2015: 5,154 scientists co-authored one paper on the Higgs boson.
Today: We're launching the largest academic collaboration in human history
— 🏛️ Building the Library of Alexandria for AGI, Accelerating Automated Scientific Discovery.
Science is the last important problem left for AI to solve. Real scientific breakthroughs require something no model has: the hard-won intuition of researchers who've spent years at the frontier.
This intuition lives in your head — the know-how, the heuristics, the reasoning patterns, the "I just know this won't work" instinct. It never makes it into papers. It dies when you retire.
ResearchSkills captures it before it's lost. We turn the tacit knowledge of the world's top researchers — their skills, thinking frameworks, and principles — into reusable AI agent skills (compatible with Claude Code and Codex). Every contribution makes every AI scientist — now and in the future — smarter, permanently.
Each skill encodes the knowledge, tools, reasoning protocols, and common pitfalls of a scientific field. Skills can be written by domain experts or auto-extracted from your research conversations using /researchskills-extract. The command extracts three types of cognitive memory from your research sessions — procedural (IF-THEN rules for research impasses), semantic (facts LLMs don't know), and episodic (concrete research episodes) — then packages them as reusable skills. Point your AI agent at a skill, and it reasons like a domain expert.
Note: Applying a skill may trigger broad edits, long workflows, and significant token usage — review the expected scope before running one deeply.
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Your skills make YOUR AI smarter first. Extracted skills are cached locally. Your Claude Code / Codex / Cursor immediately reasons better in your domain — before you ever submit anything.
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Privacy first. Nothing is scanned or uploaded without your explicit consent. A blocking consent gate asks before every operation. You review everything before submission.
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Low cost, smart caching. Conversations are compressed before analysis. Already-processed sessions are cached and skipped on re-runs. The heavy lifting is delegated to lighter models.
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Works everywhere. Claude Code, Codex, Cursor, Windsurf, VS Code, JetBrains — any tool that reads markdown instructions. Skills are plain
.mdfiles, not locked to one platform. -
Works on remote servers. SSH and headless environments are auto-detected. No browser needed — the submission URL is printed to your terminal.
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Immortalize your expertise. Your decades of know-how become a permanent, citable contribution to science. Every skill you contribute trains every future AI scientist.
Prerequisite: Install Node.js (includes npm). LTS version recommended.
npm install -g @scienceintelligence/researchskills-extractClaude Code:
/researchskills-extract
Codex (start with codex -a never -s danger-full-access):
$researchskills-extract
💡 For best results: use the most powerful model with the highest reasoning effort — Claude Code: Opus 4.6 + max effort. Codex: GPT-5.4 + x-high. Don't worry about token usage — conversations are heavily compressed before analysis, and the per-session extraction is delegated to lighter models behind the scenes. Your chosen model mainly orchestrates the pipeline.
The command scans your conversation history and extracts research skills organized by cognitive memory type:
- Procedural memory: IF-THEN rules for navigating research impasses (e.g., "IF gradient explodes THEN check learning rate before architecture")
- Semantic memory: Domain facts that LLMs don't reliably know (e.g., calibration constants, method limitations, undocumented tool behaviors)
- Episodic memory: Concrete research episodes capturing what was tried, what failed, and what the researcher learned
An interactive browser review page lets you verify the extracted skills, check de-identification, and bind them to your paper (arXiv/DOI) or project. Submit your skills to ResearchSkills, where they become part of a growing knowledge base for building better AI scientists.
After running, submit via here →
Write your own skill following the guide →
Method D: Convert Existing Skills via Claude Code / Codex
Already have research skills in notes, documents, or any format? Run one command — it reads your files, converts them, and opens a PR.
npm install -g @scienceintelligence/researchskills-extractClaude Code:
/researchskills-convert
Codex:
$researchskills-convert
The command asks where your skills are, reads them, converts each one into the correct format, and opens a PR to this repository. Forking, branching, file placement, and de-identification are handled automatically.
