dhar174

Discovers your full tool environment and amplifies prompts with capability awareness. Suggests optimal tool compositions as non-binding options. Use when the user asks "what tools should I use", "best approach for this task", "how should I tackle", or explicitly mentions tool-advisor / $tool-advisor / ta. Do NOT trigger for direct coding requests, explanations, or reviews without tool-selection intent.

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.

Comprehensive technology-agnostic prompt generator for documenting end-to-end application workflows. Automatically detects project architecture patterns, technology stacks, and data flow patterns to generate detailed implementation blueprints covering entry points, service layers, data access, error handling, and testing approaches across multiple technologies including .NET, Java/Spring, React, and microservices architectures.

Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.

A micro-prompt that reminds the agent that it is an interactive programmer. Works great in Clojure when Copilot has access to the REPL (probably via Backseat Driver). Will work with any system that has a live REPL that the agent can use. Adapt the prompt with any specific reminders in your workflow and/or workspace.

Transforms lessons learned into domain-organized memory instructions (global or workspace). Syntax: `/remember [>domain [scope]] lesson clue` where scope is `global` (default), `user`, `workspace`, or `ws`.

Generate a comprehensive repository summary and narrative story from commit history

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.

Run the test, lint, and typecheck commands for the repository, then summarize failures and next actions for coding agents.

This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.

Use the write_todos tool effectively for task planning and decomposition in Deep Agents. Use when users want to (1) implement task planning with write_todos, (2) break down complex tasks into subtasks, (3) track agent progress through todos, (4) debug why todos aren't completing, (5) design todo structures for different task types (research, coding, analysis), (6) understand todo status lifecycle and best practices, or (7) visualize todo progression from LangSmith traces.

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications.

Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues.

Implement LangGraph error handling with current v1 patterns. Use when users need to classify failures, add RetryPolicy for transient issues, build LLM recovery loops with Command routing, add human-in-the-loop with interrupt()/resume, handle ToolNode errors, or choose a safe strategy between retry, recovery, and escalation.

Initialize and configure LangGraph projects with proper structure, langgraph.json configuration, environment variables, and dependency management. Use when users want to (1) create a new LangGraph project, (2) set up langgraph.json for deployment, (3) configure environment variables for LLM providers, (4) initialize project structure for agents, (5) set up local development with LangGraph Studio, (6) configure dependencies (pyproject.toml, requirements.txt, package.json), or (7) troubleshoot project configuration issues.

Generate an initial operating contract for autonomous Copilot work. Use when starting a new task stream, kickoff session, issue burndown, or multi-step implementation where scope, stop conditions, constraints, acceptance criteria, and execution policy must be explicit before coding.

Run the repositorys test, lint, and typecheck commands, then summarize failures and next actions for coding agents.

Bootstrap or maintain a repo-specific Copilot, Codex, and Claude agent stack by inventorying docs, commands, workflows, and existing AI assets before scaffolding managed guidance.

Analyze connected repositories and deliver end-to-end web deployment strategy with GitHub-native defaults. Use when Codex must (1) research repository structure, workflows, Pages setup, and .github configuration, (2) build a scored decision matrix for deployment options, (3) pause for explicit approval before mutating changes, and (4) implement and verify deployment with local checks plus GitHub CI smoke.

Audit curriculum coverage against instructor runbook structure and repository naming rules for Basics and Advanced modules. Use when Codex needs to detect missing or misnamed Day/Hour lecture files, missing daily assignment artifacts, missing quiz artifacts, autograder-readiness gaps, or when scaffolding standardized placeholders for missing course files.

Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.

Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with render_game_to_text.

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

Generate a map of all files relevant to a task before making changes

Use Copilot Spaces to provide project-specific context to conversations. Use this skill when users mention a "Copilot space", want to load context from a shared knowledge base, discover available spaces, or ask questions grounded in curated project documentation, code, and instructions.

Create GitHub Issue for feature request from specification file using feature_request.yml template.

Create GitHub Issues from implementation plan phases using feature_request.yml or chore_request.yml templates.

Create GitHub Issues for unimplemented requirements from specification files using feature_request.yml template.

Create GitHub Pull Request for feature request from specification file using pull_request_template.md template.

Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/

Create a README.md file for the project

Process and manipulate images using ImageMagick. Supports resizing, format conversion, batch processing, and retrieving image metadata. Use when working with images, creating thumbnails, resizing wallpapers, or performing batch image operations.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications.

Analyze a GitHub user or developer's visible activity across pull requests, issues, commits, reviews, repositories, and discussions. Use when asked to summarize a developer, create a contributor profile, generate a weekly or monthly GitHub roundup, analyze work patterns, prepare GitHub evidence for self-review or promotion, or tell the story of a user's GitHub work.

Bootstrap or maintain a repo-specific Copilot, Codex, and Claude agent stack by inventorying docs, commands, workflows, and existing AI assets before scaffolding managed guidance.

Bootstrap or maintain a repo-specific Copilot, Codex, and Claude agent stack by inventorying docs, commands, workflows, and existing AI assets before scaffolding managed guidance.

This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches.

Implement LangGraph error handling with current v1 patterns. Use when users need to classify failures, add RetryPolicy for transient issues, build LLM recovery loops with Command routing, add human-in-the-loop with interrupt()/resume, handle ToolNode errors, or choose a safe strategy between retry, recovery, and escalation.

Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications. Use when users want to (1) implement multi-agent systems, (2) coordinate multiple specialized agents, (3) choose between coordination patterns, (4) set up supervisor-subagent workflows, (5) implement router-based agent selection, (6) create parallel orchestrator-worker patterns, (7) implement agent handoffs, (8) design state schemas for multi-agent systems, or (9) debug multi-agent coordination issues.

Create a README.md file for the project

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

Analyze connected repositories and deliver end-to-end web deployment strategy with GitHub-native defaults. Use when Codex must (1) research repository structure, workflows, Pages setup, and .github configuration, (2) build a scored decision matrix for deployment options, (3) pause for explicit approval before mutating changes, and (4) implement and verify deployment with local checks plus GitHub CI smoke.