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 skills add boheling/deltasci --skill skill-groundgit clone --depth 1 https://github.com/boheling/deltasciWrote 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/boheling/deltasci/skill-ground)<a href="https://agentmods.dev/skills/boheling/deltasci/skill-ground"><img src="https://agentmods.dev/badge/skills/boheling/deltasci/skill-ground.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 5.1 | $0.00155 | $0.01926 |
| Opus 5 | $0.00077 | $0.00963 |
| Sonnet 5 | $0.00031 | $0.00385 |
| Haiku 4.5 | $0.00015 | $0.00193 |
Grade A, and why
deltasci-ground 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 8d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeltaScience: The Grounding Layer (scan → gap → verify)
Purpose
Ground an AI-assisted research idea or draft against the real record, in three moves:
- Scan — find the closest existing work (prior art) across OpenAlex, arXiv, PubMed, GitHub.
- Gap — judge whether that space is crowded, contested, or open, and name the distinguishing angle.
- Verify — check every citation against the source of truth: does the identifier resolve, does the metadata match, does the cited paper actually support the claim?
The one rule that governs everything: no LLM in the trust path
There are two kinds of work here, and they are not symmetric:
- Discovery (scan, gap) is yours. You are the LLM. Write the queries, judge which results are genuinely relevant, reason about where the gap is. A weak discovery pass can only make you miss a paper — it cannot make a false statement about what exists — so your judgement is welcome here.
- Trust (verify) is the engine's. A citation is real, and supports its claim, only when
deltasci verifysays 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.
Prerequisites
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.
Inputs
| 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 |
Procedure
Step 1 — Frame the idea
Read the idea or the paper's title + abstract. Identify, in the field's standard vocabulary:
- the problem (e.g. "long-horizon sparse-reward credit assignment"),
- the technique (e.g. "group-based reinforcement learning for LLM agents"),
- the application / domain.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 145 lines · 155 tokens per session scan A 426924953458
deltasci-ground is a skill published in the GitHub repository boheling/deltasci (143 stars, last pushed 3mo ago), licensed MIT. It adds 155 tokens to every session and 1,926 once invoked, about $0.0008 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
create-custom-grader
Use when converting an existing benchmark, rubric, verifier, task YAML/JSON, or domain check into SkillEvaluator BYOG/BYOT custom evaluation.
nano-banana
Generate professional presentation slides and high-quality illustrations using Gemini image generation API (Nano Banana 2), with interactive browser-based review and iterative editing. Full workflow: content planning conversation → slidesplan.json → batch image generation → review with feedback → targeted slide…
paper-planning
Guides pre-writing planning for academic papers with 4 structured steps: story design (task-challenge-insight-contribution-advantage), experiment planning (comparisons + ablations), figure design (pipeline + teaser), and 4-week timeline management. Includes counterintuitive planning tactics (write a mock rejection…
evo-memory
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory MI (feasible/unsuccessful directions) and Experimentation Memory ME (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after…
paper-rebuttal
Guides writing effective rebuttals after receiving peer review feedback. Covers review diagnosis (score-driven color-coding), response strategy (champion identification, common-theme consolidation), tactical writing (18 rules), and counterintuitive rebuttal principles. Use when: user received reviewer scores/comments…
agent-ready-cloudflare
Audit and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent…