prompt-challenges: A practical LLM learning path from clear prompting to tool use, IDE collaboration, MCP, Skills

by erweixin · MCP Server · ★ 51

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

Security audit verdict: SAFE · quality 64/100

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.

llmprompt-engineeringprompt-learningprompt-toolkitprompts

Quick Facts

Stars51
Forks6
LanguageTypeScript
CategoryMCP Server
Quality Score64/100
Last Updated2026-04-10
Created2025-04-02
Platformsmcp, node
Est. Tokens~41k

About prompt-challenges

Prompt Challenges A learning path for everyday users: from one-shot chatbot queries to Cursor-style IDE collaboration and OpenClaw-style local agent governance with MCP, Skills, and harness thinking. Built with Astro 5, deployed for Cloudflare Pages (static pages + Worker for ).

prompt-challenges alternative? Top 6 similar tools

Looking for a prompt-challenges alternative? If you're comparing prompt-challenges with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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    🪼 Lagune AI is your security copilot as you build, your Blue Team when you audit, whether you're a developer

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    LLM prompt language based on Jinja. Banks provides tools and functions to build prompts text and chat messages

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  • TagForge by SiliconeShojo · ⭐ 69

    Advanced AI tagging and prompt generation studio.

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    A collection of AI agent skills for Clawdbot, Claude Code, Codex

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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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