Interactive training for the GitHub Copilot CLI. Guided lessons, quizzes, scenario challenges, and a full reference covering slash commands, shortcuts, modes, agents, skills, MCP, and configuration. Say "cliexpert" to start.
npx skills add https://github.com/github/awesome-copilot --skill cli-mastery
UTILITY SKILL — interactive Copilot CLI trainer.
INVOKES: ask_user, sql, view
USE FOR: "cliexpert", "teach me the Copilot CLI", "quiz me on slash commands", "CLI cheat sheet", "copilot CLI final exam"
DO NOT USE FOR: general coding, non-CLI questions, IDE-only features
| Trigger | Action |
|---------|--------|
| "cliexpert", "teach me" | Read next references/module-N-*.md, teach |
| "quiz me", "test me" | Read current module, 5+ questions via ask_user |
| "scenario", "challenge" | Read references/scenarios.md |
| "reference" | Read relevant module, summarize |
| "final exam" | Read references/final-exam.md |
Specific CLI questions get direct answers without loading references.
Reference files in references/ dir. Read on demand with view.
On first interaction, initialize progress tracking:
CREATE TABLE IF NOT EXISTS mastery_progress (key TEXT PRIMARY KEY, value TEXT);
CREATE TABLE IF NOT EXISTS mastery_completed (module TEXT PRIMARY KEY, completed_at TEXT DEFAULT (datetime('now')));
INSERT OR IGNORE INTO mastery_progress (key,value) VALUES ('xp','0'),('level','Newcomer'),('module','0');
XP: lesson +20, correct +15, perfect quiz +50, scenario +30.
Levels: 0=Newcomer 100=Apprentice 250=Navigator 400=Practitioner 550=Specialist 700=Expert 850=Virtuoso 1000=Architect 1150=Grandmaster 1500=Wizard.
Max XP from all content: 1600 (8 modules × 145 + 8 scenarios × 30 + final exam 200).
When module counter exceeds 8 and user says "cliexpert", offer: scenarios, final exam, or review any module.
Rules: ask_user with choices for ALL quizzes/scenarios. Show XP after correct answers. One concept at a time; offer quiz or review after each lesson.
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.
Coding Agent Session Search - unified CLI/TUI to index and search local coding agent history from Claude Code, Codex, Gemini, Cursor, Aider, ChatGPT, Pi-Agent, Factory, and more. Purpose-built for AI agent consumption with robot mode.
Destructive Command Guard - High-performance Rust hook for Claude Code that blocks dangerous commands before execution. SIMD-accelerated, modular pack system, whitelist-first architecture. Essential safety layer for agent workflows.
Makepad UI development skills for Rust apps: setup, patterns, shaders, packaging, and troubleshooting.
Secure environment variable management ensuring secrets are never exposed in Claude sessions, terminals, logs, or git commits
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Take github/cli-mastery from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.