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Found 40 Skills
Expert guide for creating GitHub Copilot customization files in VS Code: custom instructions (.instructions.md), prompt files (.prompt.md), custom agents (.agent.md), agent skills (SKILL.md), hooks (JSON), and agent plugins. Use this skill whenever the user asks about customizing Copilot behavior, creating reusable AI workflows, writing copilot-instructions.md, building custom chat agents, automating Copilot tasks with prompt files, or setting up agent skills and hooks in VS Code. Also trigger when the user asks which Copilot customization type to use for a given scenario — always start with the decision matrix below.
Create, track, retrieve, update, and delete custom business metrics for AI Configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.
Create and configure configs in LaunchDarkly. Helps you choose between agent vs completion mode, create the config, add variations with models and prompts, and verify the setup.
Configure ignore patterns in GrepAI. Use this skill when excluding files and directories from indexing.
Sync user-level AI configs (~/.claude/, ~/.codex/) with the dotfiles/ directory. Use when dotfiles drift, after sessions that change permissions/hooks, on new machines, or when the user says "sync", "pull dotfiles", "push configs".
Instrument an existing codebase with LaunchDarkly AI Config tracking. Walks the four-tier ladder (managed runner → provider package → custom extractor + trackMetricsOf → raw manual) and picks the lowest-ceremony option that still captures duration, tokens, and success/error.
Sets up or repairs the AGENTS.md source-of-truth pattern for any project. Creates a well-structured AGENTS.md with real stack info auto-detected from the project, then wires all AI config satellites (.claude/CLAUDE.md, .github/copilot-instructions.md, .agents/rules/, MEMORY.md) to point to it. Eliminates duplication. Always runs in plan mode — asks before acting. Use this skill whenever the user mentions AGENTS.md, agent config, source of truth for AI rules, setting up Claude/Copilot/Cursor for a project, fixing duplicate AI instructions, or wants to consolidate AI configuration files. Trigger even if the user just says "set up agents" or "fix my AI config".
Configure secret stores in Spice (environment variables, Kubernetes, AWS Secrets Manager, keyring). Use when asked to "configure secrets", "add API keys", "set up credentials", "manage passwords", "use environment variables", or "configure .env file".
Update, archive, and delete LaunchDarkly AI Configs and their variations. Use when you need to modify config properties, change model parameters, update instructions or messages, archive unused configs, or permanently remove them.
Create and manage agent graphs — directed graphs of configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.
Create and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.