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/novelty/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/novelty)<a href="https://agentmods.dev/skills/skyllwt/autosci/novelty"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/novelty.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.02729 |
| Opus 5 | $0.00020 | $0.01365 |
| Sonnet 5 | $0.00008 | $0.00546 |
| Haiku 4.5 | $0.00004 | $0.00273 |
Grade A, and why
novelty 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 9d 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 — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/novelty
Verify the novelty of a research idea or method using multiple sources. Searches WebSearch, Semantic Scholar, existing wiki work, and arXiv recent preprints, then Review LLM cross-verifies. Outputs a novelty score (1-5), closest prior work, differentiation points, and next-step recommendations. Can be used standalone or called by /ideate Phase 4.
Inputs
target: one of the following:- free-text description of the idea (a paragraph or a few sentences)
- slug of an ideas/ page in the wiki (e.g.
sparse-lora-for-edge-devices) - paper title or arXiv URL (check novelty of that paper's method)
--quick: fast mode, skip Review LLM cross-verify (Step 3), search only--verbose: output full search results, not just summaries--write(optional, default off): persist the resultingnovelty_scoreto the target's frontmatter. Only takes effect whentargetis an idea slug (i.e.wiki/ideas/{slug}.mdexists). Free-text targets and paper-novelty checks remain read-only regardless of this flag. Treat as a user-owned flag —/ideatePhase 4 sets it explicitly when calling/novelty; do not infer it from repo state.
Outputs
- Novelty Report (output to terminal):
- Novelty Score (1-5)
- List of closest prior work (top 3-5)
- Differentiation points versus each prior work
- Review LLM cross-verify assessment (unless --quick)
- Recommended action: proceed / modify / abandon
- Idea page write (only when
--writeis set AND target is an idea slug): updateswiki/ideas/{slug}.mdfrontmatternovelty_scorefield viatools/research_wiki.py set-meta. No other field is touched.
Wiki Interaction
Reads
wiki/papers/*.md— search existing papers for similar methodswiki/concepts/*.md— check concept overlapwiki/methods/*.md— check for already-cataloged methods that overlap with the candidatewiki/ideas/*.md— check for duplication with existing ideas (especiallyfailure_reasonof failed ideas)wiki/graph/context_brief.md— global context to assist search
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.
- 9d ago First seen · 218 lines · 40 tokens per session scan A 3ae4d8a8c765
novelty is a skill published in the GitHub repository skyllwt/AutoSci (1,663 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 2,729 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.
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