research
Skill39 starsChanged 8 months agoMITView on GitHub
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.
189 other projects use a version of this.
Adopted by
Most recently taken up by
- dialog-db/dialog-dbtheir own version, first seen Nov 9, 2026
- howells/skillstheir own version, first seen Nov 8, 2026
- xiaoxiaosrm/dsh-mattpocock-skillstheir own version, first seen Nov 8, 2026
- Suge8/friedbuntheir own version, first seen Nov 8, 2026
- wowyuarm/dsh-agent-teamtheir own version, first seen Nov 8, 2026
- Contexera/dsh-agent-teamtheir own version, first seen Nov 8, 2026
Used with
Projects with it also use daily-arxiv, exp-eval and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
LambenthanSkilldaily-arxiv
每日从 arXiv 拉取新论文,过滤相关性,auto-ingest 高优先级论文,检测 SOTA 更新
Also in 5 projects that use this
LambenthanSkillexp-eval
实验判决门:Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges
Also in 5 projects that use this
truongtv22SkillProblem-Solving Techniques
Apply systematic problem-solving techniques for complexity spirals (simplification cascades), innovation blocks (collision-zone thinking), recurring patterns (meta-pattern recognition), assumption constraints (inversion exercise), scale uncertainty (scale game), and dispatch when stuck. Techniques derived from Microsoft Amplifier project patterns adapted for immediate application.
Also in 5 projects that use this
liufeng1976Skillsetup-ts-deep-modules
Wire dependency-cruiser into a TypeScript repo so each package is a deep module — implementation hidden in subfolders, reachable only through its entry-point files. User-invoked.
Also in 43 projects that use this
skyllwtSkillexp-pilot-eval
Pilot result evaluation — read pilot results, apply success criteria, update idea page (pilot_result, failure_reason if failed), generate PILOT_VERDICT_REPORT. Called by /ideate Phase 5 after /exp-pilot-run.
Also in 4 projects that use this
skyllwtSkillexp-pilot-run
Pilot experiment execution — read Pilot Spec YAML, write pilot code, run experiment(Confirm with the user before operation and require the applicant to conduct manual inspection), return results. Called by /ideate Phase 5. Does NOT modify wiki pages or judge pass/fail.
Also in 4 projects that use this
- +5 more from Lambenthan
- +1 more from skyllwt
Other versions189 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 189
- mattpocock/skills★ 309ksame name
- Yeachan-Heo/oh-my-claude-sisyphus★ 41ksame name
- Yeachan-Heo/oh-my-claudecode★ 41ksame name
- QuivrHQ/quivr★ 40ksame name
- The-Vibe-Company/quivr★ 40ksame name
- udecode/plate★ 17ksame name
- openai/plugins★ 7.8ksame name
- ArcReel/ArcReel★ 6.3ksame name
- vinvcn/mattpocock-skills-zh-CN★ 5.5ksame name
- withcoral/coral★ 4.9ksame name
179 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository13 other files it gives its agents.
- skills/init/scaffold/CLAUDE.mdCLAUDE.md
- book-analyzerSkill
- courseSkill
- deep-researchSkill
- initSkill
- lectureSkill
- paper-discoverSkill
- processSkill
- recallSkill
- slide-makerSkill
- synthesizeSkill
- vault-graphSkill
- youtubeSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
13 rules. 0 are common, 12 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 169 edited versions we read keep these.
12
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 59
Don't wait for confirmation unless the topic is ambiguous — just show and go.
- Distinctive: line 63
Before hitting the web, check what the vault already contains:
- Distinctive: line 93
Use different query phrasings per sub-question (not just the topic repeated)
- Distinctive: line 94
Prefer recent sources (add "2025" or "2026" to queries when freshness matters)
- Distinctive: line 96
If initial results are thin, reformulate and search again
- Distinctive: line 98
Run searches in parallel where possible (multiple WebSearch calls in one turn).
- Distinctive: line 102
After the first search round, review what you have:
- Distinctive: line 107
Run 1-2 targeted follow-up searches to fill gaps.
- Distinctive: line 112
Read the agent definition:
- Distinctive: line 172
Grep/Glob: Search vault for existing knowledge
- Distinctive: line 175
Write: Create the research note
- Very specific: line 188
Create → notes/ml/(Research) RLHF vs DPO - Alignment Methods Compared.md </Good>
- Distinctive: line 196
No source links — claims without evidence </Bad> </Examples>
“Common” is measured: kept by at least 6 in 10 of the 169 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
<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
- Generate timestamp ID:
date +%Y%m%d%H%M%S - Determine subfolder — if the topic clearly fits an existing folder, use it.
Otherwise default to
notes/research/. Create the folder if needed. - Create the note:
---
id: YYYYMMDDHHMMSS
type: note
processing_status: processed
created_date: YYYY-MM-DD
updated_date: YYYY-MM-DD
---
[AGENT OUTPUT — starts with # title]- 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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