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/review/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/review)<a href="https://agentmods.dev/skills/skyllwt/autosci/review"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/review/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/review"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/review.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 | $0.00026 | $0.03056 |
| Opus 5.5 | $0.00010 | $0.01222 |
| Sonnet 5.5 | $0.00005 | $0.00611 |
| Haiku 4.5 | $0.00003 | $0.00306 |
Grade A, and why
review 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/review
Review any research artifact (idea, proposal, experiment plan, paper draft, method) using cross-model review. Uses Review LLM as an independent reviewer. Outputs a structured score, actionable improvement suggestions, and a mapping to wiki entities (which ideas/methods need strengthening, which gaps are discovered). Supports three difficulty levels (standard / hard / adversarial) and four review focuses. Can be used standalone or called by /ideate, /refine, /exp-design.
Inputs
artifact: the artifact to review, one of:- slug of a wiki page (e.g.
sparse-lora-for-edge-devices, searched in ideas/experiments/methods/) - file path (e.g.
wiki/outputs/paper-draft-v1.md) - free text (directly pasted proposal or idea description)
- slug of a wiki page (e.g.
--difficulty(optional, defaultstandard):standard: single-round review, delivers structured feedbackhard: multi-round dialogue (up to 3 rounds), Claude rebuts each weaknessadversarial: multi-round dialogue (up to 3 rounds), Review LLM additionally attempts to find fatal flaws, simulating the harshest reviewer
--focus(optional, default comprehensive review):method: focus on technical correctness, novelty, and feasibility of method designevidence: focus on sufficiency of evidence, experimental rigor, idea/method supportwriting: focus on clarity, structural organization, and argumentative logiccompleteness: focus on missing content (related work, ablations, baselines)
Outputs
- Review Report (output to terminal):
- Overall Score (1-10)
- Strengths (list of positives)
- Weaknesses (list of issues, ranked by severity)
- Questions (reviewer questions)
- Actionable Suggestions (improvement suggestions ranked by priority)
- Wiki Entity Mapping (which ideas/methods need strengthening, which gaps were found)
- Verdict:
ready/needs-work/major-revision/rethink
- If
--difficulty >= hard: additionally includes multi-round dialogue history and final revised score - This skill does not directly modify the wiki, but outputs a list of suggested wiki updates
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 · 285 lines · 26 tokens per session scan A a8485034e0ee
review is a skill published in the GitHub repository skyllwt/AutoSci (1,706 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 3,056 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.
review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| reviewskyllwt/AutoSci | Skill | 1.7k | 1mo ago | MIT | A | 26 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | 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 |