openai-function-calling: Helper functions to generate JSON schema dicts for OpenAI ChatGPT function calling requests

by jakecyr · Agent Tool · ★ 85

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

How to install openai-function-calling

pip install openai-function-calling

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.

chatgptfunction-callingmachine-learningopenaipython

Quick Facts

Stars85
Forks14
LanguagePython
CategoryAgent Tool
Quality Score71/100
Last Updated2025-03-07
Created2023-06-24
Platformspython
Est. Tokens~11k

About openai-function-calling

OpenAI Function Calling Helper functions to generate JSON schema dicts for OpenAI ChatGPT function calling requests. See the official Function Calling reference for more information. Installation Install from PyPi with: The openai-function-calling package does come with the openai package.

Compatible Skills

These tools work well together with openai-function-calling for enhanced workflows:

  • cursor-agent — semantic(0.15)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (63%)
  • ToolRegistry — semantic(0.35)+complementary+same_lang+similar_pop+shared_platform (57%)
  • mcp-gemini-search — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (56%)

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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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