dsh install:

dsh plugin --profile <profile> add /absolute/path/to/dsh-typesafe-ask
# restart the harness: a new bundle row is composed at boot

License

MIT