Borrowing it
Nothing to install: this file belongs to ZimoLiao/scholaraio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ZimoLiao/scholaraio/main/.claude/skills/scientific-runtime/SKILL.mdgit clone --depth 1 https://github.com/ZimoLiao/scholaraioWrote 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/zimoliao/scholaraio/scientific-runtime)<a href="https://agentmods.dev/skills/zimoliao/scholaraio/scientific-runtime"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/scientific-runtime/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.
<a href="https://agentmods.dev/skills/zimoliao/scholaraio/scientific-runtime"><img src="https://agentmods.dev/badge/skills/zimoliao/scholaraio/scientific-runtime.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 84 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00041 | $0.00914 |
| Opus 5 | $0.00020 | $0.00457 |
| Sonnet 5 | $0.00008 | $0.00183 |
| Haiku 4.5 | $0.00004 | $0.00091 |
Grade A, and why
scientific-runtime 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 9d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-espressolammpsgromacsopenfoambioinformatics
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:
- Identify the scientific tool or sub-tool that matches the problem.
- Use the tool-specific skill for workflow and scientific norms.
- Use
toolreffirst for commands, parameters, program pages, and option meanings. - If
toolrefis sufficient, continue normally. - If
toolrefis partial, fall back to official docs and continue the task. - Mention the coverage gap briefly only when it affects confidence or maintainability.
- Do not turn the current user task into documentation maintenance work.
Toolref-First Behavior
The agent should prefer:
scholaraio toolref show <tool> ...for precise lookupsscholaraio toolref search <tool> "..."for natural-language entry
The stable public surfaces are:
- the
scholaraio toolref ...CLI - the top-level
scholaraio.stores.toolrefpackage facade
The agent should not route users through internal implementation modules such as:
scholaraio.stores.toolref.fetchscholaraio.stores.toolref.manifestscholaraio.stores.toolref.storagescholaraio.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.
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.
- 9d ago First seen · 131 lines · 41 tokens per session scan A 58510d510b4f
scientific-runtime is a skill published in the GitHub repository ZimoLiao/scholaraio (570 stars, last pushed 10d ago), licensed MIT. It adds 41 tokens to every session and 914 once invoked, about $0.0002 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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