Jev DSH Decision plugin
A plugin for DeepSeek Harness and iPolloWork: structured choice of tools, skills and agents and scoring of outputs
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Extended access
Why this level
- The task and relevant context go to the TypeSafe API under your key, so secrets and whole files should not be sent
- The connection check makes a billable request, and the `TYPESAFE_API_KEY` key lives in DSH credentials or in Work
Install
Manual install
dsh plugin add /absolute/path/jev-dsh-decisionFirst `git clone https://github.com/Devin-AXIS/jev-dsh-decision.git`. Replace the path with the clone directory and set `TYPESAFE_API_KEY` in the DSH credentials.
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- The task and relevant context go to the TypeSafe API under your key, so secrets and whole files should not be sent
- The connection check makes a billable request, and the `TYPESAFE_API_KEY` key lives in DSH credentials or in Work
This is third-party code. Review the repository files before installing.
What it does
The plugin connects TypeSafe AI's Jev decision model to an agent. Jev is not a chat model: it returns a structured answer with the chosen option, a probability, a confidence and the usage. The agent stays in charge and still plans, builds parameters and runs tools, while Jev helps choose among the available tools, skills and subagents or scores a result against given criteria. The plugin gives three actions. `jev_status` shows the local configuration, `jev_check_connection` makes a real billable test request and `jev_evaluate` submits questions. Questions come in three types: `choice` picks one of 2–255 options, `score` grades on 2–10 levels and `noul` estimates the probability of a yes or no condition. Independent questions can be sent as a batch, capped at 64 questions and 256 KiB per request. The `jev-decision` skill ships with the plugin and explains to the agent how to prepare candidates and criteria. DeepSeek Harness has a native `dsh.bundle` format, and iPolloWork has a signed `.ipollowork-plugin` package through which Jev reaches OpenCode and Codex Harness.
Who it is for. Developers who build agents on DeepSeek Harness or iPolloWork and want to hand tool selection to a dedicated decision model.
Good fit when
- The agent has dozens of available skills and you want the best one chosen with a probability rather than a model's hunch
- Several independent candidate answers must be scored against set criteria and you want the grades in one batch
- You need to test a yes or no condition and learn the confidence in the estimate
- One decision module has to work in both DeepSeek Harness and iPolloWork for different engines
Not a fit when
- You need a conversational partner or a text generator: Jev is not a chat model and returns no reasoning text
- You need the chosen action to run automatically: the plugin executes nothing and does not switch models
- A TypeSafe API key is unavailable: without `TYPESAFE_API_KEY` evaluation does not work
- The engine is Claude Code or Cursor: the repository has no standalone install for them
Example request
Use Jev: first check the configuration and test the connection, then choose the skill for building a presentation from the gathered materialLimitations
- You need your own `TYPESAFE_API_KEY` or authorization in the iPolloWork plugin. The connection test spends a small amount of paid API.
- OpenCode and Codex Harness are supported only through iPolloWork, with no packages of their own.
- The `.ipollowork-plugin` package is accepted by clients that registered the `ipollowork-2026` public signing key, and older clients reject the import.
- The plugin works on available and permitted candidates: Jev itself finds, installs and permits nothing.
- The README is in Chinese and the current package version is 0.1.2.
How to disable. In DeepSeek Harness run `dsh plugin remove dsh-jev-decision-model`. The `TYPESAFE_API_KEY` stays in the credential store and is removed by hand. In iPolloWork disable and delete the plugin on the extensions page.
FAQ
How do I install the plugin in DeepSeek Harness?
Clone the repository and run `dsh plugin add /absolute/path/jev-dsh-decision`. Set `TYPESAFE_API_KEY` in the DSH credential store and restart the session.
What does configured mean in jev_status?
Only that a key was found locally. `jev_check_connection` proves it works by making a real request that spends a little paid API.
Which questions does jev_evaluate accept?
Three types: `choice` picks one of 2–255 options, `score` grades on 2–10 levels and `noul` estimates the probability of a condition. A batch can hold up to 64 questions.
Does Jev replace the agent's planning?
No. The agent still plans, builds parameters and runs tools. Jev returns only a structured decision: option, probability, confidence and usage.
Does the plugin work in OpenCode and Codex?
Only through iPolloWork: import the `.ipollowork-plugin` package once and it is available to OpenCode, DeepSeek Harness and Codex Harness. There are no packages of their own.
Discussion
No questions or reviews yet.
Related
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README in short
The Chinese README presents the plugin as a structured decision engine for an agent harness and shows a table of environments: DeepSeek Harness, iPolloWork, OpenCode and Codex Harness. It continues with capabilities, installation into DSH and iPolloWork, key setup, verification and development and test instructions.
README badge
Original SKILL.md
---
name: jev-decision
description: Use Jev to select among available tools, skills or agents, or evaluate outputs against explicit rubrics. Use when the user requests Jev or a bounded decision would help; not for open-ended writing or research.
---
# Jev DSH 决策引擎
Jev returns structured judgments. The current Agent retains planning, parameter construction, execution and native subagent collaboration.
## Call through the current host
- In iPolloWork, discover `jev-decision-model` with `ipollowork_extension_list_actions`, then use `ipollowork_extension_call` with action `evaluate` and the payload below in `args`.
- In standalone DSH, use `jev_evaluate` with the same payload. `jev_status` checks local configuration; `jev_check_connection` verifies the key with a small billable request.
- Work also exposes `status` and `check-connection`. A configured key is not proof of a successful API connection. If missing or invalid, direct the user to plugin authorization (Work) or the `TYPESAFE_API_KEY` credential (DSH); do not request the key in chat.
## Choose and prepare
Discover actual candidates from the host or the visible tool/skill catalog. Respect the user's explicit selections. Include only available, permitted candidates and their stable IDs, short descriptions and relevant constraints. Jev does not discover tools, install plugins, grant permissions or invoke another Agent by itself.
Send the task and minimal relevant context as `state`; do not send credentials, entire transcripts or complete files unnecessarily. Each question contains `type`, a complete `instructions` question, and the appropriate `criteria`. Question IDs identify results; they are not instructions to the model.
- `choice`: choose one of 2–255 keyed candidates; include an `other` / `none` candidate when needed.
- `score`: grade against 2–10 ordered levels, indexed from zero. Several independent scores can rank candidates.
- `noul`: estimate the probability of a clearly stated yes/no condition. To select multiple complementary skills, batch one relevance question per candidate instead of forcing a single Choice. A value near 0.5 means uncertainty, not medium skill.
Batch independent questions sharing a state; use a later request if a question depends on an earlier result. Plugin bounds are 64 questions and 256 KiB per request. These are plugin limits, not claims about the provider's limits.
```json
{
"state": { "task": "整理已收集的竞品资料,制作汇报 PPT", "available": ["research", "slides"] },
"questions": {
"use_research": { "type": "noul", "instructions": "这项任务是否需要分析竞品资料?" },
"use_slides": { "type": "noul", "instructions": "这项任务是否需要制作演示文稿?" }
}
}
```
## Use the result
Preserve the returned probabilities, confidence, model and usage when reporting. Jev does not return written reasoning: distinguish your explanation from its structured result. Check current availability again before execution. Read the selected Skill and construct tool arguments using the current host's schemas.
Thresholds and weights come from the user's task or application policy, not universal magic numbers. Low confidence, missing options, or API failure calls for ordinary Agent reasoning or clarification; say when Jev was unavailable instead of inventing its result. A recommendation does not authorize external actions, override the user's choice or bypass approval. No automatic global routing or self-training is installed by this plugin.
Reference: https://docs.typesafe.ai/api