pypict-claude-skill — security grade SAFE, quality 68/100

Security audit verdict: SAFE · quality 68/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by omkamal · Claude Skill · ★ 91

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

🔒 Is pypict-claude-skill safe to install? View the security audit →

About pypict-claude-skill

PICT Test Designer - Claude Skill A Claude skill for designing comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing). This skill enables systematic test case design with minimal test cases while maintaining high coverage through pairwise combinatorial testing. 🎯 What is PICT? PICT (Pairwise Independent Combinatorial Testing) is a combinatorial testing tool developed by Microsoft. It generates test cases that efficiently cover all pairwise combinations of parameters while drastically reducing the total number of tests compared to exhaustive testing. Example: Testing a system with 8 parameters and 3-5 values each: Exhaustive testing: 25,920 test cases PICT pairwise testing: 30 test cases (99.88% reduction!) 🚀 Features Automated Test Case Generation: Converts requirements into structured PICT models Constraint-Based Testing: Applies business rules to eliminate invalid combinations Expected Output Generation: Automatically determines expected results for each test case Comprehensive Coverage: Ensures all pairwise parameter interactions are tested Multiple Domains: Works for software functions, APIs, web forms, configurations, and more 📋 Table of Contents...

Quick Facts

Stars91
Forks11
LanguagePython
CategoryClaude Skill
Quality Score67.5217887377194/100
Last Updated2026-03-22
Created2025-10-19
Platformsclaude-code, python
Est. Tokens~6k

Compatible Skills

These tools work well together with pypict-claude-skill for enhanced workflows:

  • agent-skills — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (52%)
  • empower-functions — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (52%)
  • agentscope-samples — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

pypict-claude-skill alternative? Top 2 similar tools

Looking for a pypict-claude-skill alternative? If you're comparing pypict-claude-skill with other claude skill tools, these 2 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • claude-ai-mcp by anthropics · ⭐ 432

    Report issues related to MCP integration with Claude here.

  • skyll by assafelovic · ⭐ 240

    A tool for autonomous agents like OpenClaw to discover and learn skills autonomously

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Frequently Asked Questions

What is pypict-claude-skill?

pypict-claude-skill is A claude skill that generate test cases using N-wise test cases using the pypict library. It is categorized as a Claude Skill with 91 GitHub stars.

What programming language is pypict-claude-skill written in?

pypict-claude-skill is primarily written in Python.

How do I install or use pypict-claude-skill?

You can find installation instructions and usage details in the pypict-claude-skill GitHub repository at github.com/omkamal/pypict-claude-skill. The project has 91 stars and 11 forks, indicating an active community.

What are the best alternatives to pypict-claude-skill?

The top alternatives to pypict-claude-skill on Agent Skills Hub include claude-ai-mcp, skyll. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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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