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 agentmods add skills/boheling/deltasci/skillnpx skills add boheling/deltasci --skill skillgit 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)<a href="https://agentmods.dev/skills/boheling/deltasci/skill"><img src="https://agentmods.dev/badge/skills/boheling/deltasci/skill.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 | $0.00117 | $0.01759 |
| Opus 5 | $0.00059 | $0.00879 |
| Sonnet 5 | $0.00023 | $0.00352 |
| Haiku 4.5 | $0.00012 | $0.00176 |
Grade A, and why
deltasci 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 5d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeltaScience: Two-Perspective Co-Reasoning
Purpose
Generate AI4Science research hypotheses that are:
- Grounded — every factual claim is tagged with its evidence type, source, and the AI's self-assessed training coverage.
- Honest about AI's epistemic edges — claims outside the AI's training distribution are emitted as
KNOWLEDGE_GAPfor the researcher, not fabricated. - Explicit about creative leaps — connections the AI is proposing (not citing) are emitted as
NOVEL_SYNTHESIS. - Falsifiable — every hypothesis ships with a measurable threshold for being wrong.
- Domain-aware — uses a pluggable lens specific to the scientific field.
- Two-perspective — alternates between domain expert and ML engineer.
This is the Claude Code skill version of the deltasci Python package. They share the same domain packs and grounding rules.
The three first-class tags
[CLAIM type=<TYPE> coverage=<COVERAGE> source="<CITATION>"]<text>[/CLAIM]
[KNOWLEDGE_GAP category=<CATEGORY>]<question for the researcher>[/KNOWLEDGE_GAP]
[NOVEL_SYNTHESIS rationale="<one-line>"]<the proposed connection>[/NOVEL_SYNTHESIS]
coverage ∈ {well-covered, sparse}. uncovered is not allowed on a CLAIM — for that, emit a KNOWLEDGE_GAP instead.
A round with zero KNOWLEDGE_GAPs and zero NOVEL_SYNTHESES is suspect — it suggests the AI is claiming complete certainty across the entire research idea, which is itself a hallucination signal. Synthesis refuses by default.
Inputs
| Parameter | Required | Description |
|---|---|---|
idea |
Yes | The raw research idea. |
pack |
Yes | Domain pack name (biomed, materials, climate) or path to a custom pack. |
context_dir |
No | Directory of background papers/notes. |
out_dir |
No | Where to write outputs. Default: ./deltasci-output/. |
Outputs
| File | Description |
|---|---|
transcript.md |
Full 4-round dialogue with all three tag types. |
hypothesis.md |
Three-section evidence trail (well-covered / sparse / researcher-required) + falsifiability + scorecard. |
summary.json |
Machine-readable hypothesis schema + epistemic summary. |
What ships with it
7 files 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.
- 5d ago First seen · 142 lines · 117 tokens per session scan A 4fd1daa11f25
deltasci is a skill published in the GitHub repository boheling/deltasci (143 stars, last pushed 3mo ago), licensed MIT. It adds 117 tokens to every session and 1,759 once invoked, about $0.0006 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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