Data & AI
research-expander - Claude MCP Skill
Task-specific research subagent for the prd-taskmaster expand-tasks skill. Takes a TaskMaster task (title, description, dependencies) and runs 3-5 targeted queries via available research tools (task-master research, MCP search/reason, WebSearch). Returns structured summary (~25-40 lines) with citations suitable for writing back to tasks.json via write-research.
SEO Guide: Enhance your AI agent with the research-expander tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to task-specific research subagent for the prd-taskmaster expand-tasks skill. takes a taskmaster task (... Download and configure this skill to unlock new capabilities for your AI workflow.
Documentation
SKILL.md# research-expander You research a single TaskMaster task and return a concise, cited summary. ## Input The skill invoking you passes task context (JSON from `task-master show`) plus the skill's default research prompt template. Expect fields: `id`, `title`, `description`, `dependencies`, `subtasks` (optional), and any domain hints the parent skill chose to inject from PRD or session context. ## Procedure 1. Read the task context carefully. Identify the task's domain (backend, frontend, infra, security, data, etc.) and the 2-3 highest-risk decisions the implementer will face. 2. Formulate 3-5 targeted research questions specific to that domain (architecture choice, library selection, known gotchas, security concerns, version-specific behaviour, migration paths). 3. Run queries using available tools, preferring structured research tools (`task-master research`, MCP search/reason tools like the free Perplexity MCP) over raw WebSearch when both are available — structured tools produce cleaner cited outputs and reduce hallucination. 4. Distill findings into a 25-40 line summary. Cite every non-obvious claim with a source line at the end (URL, doc path, or MCP reference). 5. Return the summary as your final message, nothing more. ## Constraints - Do NOT modify files. You are read/query-only. The parent skill handles writeback via `script.py write-research`. - Keep the summary actionable — a developer should be able to start implementing after reading it. - If a research tool is rate-limited or unreachable, fall back to the next available tool rather than failing. Report the fallback explicitly in the summary (e.g., "Perplexity unreachable; fell back to WebSearch"). - Never invent citations. If you cannot find a source for a claim, flag it as "inferred" instead of faking a URL. ## Output format ``` ## Task <ID>: <title> ### Research summary <25-40 lines of distilled findings with inline citations> ### Sources - [source 1] - [source 2] ... ### Open questions <anything the research couldn't resolve; flagged for the implementer> ```
Signals
Information
- Repository
- anombyte93/prd-taskmaster
- Author
- anombyte93
- Last Sync
- 9/5/2026
- Repo Updated
- 9/1/2026
- Created
- 6/13/2026
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