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nucleus_tasks

Manage priority tasks with escalation, human-in-the-loop gates, and cognitive depth tracking to prevent context-switch overhead and rabbit-holing.

Instructions

Manage a priority task queue with escalation, human-in-the-loop (HITL) gates, and cognitive depth tracking to prevent context-switch overhead and rabbit-holing. Use this tool when you need to create, assign, update, or track work items. Do NOT use for persistent knowledge storage (use nucleus_engrams), session management (use nucleus_sessions), or multi-agent coordination (use nucleus_agents). Actions: 'add' creates a new task with a priority level (critical/high/medium/low) and optional tags. 'list' shows tasks filtered by status — returns an array of task objects. 'get_next' returns the highest-priority unclaimed task. 'claim' assigns a task to the current agent (side effect: sets status to in_progress). 'update' changes task status (pending/in_progress/done/blocked) with optional notes. 'escalate' flags a task for human review with a reason. 'import_jsonl' bulk-imports tasks from a JSONL file. 'depth_push' increments cognitive nesting depth (tracks how deep into subtasks you've gone). 'depth_pop' decrements it. 'depth_show' returns current depth and max. 'depth_reset' clears depth to zero. 'depth_set_max' sets the maximum allowed depth — system warns when exceeded. 'depth_map' visualizes the full depth tree. 'context_switch' saves current task state and loads another task's context. All mutations write to .brain/tasks/. Prerequisites: .brain directory. Returns JSON with {success: boolean, data: object}. Example: {action: 'add', params: {title: 'Fix auth bug', priority: 'high', tags: ['backend']}} returns {success: true, data: {task_id: 'task_x1y2', created: true}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesSelect the task management action. 'add' creates a new task. 'list'/'get_next'/'depth_show'/'depth_map'/'context_switch_status' are read-only. 'claim' assigns a task to the current agent. 'update' changes task status. 'escalate' flags for human review. 'import_jsonl' bulk-imports from file. 'depth_push'/'depth_pop'/'depth_reset'/'depth_set_max' track cognitive nesting depth. 'context_switch' saves and restores working context between tasks.
paramsNoAction-specific parameters as key-value pairs. add: {title: string (required), description: string (optional), priority: string (optional, 'critical'|'high'|'medium'|'low', default 'medium'), tags: string[] (optional)}. update: {task_id: string (required), status: string (required, 'pending'|'in_progress'|'done'|'blocked'), notes: string (optional)}. claim: {task_id: string (required)}. escalate: {task_id: string (required), reason: string (required, why escalation is needed)}. depth_set_max: {max_depth: integer (required, typically 3-5)}. context_switch: {to_task_id: string (required)}. import_jsonl: {file_path: string (required, path to .jsonl file with task objects)}. list: {status: string (optional, filter by 'pending'|'in_progress'|'done'|'blocked'), limit: integer (optional, default 20)}. get_next/depth_push/depth_pop/depth_show/depth_reset/depth_map/context_switch_status/context_switch_reset: no parameters needed.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Disclosures beyond annotations include side effects (claim sets status to in_progress), file system writes to .brain/tasks/, prerequisites (.brain directory), and return format. No contradiction with annotations; description adds substantial behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is lengthy but well-organized with clear sectioning of actions. Every sentence contributes to understanding, though it could be slightly more condensed. Front-loaded purpose and usage guidance is good.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity with 15+ actions and nested parameters, the description covers all actions, parameter details, return JSON shape, and provides an example. No output schema exists, but the description explains return structure adequately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but description adds significant meaning by detailing action-specific parameter requirements including required fields, defaults, and optional parameters for each action. The example further clarifies usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it manages a priority task queue with escalation, HITL gates, and cognitive depth tracking. It explicitly distinguishes from sibling tools like nucleus_engrams, nucleus_sessions, and nucleus_agents, making the purpose precise and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use (create, assign, update, track work items) and when-not-to-use (persistent knowledge, session management, multi-agent coordination) with specific alternatives. This gives clear guidance for correct selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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