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/init/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/init)<a href="https://agentmods.dev/skills/skyllwt/autosci/init"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/init/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/init"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/init.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00019 | $0.04365 |
| Opus 5.5 | $0.00008 | $0.01746 |
| Sonnet 5.5 | $0.00004 | $0.00873 |
| Haiku 4.5 | $0.00002 | $0.00436 |
Grade A, and why
init 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/init
Build a wiki from
raw/with deterministic source preparation, planner-guided discovery, provisional notes/web scaffolding, and parallel/ingestfan-out/fan-in.
Use these local references on demand:
references/prepare-and-discovery.md— prepare flow, final selection, fetch, and source-manifest rulesreferences/planner-policy.md— planner behavior and LLM trim expectationsreferences/parallel-ingest.md— worktree isolation, subagent prompt contract, merge, and cleanup
Inputs
topic(optional): research direction keywords; omit whenraw/already defines the seed set--no-introduction(optional): disable external discovery; use only when the user explicitly requests it- User-owned sources under
raw/papers/,raw/notes/,raw/web/
Outputs
wiki/scaffold and provisional pages (Summary, topics, ideas, concepts)raw/tmp/andraw/discovered/prepared sources- Final paper pages via parallel
/ingestsubagents .checkpoints/init-*.jsonmanifests for resume and replay- Updated
wiki/index.md,wiki/log.md,wiki/graph/* - Refreshed visualization artifacts:
wiki/.obsidian/graph.json(per-entity-type color groups) andwiki/canvases/*.canvas(best-effort, see Step 6). The interactive web Graph view is served bytools/serve.py(SPA), not regenerated as a standalone file.
Wiki Interaction
Reads
raw/papers/,raw/notes/,raw/web/.checkpoints/init-prepare.jsonand.checkpoints/init-sources.jsonfor resume, planning, and fan-outwiki/index.mdplus existingwiki/topics/,wiki/ideas/,wiki/concepts/,wiki/methods/for duplicate avoidance and scaffold alignment
Writes
wiki/scaffold and provisional pagesraw/tmp/andraw/discovered/wiki/index.md,wiki/log.md,wiki/graph/*.checkpoints/init-prepare.json,.checkpoints/init-plan.json,.checkpoints/init-sources.json, andinit-sessioncheckpoint metadata
Graph edges created
/inititself creates only scaffold-level edges when provisional pages need them- all paper-driven edges are delegated to
/ingest
What ships with it
3 files 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.
- 2mo ago First seen · 283 lines · 19 tokens per session scan A 681c4c42ed05
init is a skill published in the GitHub repository skyllwt/AutoSci (1,706 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 4,365 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.
init compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| initskyllwt/AutoSci | Skill | 1.7k | 1mo ago | MIT | A | 19 | 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 |