", run_in_background: true, prompt: <prompt from Step 3> ) ``` **Wave strategy**: - Wave 1: Tasks with no dependencies (they inform downstream tasks) — run in parallel - Wave 2: Tasks depending on Wave 1 — run in parallel - Wave 3+: Continue until all tasks covered — run in parallel per wave - Max 5 agents per wave to avoid overwhelming the configured research backend **Wait for each wave to complete before launching the next.** Parallel dispatch only happens WITHIN a wave; waves themselves are serial. --- ### Step 5: Collect and Write Results As each `research-expander` agent completes, save its research output: 1. Write agent output to a temp file: ```bash cat > /tmp/research-task-<ID>.md <<'EOF' <agent output> EOF ``` 2. Write research back to `tasks.json`: ```bash python3 skills/expand-tasks/script.py write-research --task-id <ID> --research /tmp/research-task-<ID>.md ``` 3. After all tasks are written, verify: ```bash python3 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 through a fresh `research-expander` invocation. --- ## 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 `research-expander` 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` (including any `mcp__plugin_prd_go__*` tools registered by this plugin), 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 | Error | Action | |-------|--------| | Research provider unreachable or rate-limited | Exit skill, tell user to verify `task-master models` research role is set and reachable | | `research-expander` agent returns empty/failed | Re-run that specific task with different queries | | `tasks.json` not found | Exit skill, tell user to run `/prd:go` first | | Task already expanded | Skip silently unless user forces re-expansion | | Agent timeout | Mark 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:go → generates PRD → parses into tasks ↓ /expand-tasks → research-expander agents run in Parallel waves → writes findings back to tasks.json ↓ 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-expander` agent typically completes in ~30s depending on research backend and query depth; 15 tasks ≈ 3 waves ≈ 2-3 minutes total

--- name: Expand Tasks slug: expand-tasks category: AI Engineering description: Expand Tasks researches TaskMaster tasks before implementation by reading tasks.json, spawning parallel research agents in waves, and writing findings back. Use it after PRD parsing and before coding starts. github: "https://github.com/anombyte93/prd-taskmaster/tree/main/skills/expand-tasks" language: Python stars: 591 forks: 58 install: "npx degit https://github.com/anombyte93/prd-taskmaster/tree/main/skills/expand-tasks ~/.claude/skills/expand-tasks" installs_to: ~/.claude/skills/expand-tasks source_path: skills/expand-tasks/SKILL.md collection_size: 11 category_size: 2451 collection_url: "https://dirskills.com/collections/anombyte93/prd-taskmaster" added: 2026-08-25T05:13:37.155Z last_synced: 2026-08-25T05:13:37.155Z canonical_url: "https://dirskills.com/skills/expand-tasks" --- # Expand Tasks Expand Tasks researches TaskMaster tasks before implementation by reading tasks.json, spawning parallel research agents in waves, and writing findings back. Use it after PRD parsing and before coding starts. **Install:** ```bash npx degit https://github.com/anombyte93/prd-taskmaster/tree/main/skills/expand-tasks ~/.claude/skills/expand-tasks ``` ## README # 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**: `skills/expand-tasks/script.py` (relative to plugin root) **Part of**: `prd-taskmaster` plugin **Depends on**: `research-expander` agent (parallel research worker), 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:go). ## Native-parallel first (token economy) Before launching agent waves, check the cheaper path: the native engine expands tasks in parallel for free. Prefer `python3 script.py expand` — backend op expand (native api) — or the `expand_tasks` MCP tool: it runs structured `expand` across pending tasks concurrently (inheriting the engine's ThreadPoolExecutor) on economy-tier models / keyless host CLIs and merges atomically. Use THIS skill's agent waves when: no provider/CLI is available, native expand reports failures for specific tasks (rerun just those here), or the research must be repo-grounded (agents can read the codebase; native expand cannot). ## Prerequisites - TaskMaster `tasks.json` must exist (run `/prd:go` first) - A research provider is configured — either (a) `task-master models --set-research --` for any task-master provider family, or (b) an MCP research tool registered in `~/.claude.json` that Claude Code can call directly (for example `mcp__plugin_prd_go__*` tools or an external search/reason MCP) - At least 1 task in `tasks.json` --- ## Workflow (5 Steps) ### Step 1: Preflight ```bash python3 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 --`) 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: ```bash python3 skills/expand-tasks/script.py gen-prompt --task-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 using the dedicated `research-expander` subagent type (defined in `agents/research-expander.md`): ``` Task( subagent_type: "research-expander", description: "Research Task : ", run_in_background: true, prompt: <prompt from Step 3> ) ``` **Wave strategy**: - Wave 1: Tasks with no dependencies (they inform downstream tasks) — run in parallel - Wave 2: Tasks depending on Wave 1 — run in parallel - Wave 3+: Continue until all tasks covered — run in parallel per wave - Max 5 agents per wave to avoid overwhelming the configured research backend **Wait for each wave to complete before launching the next.** Parallel dispatch only happens WITHIN a wave; waves themselves are serial. --- ### Step 5: Collect and Write Results As each `research-expander` agent completes, save its research output: 1. Write agent output to a temp file: ```bash cat > /tmp/research-task-<ID>.md <<'EOF' <agent output> EOF ``` 2. Write research back to `tasks.json`: ```bash python3 skills/expand-tasks/script.py write-research --task-id <ID> --research /tmp/research-task-<ID>.md ``` 3. After all tasks are written, verify: ```bash python3 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 through a fresh `research-expander` invocation. --- ## 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 `research-expander` 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` (including any `mcp__plugin_prd_go__*` tools registered by this plugin), 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 | Error | Action | |-------|--------| | Research provider unreachable or rate-limited | Exit skill, tell user to verify `task-master models` research role is set and reachable | | `research-expander` agent returns empty/failed | Re-run that specific task with different queries | | `tasks.json` not found | Exit skill, tell user to run `/prd:go` first | | Task already expanded | Skip silently unless user forces re-expansion | | Agent timeout | Mark 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:go → generates PRD → parses into tasks ↓ /expand-tasks → research-expander agents run in Parallel waves → writes findings back to tasks.json ↓ 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-expander` agent typically completes in ~30s depending on research backend and query depth; 15 tasks ≈ 3 waves ≈ 2-3 minutes total