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expand-tasks

// Expand all TaskMaster tasks with deep research via Perplexity before coding begins. Reads tasks.json, launches parallel research agents per task, writes findings back. Part of the prd-taskmaster toolkit. Use after PRD is parsed into tasks and before implementation begins. Invoke with /expand-tasks o

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updated:February 16, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
nameexpand-tasks
descriptionExpand all TaskMaster tasks with deep research via Perplexity before coding begins. Reads tasks.json, launches parallel research agents per task, writes findings back. Part of the prd-taskmaster toolkit. Use after PRD is parsed into tasks and before implementation begins. Invoke with /expand-tasks or when user says "expand tasks", "research all tasks", or "research before coding for all tasks".
allowed-toolsRead,Write,Edit,Bash,Task,Glob,Grep,AskUserQuestion

Expand Tasks with Research v1.0

Expands TaskMaster tasks with Perplexity 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), Perplexity MCP proxy

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)
  • Perplexity proxy must be running at localhost:8765
  • 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 Perplexity is down: Check health first:

curl -sf http://localhost:8765/health

If down, tell user to start it and exit.

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 Perplexity proxy

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 perplexity_batch or mcp__perplexity-api-free__search with 3-5 targeted queries
  3. Agent distills results into structured summary
  4. Summary returns to main context (~25-40 lines per task)

Critical: Research agents must NEVER use WebSearch or WebFetch. Perplexity MCP only.

Error Handling

ErrorAction
Perplexity proxy downExit skill, tell user to start proxy
Agent returns empty/failedRe-run that specific task with different queries
tasks.json not foundExit skill, tell user to run /prd-taskmaster first
Task already expandedSkip silently unless user forces re-expansion
Agent timeoutMark task as failed, continue with others

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 costs ~30s of Perplexity time; 15 tasks ≈ 3 waves ≈ 2-3 minutes total