bootloops: BootLoops 1.0: certified computational tools and house engines for exact and high-precision physics

by BootLoops-ai · Agent Tool · ★ 403

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 61/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

bootloopsfeynman-integralsllm-agentsphysicsscientific-computing

Quick Facts

Stars403
Forks66
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score61/100
Open Issues2
Last Updated2026-10-22
Created2026-10-01
Platformspython
Est. Tokens~17k

About bootloops

BootLoops BootLoops 1.0 is a harness for large language models doing precision quantitative science. At its core it is an extensive scientific software package (written, ported, and upgraded to be driven by an LLM) together with the working protocols that make the model's results checkable.

Compatible Skills

These tools work well together with bootloops for enhanced workflows:

  • osprey — semantic(0.30)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • SciAgent-Skills — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • AlterLab-Academic-Skills — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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