by DrCatHicks · Codex Skill · ★ 2.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is learning-opportunities safe to install? View the security audit →
Learning Opportunities: A Claude Code Skill for Deliberate Skill Development Build your expertise, not just your projects. This skill uses an adaptive "dynamic textbook" approach to help you integrate science-based expertise building exercises while doing agentic coding. When you complete architectural work (new files, schema changes, refactors), Claude offers optional 10-15 minute learning exercises grounded in evidence-based learning science. The exercises use techniques like prediction, generation, retrieval practice, and spaced repetition to provide you with semi-worked examples from across your own project work. Pairs well with Learning-Goal, a skill that guides you through semi-structured, interactive learning goal-setting using the technique of Mental Contrasting with Implementation Intentions (MCII), an evidence-based exercise. Installation This repository is a Claude Code plugin marketplace. To install: Add the marketplace: Install the plugin: Restart Claude Code to activate For more on Claude Code plugins, see the plugin documentation. Automatic Prompting (Optional) Linux and macOS users can install le
/plugin marketplace add https://github.com/DrCatHicks/learning-opportunities.git
/plugin install learning-opportunities@learning-opportunities
Taken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 2,364 |
| Forks | 86 |
| Language | Shell |
| Category | Codex Skill |
| License | CC-BY-4.0 |
| Quality Score | 64.650666258248/100 |
| Open Issues | 9 |
| Last Updated | 2026-08-19 |
| Created | 2026-02-07 |
| Platforms | claude-code, cli, codex |
| Est. Tokens | ~8k |
These tools work well together with learning-opportunities for enhanced workflows:
Looking for a learning-opportunities alternative? If you're comparing learning-opportunities with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
The first open-source agent skills builder. Define skills by vibe workflow, run on Claude Code, Cursor, Codex
Bridge Claude Code / Codex to IM platforms — chat with AI coding agents from Telegram, Discord, or Feishu/Lark
Skills for pi coding agent (compatible with Claude Code and Codex CLI)
Build tested agent skills and govern their lifecycle through a user-defined marketplace: evidence, discovery,
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart sear
Supercharge AI coding agents with portable skills. Install, translate & share skills across Claude Code, Curso
Explore other popular codex skill tools:
learning-opportunities is A Claude or Codex skill for deliberate skill development during AI-assisted coding. It is categorized as a Codex Skill with 2.4k GitHub stars.
learning-opportunities is primarily written in Shell.
The README on GitHub (github.com/DrCatHicks/learning-opportunities) gives: /plugin marketplace add https://github.com/DrCatHicks/learning-opportunities.git , then /plugin install learning-opportunities@learning-opportunities. Usage details are in the same README.
learning-opportunities is released under the CC-BY-4.0 license, making it free to use and modify according to the license terms.
The top alternatives to learning-opportunities on Agent Skills Hub include refly, Claude-to-IM-skill, pi-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: