dsh-typesafe-ask
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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
Set in the profile's plugin row when you need to change defaults:
- id: dsh-typesafe-ask
name: 'dsh-typesafe-ask'
config:
apiKeyRef: TYPESAFE_API_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
