planning-tasks

name	planning-tasks
description	Generates or refines Acceptance Criteria and Execution Plans for tasks. Single task or batch mode. Ensures tasks are executable at agent-autonomous quality. Uses multi-round brainstorming to extract quality information from users. Triggers: "plan task", "refine task", "generate AC", "write execution plan", "plan all tasks", "auto-plan", "タスク計画", "AC作成", "実行計画作成", "タスクを計画して"
user-invocable	true

Agentic Tasks — Task Planning

You generate and refine Acceptance Criteria (AC) and Execution Plans for tasks. Your goal is to make tasks executable at agent-autonomous quality — detailed enough that an AI agent can complete them without additional questions.

Provider Detection (once per session)

Load ${CLAUDE_PLUGIN_ROOT}/skills/detecting-provider/SKILL.md and determine active_provider. Skip if already set. Then read ${CLAUDE_PLUGIN_ROOT}/skills/providers/{active_provider}/SKILL.md for query path detection. Load ${CLAUDE_PLUGIN_ROOT}/skills/resolving-identity/SKILL.md and resolve current_user. Skip if already set.

Target Selection

Three modes of operation:

Single Task

User specifies a task by title, ID, or description. Search the Tasks DB and confirm the match.

Batch Mode

User says "plan all Backlog tasks" or similar. Query by status filter.

Pipeline Mode

Receives a list of task IDs from another skill (e.g., running-daily-tasks). Process each task in the list.

Planning Flow

For each task, determine the planning path:

Path A — [Hearing] Tasks (deterministic, no agent needed)

If the task title starts with [Hearing]:

• AC: "Confirm with {person} about {topic_from_title}. Record response in Agent Output. Update Status to Done when confirmed."
• Execution Plan: "1. Contact {person} via messaging tool\n2. Ask about: {topic}\n3. Record response in Agent Output\n4. Update Status to Done"
• Update the task immediately. No user confirmation needed (deterministic template).

Path B — All Other Tasks (agent-delegated)

1. Classify the task:

   • Has Working Directory or Repository set → dispatch to code-planning-agent
   • Otherwise → dispatch to knowledge-planning-agent

2. Check minimum input threshold:

   • If Description has no nouns or meaningful context (e.g., just "fix bug" with no other info): escalate to user before spawning agent: "I need more context to plan this task. What specifically needs to happen?"
   • If user provides more context: update Description, then proceed
   • If user declines: skip this task

3. Spawn the appropriate planning agent via the Agent tool:

   • Provide: Title, Description, Context, AC (if partial), Working Directory, Repository
   • The agent follows the Multi-round Brainstorming Protocol (see below)
   • The agent returns: generated AC + Execution Plan as structured text

4. Present agent output to user for final confirmation:

   • Show the generated AC and Execution Plan
   • Options: [Accept] [Edit] [Skip]
   • If Accept: update the task via provider
   • If Edit: let user modify, then update
   • If Skip: leave task unchanged

5. Validation gate: Run the validation script on each updated task:

   bash ${CLAUDE_PLUGIN_ROOT}/skills/validating-fields/scripts/validate-task-fields.sh \
     "Ready" /tmp/planned_task.json
   

   Report which tasks are now Ready-eligible.

Batch Execution

When processing multiple tasks:

• Spawn agents in parallel (up to maxConcurrentAgents)
• Present all results together for bulk confirmation
• Summary: "Planned N tasks. M Ready-eligible. K need more context."

Multi-round Brainstorming Protocol

This protocol is embedded in the planning agent prompts. The agent drives the conversation:

Round 1: Agent proposes an initial AC draft based on Title + Description + Context.
  → "Based on your task, I propose these completion criteria:
     1. {criterion 1}
     2. {criterion 2}
     3. {criterion 3}
     What would you add or change? You can also describe your own."

Round 2 (if user response lacks verifiable conditions):
  → Agent refines: "I understood X. Let me also suggest:
     - {additional criterion based on user input}
     - {edge case consideration}
     Anything else? What about error cases or edge conditions?"

Round 3 (continue if user is engaged):
  → Synthesize: "Here's the complete checklist:
     1. {final criterion 1}
     2. {final criterion 2}
     ...
     Anything missing?"
  → If user says "done" / "OK": finalize
  → If user adds more: incorporate and re-present

Fallback (user disengages — "もういい", "適当でいい", etc.):
  → Accept current state with [LOW CONFIDENCE] tag prepended
  → Move on to next task

Key principle: The agent PROPOSES first, then refines through dialogue. Never wait for the user to provide content from scratch — generate drafts proactively.

Semantic triggers: Round 2 fires when the user's response lacks verifiable conditions (no commands, file paths, metrics, or observable outcomes) — not based on character count.

Execution Plan Generation

After AC is finalized, generate the Execution Plan:

• Code tasks: The code-planning-agent has already explored the codebase and generates steps with specific file paths, test commands, and module references.
• Non-code tasks: The knowledge-planning-agent generates a numbered plan using domain templates (see references/knowledge-work-patterns.md).
• Each step: action verb + target + expected outcome
• If >7 steps: suggest splitting the task into multiple tasks

Summary Output

[Planning Complete]
Tasks processed: N
AC generated: X
Execution Plans generated: Y
Ready-eligible: Z (passed validation)
Skipped: K (insufficient context or user declined)

Language

Always respond in the user's language. Write AC and Execution Plans in the user's language.