Don't see your field? Propose a new area → · Need a skill but can't write it yourself? Request a skill →
ResearchSkills skills are grounded in cognitive architecture theory — Soar (Laird, 2012), ACT-R (Anderson, 1996), and Case-Based Reasoning (Kolodner, 1993). Skills are organized by how researchers' minds actually store and retrieve expertise, not by arbitrary categories.
| Type | What it stores | When it triggers |
|---|---|---|
| Procedural | IF-THEN rules for research impasses | Agent faces a decision, gets stuck, or assumptions fail |
| Semantic | Facts missing from LLM training data | Agent needs domain knowledge it doesn't have |
| Episodic | Concrete research episodes | Agent encounters a situation similar to a past experience |
Classified by the type of research impasse (adapted from Soar's impasse taxonomy):
| Subtype | Impasse | Example |
|---|---|---|
tie |
Multiple paths, unclear which to choose | "Ablation vs. full retrain — which first?" |
no-change |
Completely stuck, no idea what to do next | "Results are bizarre, nothing makes sense" |
constraint-failure |
A methodological assumption doesn't hold | "Data violates i.i.d. assumption" |
operator-fail |
Chose the right approach but execution fails | "Correct method, but CUDA OOM on large batch" |
Each procedural skill contains: When (trigger condition + exclusions) → Decision (preferred action + rejected alternatives + reasoning) → Local Verifiers (how to check) → Failure Handling (what if it doesn't work) → Anti-exemplars (when NOT to use this).
Only three sub-types qualify — everything else is redundant with LLM training data:
| Subtype | What it stores | Example |
|---|---|---|
frontier |
Post-training-cutoff knowledge | "Flash Attention 3 renamed the causal parameter" |
non-public |
Lab-internal, unpublished knowledge | "This vendor's H100 batch has NCCL topology issues" |
correction |
Fixes for LLM's incorrect default beliefs | "Adam eps=1e-8 is unstable for mixed-precision; use 1e-5" |
Classified using Case-Based Reasoning terminology:
| Subtype | Signal | Retrieval trigger |
|---|---|---|
failure |
"Did X, broke because of hidden reason Y" | Agent is about to do something similar |
adaptation |
"Standard method failed, but workaround Z worked" | Agent is stuck with the standard approach |
anomalous |
"Expected A, observed B — turned out to be important" | Agent observes a similar anomaly |
skills/
└── {domain}/ # 8 arXiv-aligned domains
└── {subdomain}/ # 155 subcategories
└── {contributor}/ # Your name
├── procedural/ # tie--, no-change--, constraint-failure--, operator-fail--
├── semantic/ # frontier--, non-public--, correction--
└── episodic/ # failure--, adaptation--, anomalous--
For the full rationale — why research is hard, why LLMs struggle with it, and how skills change agent behavior — see Why Research Is Hard. For the complete schema specification, see Skill Schema Design.
Reviewers are domain experts who guard the scientific quality of skills in their subdomain. You need substantial peer-review experience in the relevant field.
What you do: Review submitted skills for scientific accuracy and completeness. Provide constructive feedback to contributors. Promote skill status from draft to reviewed once verified.
What you get: Approve or request changes on submissions in your subdomain.
Aligned with the arXiv category taxonomy. 8 domains, 155 subcategories.
| Domain | arXiv | Subcategories | Reviewer(s) |
|---|---|---|---|
| ⚛️ Physics | astro-ph, cond-mat, gr-qc, hep, nlin, physics, ... | 51 | Seeking reviewer |
| ➗ Mathematics | math | 32 | Seeking reviewer |
| 💻 Computer Science | cs | 40 | Seeking reviewer |
| 🧬 Quantitative Biology | q-bio | 10 | Seeking reviewer |
| 📊 Statistics | stat | 6 | Seeking reviewer |
| ⚡ Electrical Engineering & Systems Science | eess | 4 | Seeking reviewer |
| 📈 Economics | econ | 3 | Seeking reviewer |
| 💹 Quantitative Finance | q-fin | 9 | Seeking reviewer |
View all 155 subcategories in the interactive knowledge tree →
CC BY 4.0 — free to share and adapt, with attribution.
With gratitude to everyone who makes this possible:
Contributors → · Reviewers → · Sponsors → · Organizers →