deltasci-ground
deltasci-ground is a Literature review & citations skill for AI agents, published by boheling in the deltasci repository on GitHub. Skillspace rates deltasci-ground D (59/100) and it passed the safety scan; the rating is based on static analysis only.
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 against the real literature — find the closest existing work, see where the genuine opening is, and catch fabricated / wrong-paper / unsupported citations.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 143
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.