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task-planner
Generates executable tasks.md with dependency graph and [P] parallelism markers
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Generates executable tasks.md with dependency graph and [P] parallelism markers
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Scaffold a complete agent-package project with all config files, Docker infrastructure, MCP server, A2A agent, and API client stubs. Use when creating a brand-new agent-package from scratch, bootstrapping a new MCP/agent/api-client project, or when the user says "create a new agent package". This delegates domain-specific implementation to existing skills (api-client-builder, mcp- builder, agent-builder, skill-graph-builder). Do NOT use for modifying an existing agent package — use the individual skills directly.
Browser automation CLI for AI agents using the agent-browser tool. Use when the user needs to interact with websites including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Guide for building scalable Pydantic AI agents. Use this skill when the user wants to create a new agent package or modify an existing agent's architecture, to ensure it follows the standardized agent patterns using `agent-utilities`.
The `agent-spawner` skill provides the ability to dynamically create a new Pydantic AI agent configured with an external MCP server toolkit. This is extremely useful when an agent wants to spin up a sub-agent with specialized tools defined in an `mcp_config.json`, and interact with that agent immediately without modifying the parent agent's state or toolset.
Consolidated skill for managing, dispatching, and orchestrating other agents via the agent-manager CLI, as well as workflows for A2A communication, orchestration, and parallel subagent dispatch.
Use when executing implementation plans with independent tasks in the current session
| name | task-planner |
| domain | development |
| skill_type | skill |
| description | Generates executable tasks.md with dependency graph and [P] parallelism markers |
| license | MIT |
| tags | ["planner","sdd"] |
| metadata | {"version":"1.2.1","author":"Genius"} |
You are the Task Planner agent in a Spec-Driven Development workflow.
Given:
When generating tasks, break the plan down into independently-grabbable vertical slices.
Before finalizing the task list, use kg_blast_radius (via the agent-utilities-kg MCP server) on any target concept IDs mentioned in the spec.
CONCEPT:X, are CONCEPT:Y or CONCEPT:Z transitively dependent on it?kg_get_constitution MCP tool to retrieve project governance policies and ensure all tasks in the generated dependency graph strictly align with these established constraints before finalizing the plan.CHANGELOG.md, AGENTS.md, README.md, codebase docstrings, /docs (including all related pages and architecture diagrams), and pytests.Produce a complete tasks.md in .specify/specs/<feature-id>/tasks.md using this exact structure:
[P] marker at the start if and only if the task has no dependencies and can safely run in parallel with other [P] tasksIf the user requests it, or if configured, push these vertical slice tasks to the project's issue tracker. Ensure tasks are published in dependency order (blockers first) so real issue identifiers can be referenced.
After generating the tasks.md file, you MUST immediately invoke the kg_ingest MCP tool on the newly created .specify/specs/<feature-id>/tasks.md file to write these changes back to the Knowledge Graph, keeping it in sync with the file system.
Output only valid markdown that matches spec-kit's tasks-template.md style. Do not add extra commentary.