AutoSci is an AI research platform organized around a wiki, with an agent that supports stages of scientific work such as reading, experimentation, writing, and retaining knowledge across projects. It is for people building or using AI-assisted research workflows, with Claude Code, Codex, and OpenCode adaptations available. The catalogue add-ons extend those agent-specific workflows.
Borrowing it
Nothing to install: this file belongs to skyllwt/AutoSci. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/skyllwt/AutoSci/main/.claude/skills/ask/SKILL.mdgit clone --depth 1 https://github.com/skyllwt/AutoSciWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/skyllwt/autosci/ask)<a href="https://agentmods.dev/skills/skyllwt/autosci/ask"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/ask/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/skyllwt/autosci/ask"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/ask.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.02695 |
| Opus 5.5 | $0.00009 | $0.01078 |
| Sonnet 5.5 | $0.00004 | $0.00539 |
| Haiku 4.5 | $0.00002 | $0.00269 |
Grade A, and why
ask scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ask
Ask a question to the wiki knowledge base. The LLM reads context_brief.md for global context, retrieves relevant pages, synthesizes an answer with citations. Good answers can be crystallized back into the wiki — written to outputs/, as new concept pages, or appended to an existing idea/method/output note — so exploration compounds like ingestion does.
Inputs
question: natural-language question (e.g. "What is the core difference between LoRA and Adapter?")--crystallize(optional): if specified, crystallize the answer back into the wiki (default: answer only, no write)--format(optional): output format, defaultmarkdown, options:table/timeline/bullets
Outputs
- Always: terminal output of synthesized answer (with
[[slug]]citations) - If crystallize:
wiki/outputs/{query-slug}.md— query result page (default crystallize target)- or
wiki/concepts/{slug}.md— if the answer reveals a new cross-paper concept - or appended to an existing
wiki/ideas/{slug}.md/wiki/methods/{slug}.md/wiki/outputs/{slug}.md— if the answer adds a finding to an existing entity - updated
wiki/graph/edges.jsonl(relationships produced by crystallize) - updated
wiki/index.mdandwiki/log.md
Wiki Interaction
Reads
wiki/graph/context_brief.md— global compressed context (ideas, gaps, failed ideas, papers, edges)wiki/index.md— page catalog for locating relevant pageswiki/graph/open_questions.md— open questions, helps identify whether the question touches known gapswiki/papers/*.md— paper pages relevant to the questionwiki/concepts/*.md— concept pages relevant to the questionwiki/methods/*.md— method pages relevant to the questionwiki/topics/*.md— topic pages relevant to the questionwiki/people/*.md— if the question involves specific researcherswiki/ideas/*.md— if the question involves research ideas or failed ideaswiki/experiments/*.md— if the question involves experiment resultswiki/Summary/*.md— if the question involves domain-wide landscape
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 211 lines · 22 tokens per session scan A 49ff6efa7fe2
ask is a skill published in the GitHub repository skyllwt/AutoSci (1,706 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 2,695 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
ask compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| askskyllwt/AutoSci | Skill | 1.7k | 1mo ago | MIT | A | 22 | Claude Code |
| imagegenopenai/codex | Skill | 133k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |