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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/skyllwt/autosci/prefillnpx skills add skyllwt/AutoSci --skill prefillgit 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/prefill)<a href="https://agentmods.dev/skills/skyllwt/autosci/prefill"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/prefill.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00024 | $0.01609 |
| Opus 5 | $0.00012 | $0.00805 |
| Sonnet 5 | $0.00005 | $0.00322 |
| Haiku 4.5 | $0.00002 | $0.00161 |
Grade A, and why
prefill 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 5d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/prefill
Sediments foundational background (seminal methods, common practice, standard architectures) into
wiki/foundations/as terminal pages. Foundations are single-direction by design: other pages link to them, foundations write no reverse links.
Trigger
Manual: /prefill [domain] or /prefill --add "concept name".
Inputs
domain(positional, optional): research domain — one ofgeneral,NLP,CV,ML Systems,Robotics. If omitted, infer fromwiki/topics/tags; ifwiki/topics/is empty, prompt the user.--add "<concept>": skip the catalog and seed exactly one foundation by name.
Outputs
wiki/foundations/{slug}.md— one page per seeded concept- Updated
wiki/index.md(foundations section regenerated byrebuild-index) wiki/log.mdentry
Wiki Interaction
Reads
wiki/topics/*.md— for domain inference (whendomainis omitted)wiki/foundations/*.md— to skip already-seeded concepts (idempotent).claude/skills/prefill/foundations-catalog.yaml— seed list
Writes
wiki/foundations/{slug}.md(new only — never overwrite)wiki/index.md(viatools/research_wiki.py rebuild-index)wiki/log.md(viatools/research_wiki.py log)
Workflow
Pre-conditions: working directory contains wiki/, tools/, .claude/. Set WIKI_ROOT=wiki/.
Step 1: Resolve domain
- If
domainargument given → use it. - Else if
--addmode → domain isgeneralunless the user specified one. - Else: read all
wiki/topics/*.mdfrontmattertags; if a single dominant domain is detected, use it; otherwise ask the user.
Step 2: Load seeds
- Catalog mode: read
.claude/skills/prefill/foundations-catalog.yaml. Pick all entries underdomains.{domain}plus everything underdomains.general(general foundations apply to every research field). --addmode: synthesize a single seed entry{slug: <slugified concept>, title: <concept>, summary: ""}. Usepython3 tools/research_wiki.py slug "<concept>"to derive the slug.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 161 lines · 24 tokens per session scan A 66a888d5bf42
prefill is a skill published in the GitHub repository skyllwt/AutoSci (1,660 stars, last pushed 6d ago), licensed MIT. It adds 24 tokens to every session and 1,609 once invoked, about $0.0001 per session on Opus 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…