SimpleLLMFunc: A simple and well-tailored LLM application framework that enables you to seamlessly integrate LLM capabilities

by NiJingzhe · Agent Tool · ★ 78

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

How to install SimpleLLMFunc

pip install SimpleLLMFunc

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 71/100

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.

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.

agentagent-development-frameworkagent-frameworkagentframeworksagenticagentic-aiagentsaiaigcframework

Quick Facts

Stars78
Forks5
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score71/100
Last Updated2026-07-20
Created2025-04-16
Platformspython
Est. Tokens~298k

About SimpleLLMFunc

LLM as Function, Prompt as Code, Context-Centric Chinese README Update Notes (0.8.4) PyRepl terminal isolation: PyRepl worker processes now isolate fd // from host terminals, capture direct fd writes and subprocess stdout/stderr into tool output, and drop late child-process output so

Compatible Skills

These tools work well together with SimpleLLMFunc for enhanced workflows:

  • Autono — semantic(0.23)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • Agently-Daily-News-Collector — semantic(0.42)+complementary+same_lang+similar_pop+shared_platform (60%)
  • llama-cpp-agent — semantic(0.28)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)

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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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