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/refinenpx skills add skyllwt/AutoSci --skill refinegit 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/refine)<a href="https://agentmods.dev/skills/skyllwt/autosci/refine"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/refine.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 | $0.00031 | $0.02442 |
| Opus 5 | $0.00015 | $0.01221 |
| Sonnet 5 | $0.00006 | $0.00488 |
| Haiku 4.5 | $0.00003 | $0.00244 |
Grade A, and why
refine 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/refine
General-purpose multi-round iterative improvement loop for any research artifact (idea, proposal, experiment plan, paper draft). Each round calls /review for structured feedback → parses actionable items → Claude fixes the artifact → updates wiki entities → re-reviews, until the score reaches the target or the maximum rounds are exhausted. Outputs an improvement history and the final review score.
Inputs
artifact: the artifact to improve, one of:- slug of a wiki page (searched in ideas/experiments/methods/outputs/)
- file path (e.g.
wiki/outputs/paper-draft-v1.md)
--max-rounds N(optional, default 4): maximum iteration rounds--target-score N(optional, default 8): target review score (1-10); stop when reached--difficulty(optional, defaulthard): difficulty level passed to /review--focus(optional): review focus passed to /review
Outputs
- Improved artifact (wiki page or file, updated in place)
- Wiki entity updates (if review finds ideas/methods needing strengthening or identifies gaps)
- REFINE_REPORT (output to terminal):
- Score trajectory across all rounds
- Cumulative list of fixed issues
- Final review score and verdict
- Unresolved issues (if any)
Wiki Interaction
Reads
wiki/ideas/*.md— if artifact is an ideawiki/experiments/*.md— if artifact is an experiment planwiki/methods/*.md— methods referenced by the reviewwiki/papers/*.md— papers referenced by the reviewwiki/outputs/*.md— if artifact is a paper draft or outputwiki/graph/context_brief.md— global context passed to /reviewwiki/graph/open_questions.md— check whether new gaps need recording
Writes
wiki/ideas/{slug}.md— if artifact is an idea, fix issues found by reviewwiki/experiments/{slug}.md— if artifact is an experiment planwiki/methods/{slug}.md— if review flags a method gap (e.g. missing source_papers, weak Procedure)wiki/outputs/*.md— if artifact is a paper draft or outputwiki/graph/edges.jsonl— if new relationships are discovered during fixeswiki/graph/context_brief.md— rebuild after each round if wiki changes were madewiki/graph/open_questions.md— rebuild after each round if wiki changes were madewiki/log.md— append operation log
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 · 233 lines · 31 tokens per session scan A da78a43b202e
refine is a skill published in the GitHub repository skyllwt/AutoSci (1,659 stars, last pushed 5d ago), licensed MIT. It adds 31 tokens to every session and 2,442 once invoked, about $0.0002 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.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…