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Agent skill

Scientific Runtime

by ZimoLiao in ZimoLiao/scholaraio

A skill your agent uses when serving scientific CLI tasks through ScholarAIO, especially when the agent should prefer scholaraio toolref, handle partial coverage safely, or avoid turning user work…

MITAuto-check passed

Install Scientific Runtime

skills CLI
$ npx skills add ZimoLiao/scholaraio --skill scientific-runtime -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ZimoLiao/scholaraio scientific-runtime --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ZimoLiao/scholaraio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/scientific-runtime .claude/skills/scientific-runtime && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scientific-runtime
GitHub stars
577
Token cost
~1.1k tokens
SKILL.md length
551 words
Files
1
Skills in repo
43
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when serving scientific CLI tasks through ScholarAIO, especially when the agent should prefer scholaraio toolref, handle partial coverage safely, or avoid turning user work…

  • Works in 7 steps: Identify the scientific tool or sub-tool… → Use the tool-specific skill for workflow… → Use toolref first for commands,… → …
  • Serving scientific CLI tasks through ScholarAIO
  • SKILL.md covers Core Principle, Runtime Protocol, Toolref-First Behavior and When Toolref Is Incomplete, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scientific Runtime is an agent skill from ZimoLiao/scholaraio. Use when serving scientific CLI tasks through ScholarAIO, especially when the agent should prefer scholaraio toolref, handle partial coverage safely, or avoid turning user work into documentation maintenance.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Scholar All-In-One: A research infrastructure for AI agents. The licence is MIT.

When your agent uses it

  • Serving scientific CLI tasks through ScholarAIO
  • Especially when the agent should prefer scholaraio toolref
  • Handle partial coverage safely
  • Avoid turning user work into documentation maintenance

Example prompts

  • “/scientific-runtime”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Identify the scientific tool or sub-tool that matches the problem.
  2. Use the tool-specific skill for workflow and scientific norms.
  3. Use toolref first for commands, parameters, program pages, and option meanings.
  4. If toolref is sufficient, continue normally.
  5. If toolref is partial, fall back to official docs and continue the task.
  6. Mention the coverage gap briefly only when it affects confidence or maintainability.
  7. Do not turn the current user task into documentation maintenance work.

What it can do on your machine

Read from SKILL.md and the folder at commit 777628b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scientific Runtime loads about 1.1k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 551 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ZimoLiao/scholaraio at commit 777628b, republished under its MIT licence (© ZimoLiao). 551 words, ~1,079 tokens.

Download SKILL.mdSave it as .claude/skills/scientific-runtime/SKILL.md (or your agent's skills folder).
name
scientific-runtime
description
Use when serving scientific CLI tasks through ScholarAIO, especially when the agent should prefer scholaraio toolref, handle partial coverage safely, or avoid turning user work into documentation maintenance.

Scientific Runtime Protocol

This is a shared runtime skill for scientific CLI work.

It is not a tool manual. It tells the agent how to behave when serving real users on scientific tool tasks.

Use it alongside a tool-specific scientific skill such as:

  • quantum-espresso
  • lammps
  • gromacs
  • openfoam
  • bioinformatics

Core Principle

ScholarAIO is for users, not for people who want to co-maintain the internal documentation layer.

So the agent should absorb complexity whenever possible.

The user should experience:

  • natural language help
  • reliable parameter lookup
  • graceful fallback when coverage is partial

The user should not experience:

  • being asked to manually patch toolref
  • being forced to learn internal parser gaps
  • being blocked because a documentation layer is imperfect

Runtime Protocol

For any scientific CLI task:

  1. Identify the scientific tool or sub-tool that matches the problem.
  2. Use the tool-specific skill for workflow and scientific norms.
  3. Use toolref first for commands, parameters, program pages, and option meanings.
  4. If toolref is sufficient, continue normally.
  5. If toolref is partial, fall back to official docs and continue the task.
  6. Mention the coverage gap briefly only when it affects confidence or maintainability.
  7. Do not turn the current user task into documentation maintenance work.

Toolref-First Behavior

The agent should prefer:

  • scholaraio toolref show <tool> ... for precise lookups
  • scholaraio toolref search <tool> "..." for natural-language entry

The stable public surfaces are:

  • the scholaraio toolref ... CLI
  • the top-level scholaraio.stores.toolref package facade

The agent should not route users through internal implementation modules such as:

  • scholaraio.stores.toolref.fetch
  • scholaraio.stores.toolref.manifest
  • scholaraio.stores.toolref.storage
  • scholaraio.stores.toolref.search

Those internal module boundaries may change during refactors. User-facing guidance should stay anchored to the CLI and the top-level package behavior.

Before writing configuration or scripts, first resolve:

  • which program or subcommand is relevant
  • which parameters are high-risk
  • which defaults or restrictions matter for validity
Show full SKILL.md (255 more words)Show less

When Toolref Is Incomplete

If toolref does not fully answer the question:

  • continue using the official documentation source
  • clearly separate "task progress" from "maintenance opportunity"
  • do not ask the user to stop and repair the docs layer first
  • do not expose internal refactor details unless they materially affect current behavior

Use this pattern:

  • "I used toolref for the main entry point."
  • "For this deeper detail, I fell back to the official docs because current coverage is partial."

