dsh-tool-goal gives the model three tools over the persisted goal service: get_goal reads the current goal, create_goal starts a new one, and update_goal edits, pauses, resumes, completes, or blocks it. The model may infer a long-running objective from a direct human request and create a goal; updates must carry the exact id and revision read beforehand. Authority is enforced at execution: create, edit, pause, and resume require a direct human turn on a top-level agent, while complete and blocked also accept the current goal round during automatic continuation. A configured threshold (default 3) bounds how soon an autonomous round may self-report blocked. Mount it with dsh-goal whenever the model should manage goals itself.

Use this package
Understand the implementation
Further Exploration
Model Experience
Known Limitations and Deferred Work
Dev Note

Use this package

Mount dsh-tool-goal beside the goal service when the model should create and update persisted goals itself. The tools are the model-facing half of the goal surface; the /goal command is the human-facing half, and the continuation driver uses the same tools to complete or block goals at the end of autonomous rounds.

Tools

All three tools return the same compact JSON — { goal: null } when no goal is current, otherwise the goal's id, revision, objective, phase, rounds started, round cap, optional blocker reason, and whether continuation is armed — matching what Native callers already render.

Tool	What it does
`get_goal()`	Reads the current goal, or `null` when none is current
`create_goal(objective, max_goal_rounds?)`	Creates one goal from a direct top-level human turn
`update_goal(goal_id, revision, action, objective?, max_goal_rounds?, blocked_reason?)`	`edit`, `pause`, `resume`, `complete`, or `blocked` on the exact goal revision

Call get_goal before update_goal and copy the exact goal_id and revision; all calls are exclusive, so a model-ordered batch observes earlier mutations and their new revisions. Replacements belong only to edit; blocked_reason is required only for blocked and is persisted with the stable code model-reported. Strict-schema empty-string and zero fillers count as omitted, while meaningful values remain limited to their action.

Configure it

- id: tool-goal
  name: '@deepseek-ai/dsh-tool-goal'
  config:
    blockedAfterConsecutiveRounds: 3

The value must be a positive safe integer. It supplies both the hard lower bound on model self-blocking and the number named in model guidance. The generated configuration catalog is the exhaustive source for every accepted field.

Authority rules

The tools execute only for the exact live calling agent inside its active driver with an open turn. create, edit, pause, and resume additionally require a direct human message in a runtime-root agent's current turn — a subagent or a non-human producer cannot create or edit goals. complete and blocked also accept the exact current goal round: a goal-sourced round may complete the goal immediately, but a blocked call is mechanically rejected until the configured number of consecutive rounds has passed — the model judges whether the same condition actually persisted and must describe it in blocked_reason. A direct human request may stop a goal immediately.

An autonomous round that successfully reports complete or blocked also ends the physical turn after that step, and the model receives a closing instruction to write the final message to the user. Direct-human mutations never trigger that stop: the assistant may acknowledge the change and the loop keeps concurrent human steering available.

Understand the implementation

Implementation internals — click to expand

This section explains how the tools enforce authority and render output; the observable contract is covered in Use this package.

Design

Authority at execution. Every call resolves the exact live agent, its inherited AgentRegistry initiator, running status, and an open turn; create, edit, pause, and resume additionally require an accepted { kind: 'user' } message or steering event in a runtime-root agent's current turn. Durable fork lineage does not demote a resumed root; live subagent ownership does.
Host attestation of human input. { kind: 'user' } is assigned by Agent.followup() and steer() when their caller omits a source, so plugins, schedulers, and other non-human producers must pass their own source rather than inheriting human authority.
System-prompt guidance with the configured threshold. The package registers one tool:goal system-prompt section whose fixed text interpolates blockedAfterConsecutiveRounds; the same value is the hard lower bound enforced at execution.
Wrap-up context for terminal rounds. A successful autonomous complete or blocked defers a closing <goal_complete> or <goal_blocked> instruction so the model addresses the user once before the turn ends; direct-human mutations never defer this context.

