ask
DAIR-AutoResearch/AutoSci/.claude/skills/ask/SKILL.md
Ask the wiki a question, retrieve and synthesize relevant pages, optionally crystallize the answer back into the wiki
Often used withdaily-arxivexp-evalexp-run
One search for your own team's files and the public ecosystem. See how real projects write their agent instructions, and bring the good ideas home.
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.
DAIR-AutoResearch/AutoSci/.claude/skills/ask/SKILL.md
Ask the wiki a question, retrieve and synthesize relevant pages, optionally crystallize the answer back into the wiki
Often used withdaily-arxivexp-evalexp-run
DAIR-AutoResearch/AutoSci/.claude/skills/check/SKILL.md
Scan the full wiki to detect health issues and produce a tiered fix-recommendation report (covers all entity types in runtime/schema/entities.yaml + graph consistency)
Often used withdaily-arxivexp-evalexp-run
DAIR-AutoResearch/AutoSci/.claude/skills/discover/SKILL.md
Build a ranked shortlist of candidate papers (anchor-driven, topic-driven, or derived from current wiki state) that the user — or an upstream skill — may decide to feed into `/ingest`. Use whenever the user asks "what should I read next", "find papers similar to this one", "recommend related work", "what's around this topic", or whenever `/ingest` is invoked with `--discover`. Does not ingest; only proposes.
Often used withdaily-arxivexp-evalexp-run
DAIR-AutoResearch/AutoSci/.claude/skills/edit/SKILL.md
Add or remove raw sources, or update wiki content, per user request
Often used withdaily-arxivexp-evalexp-run
DAIR-AutoResearch/AutoSci/.claude/skills/exp-design/SKILL.md
Idea-driven experiment design with iterative ablation — method candidate generation (direct / hybrid / cross-idea combination) → benchmark selection → iterative ablation (non-linear: ablation can trigger method simplification and re-planning) → sensitivity analysis → main experiment → optional generalization → deep analysis of intermediate quantities. Use when designing a full experiment suite for an idea after pilot evaluation.
DAIR-AutoResearch/AutoSci/.claude/skills/exp-eval/SKILL.md
Experiment verdict gate — Review LLM independently judges results → 4 verdict paths → auto-update the linked idea's status / failure_reason and graph edges
DAIR-AutoResearch/AutoSci/.claude/skills/exp-pilot-eval/SKILL.md
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.
DAIR-AutoResearch/AutoSci/.claude/skills/exp-pilot-run/SKILL.md
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.
DAIR-AutoResearch/AutoSci/.claude/skills/exp-run/SKILL.md
Full experiment execution pipeline — prepare code → deploy(Confirm with the user before operation and ask the applicant to conduct manual inspection) → monitor → collect results, supporting three run modes
DAIR-AutoResearch/AutoSci/.claude/skills/exp-status/SKILL.md
View the status of all running experiments; optionally auto-collect completed experiments and advance the pipeline
DAIR-AutoResearch/AutoSci/.claude/skills/ideate/SKILL.md
Multi-phase research idea generation pipeline: landscape scan → dual-model brainstorm → filter & validation → write to wiki → pilot
Often used withdaily-arxivexp-evalexp-run
DAIR-AutoResearch/AutoSci/.claude/skills/novelty/SKILL.md
Multi-source novelty verification — WebSearch + Semantic Scholar + wiki + Review LLM cross-verify — outputs novelty score and recommendations. Optionally writes the score back to an idea page with --write.
DAIR-AutoResearch/AutoSci/.claude/skills/paper-compile/SKILL.md
LaTeX compile → PDF — latexmk compile + auto-fix + page count/anonymity/font/[UNCONFIRMED] checks + submission checklist
Often used withdaily-arxivexp-evalexp-run
DAIR-AutoResearch/AutoSci/.claude/skills/paper-draft/SKILL.md
Draft a LaTeX paper from PAPER_PLAN — write each section from wiki sources + generate figures/tables + BibTeX verification + de-AI polish
DAIR-AutoResearch/AutoSci/.claude/skills/paper-plan/SKILL.md
Compile a paper outline from the idea graph — evidence map → narrative structure → section plan + figure plan + citation plan, Review LLM review mandatory
Often used withdaily-arxivpaper-illustrationexp-eval
DAIR-AutoResearch/AutoSci/.claude/skills/poster/SKILL.md
Generate an academic poster from a drafted paper — distill sections into a single-page HTML poster with figures and inter-section transitions
DAIR-AutoResearch/AutoSci/.claude/skills/prefill/SKILL.md
Seed wiki/foundations/ with domain background knowledge so subsequent /ingest does not create duplicate concept pages for textbook material
DAIR-AutoResearch/AutoSci/.claude/skills/rebuttal/SKILL.md
Parse review comments → atomize concerns (Rvx-Cy) → map to wiki ideas/methods → check evidence → Review LLM stress-test → generate rebuttal
Often used withdaily-arxivexp-evalexp-run
DAIR-AutoResearch/AutoSci/.claude/skills/refine/SKILL.md
General-purpose multi-round iterative improvement — repeatedly calls /review on any research artifact, parses feedback, applies fixes, updates wiki, until the target score is reached
Often used withaddskilldaily-arxivastra-fullreview
DAIR-AutoResearch/AutoSci/.claude/skills/research/SKILL.md
End-to-end research orchestrator — idea discovery → experiment design → execution → verdict → paper writing, with human gates and session-resumable state
Often used withdaily-arxivProblem-Solving Techniquessetup-ts-deep-modules
Plain text files in a repository that tell a coding agent how the project works: commands to run, conventions to follow and things to avoid. CLAUDE.md, AGENTS.md, cursor rules and skills are the common kinds.
CLAUDE.md is read by Claude Code. AGENTS.md is an open format that Codex, Cursor and other agents read. Many projects keep one and point the other at it.
A folder with a SKILL.md that describes one capability, such as filling PDFs or reviewing code. The agent loads it only when the task calls for it.
Your agents already can, over MCP, limited to the files you're allowed to read. Searching them from this page is coming.