omnisci

omnisci is a skill for Claude Code, Codex from Omni-Scientist/OmniScientist. It costs 145 tokens per session (4,873 once invoked), scanned A, original, MIT.

A command-line research workflow that turns raw data and an open research question into observations, testable hypotheses, analyses, citations, and a draft paper bundle. It uses DeepSeek V4 Flash as the scientist and command-line tools as instruments.

In plain words
What is it for?
Use it to analyze images, signals, audio, video, 3-D data, tables, or graphs; create evidence and hypotheses; and produce a cited PDF and Overleaf project.
Why use it?
It organizes the research process and handles mechanical work such as rendering data, running analysis code, finding references, creating LaTeX, and checking required stages.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to analyze images, signals, audio, video, 3-D data, tables, or graphs; create evidence and hypotheses; and produce a cited PDF and Overleaf project.

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Install with agentmods
npx agentmods add skills/omni-scientist/omniscientist/omnisci
Install

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.

Any agent
npx skills add Omni-Scientist/OmniScientist --skill omnisci
Clone the repo
git clone --depth 1 https://github.com/Omni-Scientist/OmniScientist

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for omnisci

README.md
[![agentmods](https://agentmods.dev/badge/skills/omni-scientist/omniscientist/omnisci/github.svg)](https://agentmods.dev/skills/omni-scientist/omniscientist/omnisci)
Your own site
<a href="https://agentmods.dev/skills/omni-scientist/omniscientist/omnisci"><img src="https://agentmods.dev/badge/skills/omni-scientist/omniscientist/omnisci/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for omnisci

Your own site · 80×15
<a href="https://agentmods.dev/skills/omni-scientist/omniscientist/omnisci"><img src="https://agentmods.dev/badge/skills/omni-scientist/omniscientist/omnisci.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 145 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,873 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00145 $0.04873
Opus 5 $0.00072 $0.02436
Sonnet 5 $0.00029 $0.00975
Haiku 4.5 $0.00015 $0.00487

Measured 7d ago against content hash a422f8204c6d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

omnisci 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 7d ago.

The scan reads SKILL.md. This mod also ships 15 executable files (bin/case_cli.py, bin/evidence_cli.py, bin/gate_cli.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

cli/skills/omnisci/SKILL.md · 309 lines

How it starts

The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.

omnisci: DeepSeek is the scientist, the CLIs are the instruments

Do the science in the current CLI session. The python here does only what a model must not do by hand: render raw data into something viewable, run analysis code, fetch real references, assemble LaTeX, and enforce the gates. These CLIs never call another model API. DeepSeek V4 Flash remains the scientist and author. Because the official DeepSeek endpoint accepts text only, the CLI's view_image sends pixels to its fixed, vision-capable sidecar and returns only the factual observation to DeepSeek. This follows OmniScientist's own text-backbone plus VISION_SIDECAR design.

Where the commands live

The CLIs ship inside this skill and the launcher sets OMNISCI. Confirm it once and use it in every command:

test -n "$OMNISCI" && test -f "$OMNISCI/evidence_cli.py"
python3 $OMNISCI/evidence_cli.py --help            # confirm before going further

--task takes a case directory (absolute paths always work), or a bare name that resolves under $OMNISCI_CASES or the engine's bundled examples/. Every other path you pass (a script, a figure, a .tex, a sections.json) is resolved relative to the case directory.

Every command echoes the case it resolved. Check it on your first call. A bare name can land on a bundled example that already holds someone else's recorded runs, and the gate would then happily ground your paper's numbers against their ledger. When the case is the user's own folder, pass its absolute path.

Three state-changing steps are OmniScientist tools, not shell commands: omnisci_record, omnisci_bib, and omnisci_compile. Call them through the tool protocol. They run the packaged CLIs with argument arrays and return receipts that the final delivery verifier binds to the current files. A bash call to the same Python CLI may help diagnose a failure, but it cannot satisfy final delivery.

Before the loop: is there a case?

A case is a directory holding a series.json. Bundled demos have one. A real user almost never does: they have a folder of images, recordings or volumes, and a question. Build the case first, from their folder:

Read the full file on GitHub · 309 lines

Changes

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.

  1. 7d ago Changed · +31 lines a422f8204c6d
  2. 12d ago First seen · 278 lines · 145 tokens per session scan A e6590e40ffb7

Subscribe to this mod's changes

omnisci is a skill published in the GitHub repository Omni-Scientist/OmniScientist (153 stars, last pushed 9d ago), licensed MIT. It adds 145 tokens to every session and 4,873 once invoked, about $0.0007 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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