Escalation Rule

Escalate a gap to onboarding or maintenance only when:

  • the same gap appears repeatedly
  • it blocks a common task
  • it affects correctness, not just convenience

If it is a one-off edge case, do not derail the user task.

Separation Of Responsibilities

  • tool-specific skill: when to use the tool, workflow, scientific norms
  • toolref: interface and parameter reference
  • scientific runtime: how to behave under uncertainty or partial coverage

When code changes are involved:

  • preserve the public scholaraio.stores.toolref entry surface
  • treat package-internal reorganizations as an implementation detail
  • if a refactor changes behavior visible through CLI or top-level imports, treat that as a regression until proven otherwise

Anti-Patterns

Do not:

  • dump raw flags from memory
  • tell the user to "go improve toolref first"
  • confuse a successful CLI run with a valid scientific result
  • replace scientific judgment with parameter lookup alone
  • instruct the user to use internal module names as if they were the supported interface

Output Style

When answering the user:

  • keep maintenance details short
  • foreground scientific progress and decision-making
  • mention fallback only when it materially changes confidence or provenance

© ZimoLiao, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/scientific-runtime of ZimoLiao/scholaraio.

Open the folder on GitHubat commit 777628b

Compare with similar skills

Scientific Runtime next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scientific Runtime compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scientific Runtime this skillZimoLiao/scholaraio577—~1.1kAutomated safety check: PassMIT
Scientific VisualizationK-Dense-AI/scientific-agent-skills48k1 repos~3.9kAutomated safety check: NotesMIT
Scientific SchematicsK-Dense-AI/scientific-agent-skills48k1 repos~5.1kAutomated safety check: NotesMIT
Scientific SlidesK-Dense-AI/scientific-agent-skills48k1 repos~5.4kAutomated safety check: NotesMIT
Scientific Critical ThinkingK-Dense-AI/scientific-agent-skills48k1 repos~3.3kAutomated safety check: PassMIT
Scientific Thinking Literature Reviewaffaan-m/ECC276k1 repos~1.3kAutomated safety check: PassMIT

Similar skills

  • Scientific Visualization

    K-Dense-AI/scientific-agent-skills

    Creates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly.

    48k GitHub starsUsed in 1 repo~3.9k tokens
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    Generates scientific diagram drafts using Nano Banana 2 AI with smart iterative refinement.

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Questions about Scientific Runtime

What does Scientific Runtime do?

A skill your agent uses when serving scientific CLI tasks through ScholarAIO, especially when the agent should prefer scholaraio toolref, handle partial coverage safely, or avoid turning user work…. Scientific Runtime is an agent skill from ZimoLiao/scholaraio. Use when serving scientific CLI tasks through ScholarAIO, especially when the agent should prefer scholaraio toolref, handle partial coverage safely, or avoid turning user work into documentation maintenance.

When should I use Scientific Runtime?

Scientific Runtime fits situations like: serving scientific CLI tasks through ScholarAIO; especially when the agent should prefer scholaraio toolref; handle partial coverage safely; avoid turning user work into documentation maintenance.

How do I install Scientific Runtime in Claude Code?

Run `npx skills add ZimoLiao/scholaraio --skill scientific-runtime -a claude-code`. Or copy the skill folder (.claude/skills/scientific-runtime in ZimoLiao/scholaraio) into .claude/skills/scientific-runtime in your project. Claude Code loads it when a task matches its description.

How do I install Scientific Runtime in Codex?

Run `npx skills add ZimoLiao/scholaraio --skill scientific-runtime -a codex`. Or copy the skill folder (.claude/skills/scientific-runtime in ZimoLiao/scholaraio) into .agents/skills/scientific-runtime in your project. Codex loads it when a task matches its description.

Can I use Scientific Runtime in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ZimoLiao/scholaraio --skill scientific-runtime -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scientific-runtime, .gemini/skills/scientific-runtime, .github/skills/scientific-runtime and .opencode/skills/scientific-runtime in your project.

What does Scientific Runtime need to run?

SKILL.md names no scripts, command-line tools or credentials: Scientific Runtime is instructions for the agent only.

Does Scientific Runtime access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scientific Runtime safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scientific Runtime use?

Scientific Runtime is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scientific Runtime use?

About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scientific Runtime?

Skills that share tags, products or a category with Scientific Runtime: Scientific Visualization (K-Dense-AI/scientific-agent-skills, 48k stars), Scientific Schematics (K-Dense-AI/scientific-agent-skills, 48k stars), Scientific Slides (K-Dense-AI/scientific-agent-skills, 48k stars) and Scientific Critical Thinking (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scientific Runtime?

ZimoLiao (a GitHub user) maintains it in ZimoLiao/scholaraio, which has 577 GitHub stars. The repository holds 43 skills in this directory. The repository was last updated on September 25, 2026.

Source: ZimoLiao/scholaraio on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.