# Research > Research & Information Gathering - Skill: `elophanto/research` (Agent Skill) - Install (CLI): `npx skillmds@latest add elophanto/research` - Raw SKILL.md: https://api.skillmd.com/api/skills/elophanto/research/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: elophanto (https://skillmd.com/u/elophanto) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/elophanto/research --- # Research & Information Gathering ## Description Comprehensive guide for researching topics, gathering information from the web, local files, and knowledge base, synthesizing findings, and preserving learned knowledge for future use. ## Triggers - research - find information - look up - summarize - investigate - analyze - compare - what is - how does - explain - find out - check - verify - learn about ## Instructions ### 1. Research Strategy Selection Choose your approach based on the information source: #### Knowledge Base First (Always Start Here) 1. knowledge_search with the query — you may already know this from past tasks 2. If results are relevant and sufficient, use them — no need for external research 3. If partial results, use them as context and supplement with other sources #### Local File Research 1. file_list to find relevant files (use pattern matching: `*.py`, `*.md`) 2. file_read for specific files — use line ranges for large files 3. For code analysis, self_read_source gives better context for agent source files 4. For project-wide searches, shell_execute with grep/find for complex patterns #### Web Research 1. browser_navigate to authoritative sources first (official docs, primary sources) 2. For general queries, start with a search engine (Google, DuckDuckGo) 3. browser_extract for text content, browser_read_semantic for structured overview 4. Cross-reference multiple sources when accuracy matters 5. For APIs, browser_get_network can reveal data endpoints directly ### 2. Source Hierarchy Prioritize sources in this order: 1. **Primary sources** — Official documentation, original papers, company blogs, government sites, specification documents 2. **Authoritative aggregators** — Wikipedia (for overview), MDN (for web tech), Python docs (for Python) 3. **Community knowledge** — Stack Overflow (verified answers), GitHub issues, technical blogs with code examples 4. **General web** — News articles, forum posts, social media (lowest reliability) ### 3. Research Workflows #### Fact-Checking / Verification ``` 1. knowledge_search → check if we already know this 2. browser_navigate to the most authoritative source 3. browser_extract → get the relevant passage 4. If conflicting claims: check 2-3 additional sources 5. Report findings with source attribution 6. knowledge_write to save verified facts for future use ``` #### Topic Deep-Dive ``` 1. knowledge_search → existing knowledge 2. browser_navigate → overview article (Wikipedia, docs homepage) 3. browser_read_semantic → structured overview of the topic 4. Identify key subtopics from the overview 5. browser_navigate to each subtopic's authoritative source 6. browser_extract → detailed information per subtopic 7. Synthesize findings into a structured summary 8. knowledge_write to preserve the research ``` #### Competitive / Comparison Research ``` 1. Identify the items to compare 2. For each item: a. browser_navigate to its official site b. browser_extract → features, pricing, specs c. browser_navigate to review/comparison sites 3. Build a comparison matrix from collected data 4. Present findings with clear differentiators ``` #### Current Events / Recent Information ``` 1. browser_navigate to news sources 2. browser_extract for article content 3. Check publication dates — prioritize the most recent 4. Cross-reference across 2-3 sources for accuracy 5. Distinguish between confirmed facts and speculation ``` #### Technical Documentation Lookup ``` 1. browser_navigate directly to the docs site (e.g., docs.python.org) 2. Use the site's search if available: a. browser_get_elements to find the search input b. browser_type the query c. browser_get_elements to find results 3. browser_extract the relevant documentation section 4. If the docs are paginated, follow links to subpages ``` ### 4. Information Synthesis #### Lead with the Answer - State the conclusion or answer first - Then provide supporting evidence and sources - Don't narrate the research process ("First I searched..., then I found...") #### Handle Uncertainty - If confident: state directly - If likely but not certain: "Based on [source], this appears to be..." - If conflicting: "Sources disagree — [source A] says X while [source B] says Y" - If unknown: "I couldn't find reliable information on this" #### Cite Sources - When reporting facts from the web, mention where they came from - For critical decisions, provide the URL so the user can verify - Don't over-cite obvious/common knowledge #### Structured Output - For comparisons: use a table or structured list - For explanations: start simple, add detail as needed - For summaries: lead with key points, details below - For data: present the most relevant subset, offer the full set ### 5. Preserving Knowledge After completing research, save valuable findings: ``` knowledge_write: path: "learned/research/{topic}.md" content: | # Topic Name ## Summary Key findings in 2-3 sentences. ## Details The full research findings. ## Sources - [Source 1](url) - [Source 2](url) ## Last Updated YYYY-MM-DD ``` **When to save:** - Factual information the user might ask about again - Technical documentation lookups for tools/services the user uses - Comparison research that took significant effort - Any finding that required multiple sources to verify **When NOT to save:** - One-off queries the user won't revisit - Information that changes rapidly (stock prices, weather) - Content that's trivially searchable ### 6. Common Pitfalls - **Don't guess** — if you're not sure, research it rather than stating potentially wrong information - **Don't over-research** — for simple factual questions, one authoritative source is enough - **Don't forget the knowledge base** — always check knowledge_search first before going to the web - **Don't scrape paywalled content** — if a page requires login/payment, tell the user - **Don't present search engine snippets as facts** — navigate to the actual page and read the full content - **Don't ignore dates** — information from 2020 may be outdated in 2026; check recency ## Verify - Every non-trivial claim in the output is paired with a source link, file path, or query result, not stated as a bare assertion - Sources span at least 2-3 independent origins; single-source conclusions are flagged as such - Counter-evidence or limitations are explicitly listed, not omitted to make the narrative tidier - Numbers in the deliverable carry units, time windows, and an as-of date (e.g., '$1.2M ARR as of 2026-04-30') - Direct quotes are verbatim and cite their location; paraphrases are marked as such - Out-of-date or unreachable sources are noted in the bibliography rather than silently dropped ## Notes The knowledge base (knowledge_search / knowledge_write) is your institutional memory. Use it aggressively — every substantial research task should leave behind a knowledge artifact for future sessions. The agent across sessions only remembers what was explicitly saved to the knowledge base or task memory.