Expand Tasks

Expand Tasks with Research v1.0

Expands TaskMaster tasks with research before coding begins. Deterministic operations handled by script.py; AI handles judgment.

Script location: ~/.claude/skills/expand-tasks/script.py Part of: prd-taskmaster toolkit Depends on: research-before-coding skill (pattern), any research provider configured via task-master models --set-research or registered as an MCP research tool

When to Use

Activate when user says: expand tasks, research tasks, research before coding for all, expand subtasks. Do NOT activate for: single task research (use /research-before-coding), PRD generation (use /prd-taskmaster).

Prerequisites

• TaskMaster tasks.json must exist (run /prd-taskmaster first)
• A research provider is configured — either (a) task-master models --set-research <model> --<provider> for any task-master provider family, or (b) an MCP research tool registered in ~/.claude.json that Claude Code can call directly
• At least 1 task in tasks.json

Workflow (5 Steps)

Step 1: Preflight

python3 ~/.claude/skills/expand-tasks/script.py read-tasks

Returns JSON: total, expanded, pending_expansion, tasks[].

If pending_expansion is 0: Report all tasks already expanded. Exit skill.

If research provider is not configured: Check via task-master models and verify a research role is set. If none, tell the user to configure one (task-master models --set-research <model> --<provider>) and exit. The skill does not assume any specific research backend — it uses whatever is configured.

Step 2: Choose Scope

Use AskUserQuestion:

• All tasks (default): Expand every task that hasn't been researched yet
• Specific tasks: User provides task IDs (comma-separated)
• By dependency level: Expand tasks with no dependencies first, then next wave

AI judgment: Recommend "All tasks" for initial expansion, "By dependency level" for incremental work.

Step 3: Generate Research Prompts

For each task to expand:

python3 ~/.claude/skills/expand-tasks/script.py gen-prompt --task-id <ID>

Returns JSON with prompt field containing the full research agent prompt.

AI judgment: Review the auto-generated prompt. Customize research questions if the task needs domain-specific queries. Add project context from the PRD or session-context files if relevant.

Step 4: Launch Parallel Research Agents

Launch research agents in parallel waves. Each wave = up to 5 concurrent agents.

For each task, spawn a Task agent:

Task(
  subagent_type: "general-purpose",
  model: "sonnet",
  description: "Research Task <ID>: <title>",
  run_in_background: true,
  prompt: <prompt from Step 3>
)

Wave strategy:

• Wave 1: Tasks with no dependencies (they inform downstream tasks)
• Wave 2: Tasks depending on Wave 1
• Wave 3+: Continue until all tasks covered
• Max 5 agents per wave to avoid overwhelming the configured research backend

Wait for each wave to complete before launching the next.

Step 5: Collect and Write Results

As each agent completes, save its research output:

1. Write agent output to a temp file:

   echo "<agent output>" > /tmp/research-task-<ID>.md
   

2. Write research back to tasks.json:

   python3 ~/.claude/skills/expand-tasks/script.py write-research --task-id <ID> --research /tmp/research-task-<ID>.md
   

3. After all tasks are written, verify:

   python3 ~/.claude/skills/expand-tasks/script.py status
   

AI judgment: Review each research result for quality. If a result is too thin (< 5 lines of useful content) or clearly failed, re-run that specific task's research.

Research Agent Prompt Pattern

The gen-prompt command generates prompts that follow the research-before-coding pattern:

1. Agent receives task context (title, description, dependencies, subtasks)
2. Agent runs 3-5 targeted queries against the user's configured research provider. The agent is tool-agnostic: it picks up whichever research tools are available in the current Claude Code session. This may be task-master research, an MCP search/reason tool from ~/.claude.json, WebSearch as a last resort, or whatever the user has bound. The skill does not hard-code any specific research MCP.
3. Agent distills results into structured summary
4. Summary returns to main context (~25-40 lines per task)

Critical: prefer structured research tools (task-master research, MCP search/reason tools) over raw WebSearch/WebFetch when available — they produce cleaner outputs with citations.

Error Handling

Output

After all tasks are expanded, the skill reports:

• Total tasks expanded
• Any failures that need retry
• Next recommended action (usually: begin implementation)

Integration with prd-taskmaster

This skill fits between Step 8 (Parse & Expand Tasks) and Step 11 (Choose Next Action) of the prd-taskmaster workflow. After PRD is parsed into tasks but before execution begins.

/prd-taskmaster → generates PRD → parses into tasks
    ↓
/expand-tasks → researches each task → writes findings back
    ↓
Implementation begins (with research context in each task)

Tips

• Run after PRD generation but before any implementation
• Research results are stored in research_notes field of each task in tasks.json
• Re-running on already-expanded tasks is safe (will skip unless forced)
• For very large task lists (20+), consider expanding in dependency order to save context
• Each research agent typically completes in ~30s depending on research backend and query depth; 15 tasks ≈ 3 waves ≈ 2-3 minutes total