# Research > Multi-source research orchestrator that routes questions across Context7 library docs, Tavily web research, codebase analysis, and GitHub examples, then returns a compact synthesis with confidence scoring. - Skill: `paulnsorensen/research` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulnsorensen/research` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulnsorensen/research/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - License: MIT - Author: paulnsorensen (https://skillmd.com/u/paulnsorensen) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulnsorensen/research --- # Research Use this skill when the user asks to research a technical question, compare libraries or approaches, investigate external APIs, or gather 2+ sources before making an implementation decision. Do not use this skill for a single library-doc lookup, a codebase-only question, or a simple fact that can be answered directly. ## Context Discipline Run the orchestration inline, but keep raw evidence out of the main context: 1. Route sources once and state the committed routing decision. 2. Spawn fetchers in parallel where the harness supports it. 3. Have fetchers write findings to scratch files under `$TMPDIR`. 4. Have one synthesis sub-agent read those scratch files and return the final answer. 5. Write the full report to `.cheese/research/.md`. 6. Delete the scratch directory after the report is written. The caller should see only the routing decision, fetcher status, synthesis, and the report path. ## Arguments The entire request body is the research question. The skill always writes a full report; the caller does not pass an output path. Create a run directory: ```bash RUN_ID="$(date +%Y%m%d-%H%M%S)-" RUN_DIR="${TMPDIR:-/tmp}/cheese-flow-research-${RUN_ID}" mkdir -p "$RUN_DIR" ``` Use a 4-6 word kebab-case slug derived from the topic. ## Phase 1: Classify Identify: - Primary topic - Question type: factual lookup, how-to, comparison, pattern search, API usage - Complexity: simple fact, focused question, comparison, or deep analysis - Constraints: version, language, framework, performance, license, architecture ## Phase 2: Route Sources Decide once. If a source is committed here, it must be executed in Phase 3. ```text Is it about a specific library API, config, or migration? YES -> Context7, plus GitHub if real-world usage matters Is it a factual, current, vendor, product, or "what/who/when" question? YES -> Tavily basic search Is it a "how should I..." or best-practices question? YES -> Tavily advanced search, plus Context7 when a named library is involved Is it about patterns in this repo? YES -> Codebase fetcher Is it about how open-source projects solve something? YES -> GitHub fetcher, plus Tavily if written analysis would help ``` Source guide: | Source | Best For | Notes | | --- | --- | --- | | Context7 | Library APIs, config, migration notes | Prefer over general web for named dependencies. | | Tavily | Current facts, technical articles, vendor docs, best practices | Use basic for factual lookups, advanced for analysis. | | Codebase | Local conventions, existing usage, constraints | Use repository search and reads. | | GitHub | Real-world OSS usage patterns | Use `gh` or the harness GitHub integration. | Emit a compact routing block: ```text ROUTING DECISION: - Context7: YES (library: "", query: "") - Tavily: YES (query: "", depth: basic|advanced) - Codebase: NO (external-only question) - GitHub: NO (not looking for OSS usage patterns) ``` ## Phase 3: Execute Fetchers Hard rule: if a source was committed in Phase 2, spawn it in Phase 3. Do not silently skip a routed source because it seems low value later. Every fetcher must: 1. Use only its assigned source tools. 2. Write findings to `/.md`. 3. Return exactly one status line: - `done: /.md` - `unavailable: ` Scratch file schema: ```markdown # - _Confidence: <0-100>_ _Status: _ ## Direct Answer <1-2 sentences> ## Evidence ## Sources - ``` ### Context7 Fetcher Use Context7 for library documentation. Resolve the library first, then query the resolved docs. Limit to 3 Context7 calls. Prompt shape: ```text You are fetching library documentation via Context7. Use only Context7 MCP tools. Do not use web search. Steps: 1. Resolve the library ID for "". 2. Query docs for "". 3. Verify the resolved library matches the question. 4. Write findings to /context7.md using the scratch schema. 5. Return only: done: /context7.md If Context7 is unavailable, write a one-line unavailable note to the scratch file and return only: unavailable: ``` ### Tavily Fetcher Use Tavily for web, facts, technical articles, product docs, and current ecosystem context. Do not route Serper; Tavily is the web source. Prompt shape: ```text You are researching with Tavily. Use only Tavily MCP tools. Do not use generic web search. Query: "" Depth: basic for factual/current lookups, advanced for how-to or comparisons. Steps: 1. Search with the selected depth. 2. If snippets are insufficient, extract from one promising URL. 3. Max 3 Tavily calls. 4. Write findings to /tavily.md using the scratch schema. 5. Return only: done: /tavily.md Do not use high-cost deep research tools unless the user explicitly asked for deep research. ``` ### Codebase Fetcher Use repository tools to find local precedents and constraints. Prompt shape: ```text You are analyzing the local codebase for patterns. Question: Find: - Existing usage of this dependency, pattern, or architecture - Constraints implied by current code - File references that matter to the decision Write findings to /codebase.md and return only: done: /codebase.md ``` ### GitHub Fetcher Use GitHub for real-world open-source patterns. Prompt shape: ```text You are searching GitHub for real-world examples. Use the harness GitHub integration or `gh` CLI. Find: - 2-3 relevant public examples - Observable implementation patterns - Caveats or maintenance signals Write findings to /github.md and return only: done: /github.md ``` ## Phase 4: Synthesis Spawn one synthesis sub-agent. It reads each `done` scratch file and ignores unavailable sources for content while still counting them in confidence. Synthesis task: 1. Build one evidence row per routed source: `Source | Finding | Score | Notes`. 2. Apply the mechanical confidence cap: - Any unavailable, failed, skipped, or not-spawned source caps overall confidence at 49. - 3+ agreeing sources: 85-100. - 2 agreeing sources: 60-84. - Disagreement: cap at 49 and explain why. - 1 completed source: inherit that source's score. 3. Return exactly: ```markdown ## Research: ### Finding <1-3 concise paragraphs> ### Evidence by Source | Source | Finding | Score | Notes | | --- | --- | --- | --- | | ... | ### Implications <2-4 sentences about how this affects the user's task> ### Overall Confidence **** - ``` After the synthesis block, append a fenced `report-body` block containing a full markdown report with source URLs, file refs, and repo links. The orchestration layer writes that report body verbatim to `.cheese/research/.md`. ## Cleanup After the report is written: ```bash rm -rf "$RUN_DIR" ``` ## Rules - Do not read scratch files in the main context. - Do not write application code or implement the researched solution. - Do not use Serper or SerperAPI; Tavily is the web and facts source. - Do not use high-cost Tavily deep research unless the user explicitly asks. - Do not skip a source that was committed in the routing decision. - If a routed source fails, surface the incomplete confidence cap instead of pretending the remaining evidence is complete.