storm-research
Skill3 starsChanged 4 months agoMITView on GitHub
Use when researching any topic, question, market, company, technology, or decision — before writing reports or proposals, making business or investment decisions, preparing for negotiations, interviews, or presentations, or learning a new field. This is the default research method for all research tasks. Skip only for single-fact lookups, or when the user explicitly asks for a quick answer or names a different method.
15 other projects use a version of this.
Adopted by
Most recently taken up by
- ZhouYinLong-lab/Dog-Skillstheir own version, first seen Nov 8, 2026
- jrgilbertson/the-rookerytheir own version, first seen Nov 3, 2026
- rmichak/claude-skillstheir own version, first seen Nov 2, 2026
- Pdiamondz1/hma_project_foundationtheir own version, first seen Oct 9, 2026
- rumotion/ai-agent-project-templatetheir own version, first seen Oct 3, 2026
- EricWurth/skillstheir own version, first seen Sep 30, 2026
Used with
Projects with it also use roast.
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.
sumleoSkillroast
深度思维烤问引擎——通过渐进式追问挖掘用户内心深处的构思,暴露谬误、局限、潜能和潜意识中的盲点。触发条件:(1) 用户说'烤问我'、'挑战我的想法'、'帮我想清楚'、'roast me'、'challenge my thinking'、'审视一下';(2) 用户在描述一个想法/计划/决策但逻辑模糊或自相矛盾;(3) 用户使用 /roast 命令。适用于:创业想法、研究方向、产品设计、人生决策、论文论点、技术方案、任何需要深度思考的场景。不触发:用户明确只需要执行(不需要质疑)、情绪崩溃时(先安抚再烤问)。
Also in 3 projects that use this
Other versions15 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 15
- baifan-wang/skills★ 37same name
- jrgilbertson/the-rookery★ 15same name
- SevenBaylor/storm-research★ 9same name
- ZhouYinLong-lab/Dog-Skills★ 7same name
- bryannamd/claude-storm-research-skill★ 4same name
- EricWurth/skills★ 2same name
- launchstack-dev/storm-research★ 2same name
- Pdiamondz1/hma_project_foundation★ 1same name
- rmichak/claude-skills★ 1same name
- rumotion/ai-agent-project-template★ 1same name
5 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 repository6 other files it gives its agents.
- extract-approachSkill
- focus-groupSkill
- review-panelSkill
- sales-councilSkill
- second-brainSkill
- vault-gardenerSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 11 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Run all four, in order. Do not stop after Phase 1 or 3.
- Distinctive: line 33
All 5 agree → probably true.
- Distinctive: line 58
Perspectives that converge: If the skeptic sounds like the academic, you picked lazy personas — force genuine disagreement in framing.
“Common” is measured: kept by at least 6 in 10 of the 11 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
STORM Research
Overview
Multi-perspective research method adapted from Stanford's STORM (Shao et al., NAACL 2024, github.com/stanford-oval/storm). Core principle: one query returns the majority view; five grounded perspectives plus a contradiction map returns understanding. The STORM paper reports ~25% better organization and ~10% broader coverage from multi-perspective question asking versus its single-pass baseline.
The Iron Rule: every perspective must be grounded in real retrieval. Persona-prompting without sources is five flavors of one model's blind spots hallucinating in harmony. Each perspective's claims come from actual searches (WebSearch, WebFetch, or whatever search MCPs you have wired up) with sources noted. No search → no claim.
The Four Phases
Run all four, in order. Do not stop after Phase 1 or 3.
Phase 1 — Multi-Perspective Scan
Pick 5 perspectives suited to the topic. Default set:
| Perspective | Asks |
| Practitioner | What actually happens day-to-day? What breaks in reality? |
| Skeptic | What's overhyped or wrong? What could fail? |
| Economist | Who profits? What incentives shape the claims? |
| Historian | Has this pattern happened before? How did it end? |
| Academic | What does the actual evidence/data say? |
Swap perspectives when the topic demands (e.g. regulator, customer, competitor, clinician). For each: generate that persona's 2-3 sharpest questions, then search to answer them. Note the source for each finding.
Phase 2 — Contradiction Map
Find where the perspectives fight and why. Rules of evidence:
- All 5 agree → probably true.
- Perspectives conflict → the real understanding lives here; name the disagreement and the reason (incentive, timeframe, evidence quality).
- Nobody addressed it → you found a gap; say so explicitly.
Phase 3 — Synthesis
One briefing: what's established (with sources), the named contradictions, reliability ranking of major claims, and a specific recommended action — not a summary that hedges everything.
Phase 4 — Peer Review
Adversarially grade your own output: strongest claim, weakest claim, likely source bias, missing perspective, and a confidence score per key conclusion. The original STORM pipeline has no self-critique step, so this phase is our addition — and it is not optional. (And per the review-panel rule: Phase 4 is still self-critique — the finished deliverable gets an independent reviewer too.)
Scaling
| Mode | When | How |
| Quick | Casual question, minutes matter | 3 perspectives, ~5 searches, all inline |
| Standard (default) | Most research requests | 5 perspectives, 8-15 searches, inline or 2-3 parallel subagents |
| Deep | "Thorough/comprehensive/audit" | One subagent per perspective in parallel, then synthesize; consider a dedicated deep-research harness if you run one |
Output Shape
Deliver: Briefing (synthesis + recommended action) → Contradiction map → Perspective findings with sources → Peer review with confidence scores. Briefing first — the reader gets the answer before the apparatus.
Common Mistakes
- Answering from training data with persona flavoring. The perspectives structure the questions; retrieval answers them.
- Perspectives that converge. If the skeptic sounds like the academic, you picked lazy personas — force genuine disagreement in framing.
- Skipping Phase 2. The contradiction map is the step that separates this from a book report.
- Skipping Phase 4 to save time. 60 seconds of self-critique is the method's error bar.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