Source map

File	Role
`src/index.ts`	Plugin entry: tool registration, config, system-prompt section, result rendering
`src/authority.ts`	Execution-time authority checks and goal-round acceptance
`src/wrapup.ts`	Closing-message instruction for terminal autonomous updates
`src/invariant.ts`	Invariant companion: empty (no runtime invariant — the goal domain owns accepted mutations)

Tool output

All three tools share one canonical output: the compact JSON { goal: null } or { goal: { id, revision, objective, phase, roundsStarted, maxGoalRounds, blockedReason? }, activation }. activation in a result is a live observation and never becomes replay authority. UI clients receive pure generic cards — read for get_goal, other for mutations.

Further Exploration

The tools are the model-facing half of the goal surface; read these pages for the state they mutate and the policy they defer to.

Goal service — the goal state and lifecycle the tools mutate.
Goal group map — the goal packages and how they compose.
Generated tool catalog — the exact schemas the model receives.
Goal-tool Agent Note — the authority split and UX decisions.

Model Experience

System prompt

What the model sees

A fixed goal policy says when semantic human intent warrants creation, requires exact read-before-update refs, explains rearming after resume/fork, and limits completion/blocking claims. The configured threshold is interpolated into that guidance.

Goal policy

Use goal tools for one long-running completion objective in the current session. create_goal may infer goal intent from a direct human request in any language; do not create a goal for routine single-turn work. Call get_goal before update_goal and copy its exact goal_id and revision. After session resume or fork, an active goal is disarmed: when a human asks to continue or resume in any wording or language, use update_goal action resume to rearm it. Mark complete only when the objective is actually achieved. Mark blocked only after the same blocking condition persists for at least 3 consecutive goal rounds, and report that concrete condition in blocked_reason; difficulty, uncertainty, or useful remaining work is not blocked.

Token effect

Small fixed input cost on every request where this plugin's prompt registration is in scope.

KV Cache effect

Prefix-stable while the plugin scope, configured threshold, and guidance text are unchanged. Activation, disposal, or configuration changes may invalidate reuse from this prompt section.

Tool schemas and results

What the model sees

The generated get_goal, create_goal, and update_goal schemas. Successful results are compact JSON. A mutation appends the goal domain's durable goal/change event without queuing model context. activation in a result is a live observation and never becomes replay authority.

Token effect

Fixed schema cost plus one compact result per call. The durable mutation adds no separate model-visible context.

KV Cache effect

Schemas are prefix-stable while their definitions and visibility are unchanged. Calls and results append after the reusable request prefix without invalidating earlier entries.

Known Limitations and Deferred Work

These limits define when the goal tools are a poor fit or need special care. They are current package constraints, not a task backlog.

Semantic intent remains model judgment — execution can prove that the current turn contains a direct human message, not whether the request is substantial enough to merit a goal.
Same-condition blocking remains model judgment — the runtime enforces distinct admitted-round count, not semantic equivalence of obstacles; an independent evaluator is deferred.
No scheduling or direct human rendering — these tools mutate state only; the same-session driver and dsh-command-goal are independent consumers of the same domain.
Goal-round authority requires a driver — the autonomous complete/blocked path is dormant unless a continuation driver admits goal-sourced user turns; mounting this tool package alone does not create them.
Prompt registration is independent of filtering — a scope may hide the tools while retaining their guidance unless the deployment scopes both registrations together.

Dev Note

Working context for maintainers — click to expand

This Dev Note is working context for maintainers; it is explicitly non-authoritative. Open question: whether the goal-policy section should be independently scoped from the tool registrations, so a scope cannot hide the tools while keeping the guidance.

README.md

@deepseek-ai/dsh-tool-goal

Summary

Table of Contents

Use this package

Tools

Configure it

Authority rules

Understand the implementation

Design

Source map

Tool output

Further Exploration

Model Experience

System prompt

What the model sees

Goal policy

Token effect

KV Cache effect

Tool schemas and results

What the model sees

Token effect

KV Cache effect

Known Limitations and Deferred Work

Dev Note