用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/tomevault-io/skills-registry --skill deltasci-ground命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | deltasci-ground |
| description | > Use when this capability is needed. |
Ground an AI-assisted research idea or draft against the real record, in three moves:
There are two kinds of work here, and they are not symmetric:
deltasci verify says so. Never assert from memory that a PMID/DOI is valid or that a paper supports a claim. The engine fetches the live record and decides deterministically. This is the entire point of the tool: the verdict must not depend on a model that can hallucinate agreement.If you ever catch yourself about to write "this citation looks correct" without having run deltasci verify, stop and run it.
pip install deltasci # core engine (keyless)
pip install 'deltasci[pdf]' # add PDF support for whole-paper input
The engine is deterministic and needs no API key. All three commands emit --json.
| Input | How |
|---|---|
| A research idea / abstract | pass the text |
| A paper or draft PDF | pass the path with --pdf |
| A related-work snippet with citations | pass the text to verify |
Read the idea or the paper's title + abstract. Identify, in the field's standard vocabulary:
Critical: find the paper's own coined names — its method, system, or benchmark names (e.g. a made-up acronym like SkillEvo, WebArena-Lite) — and set them aside. Never search for them. No other work uses those terms, so they return nothing and poison a query. This is the single most common reason a scan finds "no prior art" for a hot area.
Write 3–5 search queries, most-specific first, collectively covering problem + technique + application, using canonical terms and synonyms. Then issue them with the explicit-query primitive:
deltasci scan \
--query "llm agent reinforcement learning skill" \
--query "long-horizon sparse reward credit assignment" \
--query "group relative policy optimization GRPO" \
--json
--sources openalex,arxiv,github.--limit 20 for a wider net.Every hit in the JSON is a real, retrieved record (title, authors, year, venue, url). Read them and rerank by genuine relevance — judge by meaning, not shared words. A paper that merely shares vocabulary but solves a different problem is not close. Do not invent or embellish any record; only use what the engine returned.
If failed_sources is non-empty, a corpus was slow/rate-limited — note it as a coverage gap (the run is incomplete, not empty).
From the real hits, classify the space:
Ground every statement in the listed works, naming them by author and year. State what the works already cover and the one distinguishing angle the idea leaves open. Never invent a paper to fill the story.
Honesty rule on absence: you may call CROWDED or CONTESTED freely (you can't un-find a close match). But only call OPEN if the scholarly sources (OpenAlex / arXiv / PubMed) actually answered. If one failed, the space is INCONCLUSIVE — re-run, never "open." Absence of evidence from a source that didn't respond is not evidence of an open gap.
Optional deterministic cross-check (density-based, keyless):
deltasci gap --query "llm agent reinforcement learning skill" --json
For any draft, related-work section, or paper that contains citations, run the engine. Do not eyeball them.
# A whole paper (parses the bibliography, checks each reference in context):
deltasci verify --pdf paper.pdf --json
# A snippet of prose with inline identifiers:
deltasci verify --text "AlphaFold predicts structure (PMID 34265844). TAMs drive osteosarcoma (PMID 32015508)." --json
Report the per-citation verdicts exactly as the engine returns them:
deltasci verify exits 2 if any audit fails, so it drops straight into CI. Surface FABRICATED / METADATA-MISMATCH / UNSUPPORTED prominently — these are the failures the tool exists to catch.
Give the researcher:
deltasci verify returned PASS — nothing else.deltasci scan --query.deltasci verify (never from memory).Source: boheling/deltasci — distributed by TomeVault.