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research

Research a topic and create a new note in the vault. Use this skill whenever the user wants to learn about a topic they don't know yet, investigate a question, explore an idea, or asks "what is X", "research X", "tell me about X", "I want to understand X", "look into X". Also triggers on /research. This is for BROAD research (web + vault) — for academic papers specifically, use /paper-discover instead.

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来源信息

仓库
tuan3w/obsidian-vault-agent
最近来源活动
2026年3月21日 07:46
检测到的 SKILL.md 语言
英语
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39
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2

安装方式

默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。

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决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。

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SKILL.md
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name
research
description
Research a topic and create a new note in the vault. Use this skill whenever the user wants to learn about a topic they don't know yet, investigate a question, explore an idea, or asks "what is X", "research X", "tell me about X", "I want to understand X", "look into X". Also triggers on /research. This is for BROAD research (web + vault) — for academic papers specifically, use /paper-discover instead.
allowed-tools
Bash, Read, Write, Edit, Agent, Grep, Glob, WebFetch, WebSearch
argument-hint
<topic or question>
<Purpose> Deep research on any topic — search the web and the vault, then synthesize findings into a high-quality vault note. Unlike a quick web search, this skill decomposes the question, searches multiple angles, checks what the vault already knows, and produces a note with source links, confidence levels, and vault connections. </Purpose> <Use_When> - User asks to research a topic ("research quantum error correction") - User wants to understand something new ("what is RLHF and why does it matter?") - User wants a research note created in the vault - User uses /research with a topic </Use_When> <Do_Not_Use_When> - User wants to find academic papers specifically (use /paper-discover) - User wants to process an existing vault note (use /process) - User wants to synthesize across existing vault notes (use /synthesize) - User has a YouTube video to process (use /youtube) </Do_Not_Use_When> <Steps> ## Stage 1: PLAN — Decompose the Question Parse the topic from $ARGUMENTS. If vague, ask one clarifying question. Break the research topic into 3-5 **specific sub-questions** that together cover the topic well. Think about: - What IS it? (definition, core mechanism) - Why does it matter? (motivation, impact, who cares) - How does it work? (process, architecture, method) - What are the tradeoffs? (limitations, alternatives, open problems) - Where is it going? (trends, recent developments, future) Present the sub-questions to the user briefly: ``` Researching "topic". Sub-questions: 1. ... 2. ... 3. ... Searching now. ``` Don't wait for confirmation unless the topic is ambiguous — just show and go. ## Stage 2: SEARCH VAULT — What Do We Already Know? Before hitting the web, check what the vault already contains: ``` Grep(pattern="KEYWORD", path="notes/", glob="*.md", head_limit=15) ``` Also try MCP search if available: ``` mcp__obsidian-vault__search_notes(query="KEYWORD", limit=10) ``` Note any existing vault notes that are relevant — these become [[wikilinks]] in the output and inform what the web search should FOCUS on (gaps, not repeats). ## Stage 3: SEARCH WEB — Multi-Query, Parallel For each sub-question, run a targeted WebSearch: ``` WebSearch(query="specific sub-question query", num_results=5) ``` Then WebFetch the 3-5 most promising URLs to get full content: ``` WebFetch(url="URL", prompt="Extract key facts, data, and insights about [sub-question]. Include specific numbers, dates, names, and technical details.") ``` **Search strategy:** - Use different query phrasings per sub-question (not just the topic repeated) - Prefer recent sources (add "2025" or "2026" to queries when freshness matters) - Mix source types: technical blogs, official docs, news, research summaries - If initial results are thin, reformulate and search again Run searches in parallel where possible (multiple WebSearch calls in one turn). ## Stage 4: DEEPEN — Find Gaps and Conflicts After the first search round, review what you have: - Which sub-questions are well-answered? Which are thin? - Are there conflicting claims across sources? - Did any source mention something surprising worth following up? Run 1-2 targeted follow-up searches to fill gaps. This second pass is what separates good research from a quick Google. ## Stage 5: SYNTHESIZE — Build the Note Read the agent definition: ``` Read("${CLAUDE_SKILL_DIR}/agents/research-noter.md") ``` Launch the research-noter agent: ``` Agent( subagent_type="general-purpose", model="sonnet", prompt="You are Research Noter. Follow these instructions exactly: [INSERT FULL CONTENT OF agents/research-noter.md HERE] RESEARCH TOPIC: [topic] SUB-QUESTIONS: [list] VAULT CONTEXT (existing notes on this topic): [vault search results] WEB FINDINGS: [organized by sub-question, with source URLs] Produce the note body following the Output Format. Do NOT include frontmatter." ) ``` ## Stage 6: INTEGRATE — Create the Vault Note 1. Generate timestamp ID: `date +%Y%m%d%H%M%S` 2. Determine subfolder — if the topic clearly fits an existing folder, use it. Otherwise default to `notes/research/`. Create the folder if needed. 3. Create the note: ```markdown --- id: YYYYMMDDHHMMSS type: note processing_status: processed created_date: YYYY-MM-DD updated_date: YYYY-MM-DD --- [AGENT OUTPUT — starts with # title] ``` 4. Report to user: - Note path - Number of sources used - Key vault connections found - Any gaps flagged as uncertain </Steps> <Tool_Usage> - **WebSearch**: Multi-query web search (one per sub-question) - **WebFetch**: Deep-read promising URLs for full content - **Grep/Glob**: Search vault for existing knowledge - **MCP search_notes**: Vault search via Obsidian MCP - **Agent**: Synthesis agent (sonnet) - **Write**: Create the research note - **Bash**: Generate timestamp ID </Tool_Usage> <Examples> <Good> User: /research RLHF vs DPO for language model alignment 1. Plan → 4 sub-questions: what is each, how do they differ mechanically, what are the empirical results, what's the current trend 2. Vault search → found (Term) RLHF, (Paper) DPO paper, 3 related notes 3. Web search → 8 sources across sub-questions, including recent benchmarks 4. Deepen → gap on compute costs, found 2 more sources 5. Synthesize → 5-section note with 12 sources, 6 vault wikilinks 6. Create → notes/ml/(Research) RLHF vs DPO - Alignment Methods Compared.md </Good> <Bad> - Searches once and stops — no gap-finding, no deepening - Creates a note that's just a list of links with no synthesis - Ignores what the vault already knows about the topic - Produces a wall of text instead of structured, bullet-point insights - No source links — claims without evidence </Bad> </Examples> <Escalation_And_Stop_Conditions> - **Topic too broad** ("research AI"): Ask user to narrow down - **No web results**: Inform user, offer to create note from vault knowledge only - **Mostly paywalled sources**: Note the limitation, use what's available - **Topic already well-covered in vault**: Show existing notes, ask if user wants a fresh perspective or an update </Escalation_And_Stop_Conditions> $ARGUMENTS
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