deltasci-ground
The grounding layer for AI-assisted research: scan the real prior art around an idea, gauge how crowded or open the gap is, and verify every citation against the source of truth. You (the agent) supply the discovery intelligence — writing search queries, judging relevance, reasoning about the gap — while the deterministic deltasci engine fetches real records and runs the citation checks. No LLM ever sits in the trust path: a citation is "verified" only when the engine says so, never from your memory. Use when a researcher wants to ground an idea, a related-work section, or a whole paper agai
Profile →npx skills add boheling/deltasci --skill skill-ground --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DeltaScience: The Grounding Layer (scan → gap → verify) ## Purpose Ground an AI-assisted research idea or draft against the **real record**, in three moves: 1. **Scan** — find the closest existing work (prior art) across OpenAlex, arXiv, PubMed, GitHub. 2. **Gap** — judge whether that space is crowded, contested, or open, and name the distinguishing angle. 3. **Verify** — check every citation ag
What does the deltasci-ground skill do?
The grounding layer for AI-assisted research: scan the real prior art around an idea, gauge how crowded or open the gap is, and verify every citation against the source of truth. You (the agent) supply the discovery intelligence — writing search queries, judging relevance, reasoning about the gap — while the deterministic deltasci engine fetches real records and runs the citation checks. No LLM ever sits in the trust path: a citation is "verified" only when the engine says so, never from your memory. Use when a researcher wants to ground an idea, a related-work section, or a whole paper agai
How do I install it?
Run `npx skills add boheling/deltasci --skill skill-ground --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From boheling/deltasci, a repository with 143 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.