- Agent GraphsCreate 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.launchdarkly/ai-tooling19
- Aiconfig Agent GraphsDEPRECATED redirect — this skill was renamed to agent-graphs. Do not use this skill; invoke agent-graphs instead. Kept only so old references to aiconfig-agent-graphs still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig Ai MetricsDEPRECATED redirect — this skill was renamed to built-in-metrics. Do not use this skill; invoke built-in-metrics instead. Kept only so old references to aiconfig-ai-metrics still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig CreateDEPRECATED redirect — this skill was renamed to configs-create. Do not use this skill; invoke configs-create instead. Kept only so old references to aiconfig-create still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig Custom MetricsDEPRECATED redirect — this skill was renamed to custom-metrics. Do not use this skill; invoke custom-metrics instead. Kept only so old references to aiconfig-custom-metrics still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig MigrateDEPRECATED redirect — this skill was renamed to migrate. Do not use this skill; invoke migrate instead. Kept only so old references to aiconfig-migrate still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig Online EvalsDEPRECATED redirect — this skill was renamed to online-evals. Do not use this skill; invoke online-evals instead. Kept only so old references to aiconfig-online-evals still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig ProjectsDEPRECATED redirect — this skill was renamed to projects. Do not use this skill; invoke projects instead. Kept only so old references to aiconfig-projects still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig SnippetsDEPRECATED redirect — this skill was renamed to snippets. Do not use this skill; invoke snippets instead. Kept only so old references to aiconfig-snippets still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig TargetingDEPRECATED redirect — this skill was renamed to configs-targeting. Do not use this skill; invoke configs-targeting instead. Kept only so old references to aiconfig-targeting still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig ToolsDEPRECATED redirect — this skill was renamed to tools. Do not use this skill; invoke tools instead. Kept only so old references to aiconfig-tools still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig UpdateDEPRECATED redirect — this skill was renamed to configs-update. Do not use this skill; invoke configs-update instead. Kept only so old references to aiconfig-update still point users to the new name.launchdarkly/ai-tooling19
- Aiconfig VariationsDEPRECATED redirect — this skill was renamed to configs-variations. Do not use this skill; invoke configs-variations instead. Kept only so old references to aiconfig-variations still point users to the new name.launchdarkly/ai-tooling19
- ApplyApply LaunchDarkly SDK onboarding: install dependency (or dual-SDK pair), configure env and secrets with consent, add init at entrypoint(s), verify compile. Nested under sdk-install; next is run.launchdarkly/ai-tooling19
- Built In MetricsInstrument an existing codebase with LaunchDarkly 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.launchdarkly/ai-tooling19
- Configs CreateCreate 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.launchdarkly/ai-tooling19
- Configs TargetingConfigure config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.launchdarkly/ai-tooling19
- Configs UpdateUpdate, archive, and delete LaunchDarkly 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.launchdarkly/ai-tooling19
- Configs VariationsExperiment with configs by creating and managing variations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.launchdarkly/ai-tooling19
- Custom MetricsCreate, track, retrieve, update, and delete custom business metrics for configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.launchdarkly/ai-tooling19
- DetectDetect repository stack for LaunchDarkly SDK onboarding: languages, frameworks, package managers, monorepo targets, entrypoints, existing LD usage. Nested under sdk-install; next is plan.launchdarkly/ai-tooling19
- First FlagCreate a boolean first flag, add evaluation, toggle on/off for end-to-end proof. Parent onboarding Step 6; uses MCP, API, or ldcli; optional flag-create skill.launchdarkly/ai-tooling19
- Launchdarkly Experiment SetupSet up and run experiments in LaunchDarkly. Create experiments with metrics, treatments, and flag config, start iterations to collect data, swap design between iterations, and stop with a winner.launchdarkly/ai-tooling19
- Launchdarkly Flag CleanupSafely remove a feature flag from code while preserving production behavior. Use when the user wants to remove a flag from code, delete flag references, or create a PR that hardcodes the winning variation after a rollout is complete.launchdarkly/ai-tooling19
- Launchdarkly Flag CommandResolve `/flag` style requests into the right LaunchDarkly flag lookup flow. Use when the user types `/flag`, asks to quickly find a flag by name/key, wants a direct flag detail summary, or needs fast disambiguation between similar flags.launchdarkly/ai-tooling19
- Launchdarkly Flag CreateCreate and configure LaunchDarkly feature flags in a way that fits the existing codebase. Use when the user wants to create a new flag, wrap code in a flag, add a feature toggle, or set up an experiment. Guides exploration of existing patterns before creating.launchdarkly/ai-tooling19
- Launchdarkly Flag DiscoveryAudit your LaunchDarkly feature flags to understand the landscape, find stale or launched flags, and assess removal readiness. Use when the user asks about flag debt, stale flags, cleanup candidates, flag health, or wants to understand their flag inventory.launchdarkly/ai-tooling19
- Launchdarkly Flag TargetingControl LaunchDarkly feature flag targeting including toggling flags on/off, percentage rollouts, targeting rules, individual targets, and copying flag configurations between environments. Use when the user wants to change who sees a flag, roll out to a percentage, add targeting rules, or promote config between environments.launchdarkly/ai-tooling19
- Launchdarkly Guarded RolloutConfigure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.launchdarkly/ai-tooling19
- Launchdarkly Metric ChooseChoose the right metrics for a LaunchDarkly experiment, guarded rollout, or release policy. Use when the user wants to know which metrics to use, which is the primary metric for an experiment, what guardrails to add, or which events to monitor in a rollout. Surfaces what will auto-attach from existing release policies before making additional recommendations.launchdarkly/ai-tooling19
- Launchdarkly Metric CreateCreate a LaunchDarkly metric that measures what matters for an experiment or rollout. Use when the user wants to create a metric, track an event, measure page views, button clicks, conversion, latency, error rate, or any custom numeric or binary outcome. Instruments the event first when needed (including SDK setup and .env), then creates and verifies the metric.launchdarkly/ai-tooling19
- Launchdarkly Metric InstrumentInstrument a LaunchDarkly metric event in a codebase by adding a track() call. Use when the user wants to wire up an event, instrument an action for a metric, add tracking to a feature, or confirm that an event is flowing to LaunchDarkly.launchdarkly/ai-tooling19
- Mcp ConfigureConfigure the LaunchDarkly hosted MCP server during onboarding. Use when the parent LaunchDarkly onboarding skill reaches Step 4 (MCP). Supports Cursor, Claude Code, Windsurf, GitHub Copilot, and other MCP-compatible agents. OAuth authentication; no API keys for the hosted server.launchdarkly/ai-tooling19
- MigrateMigrate 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.launchdarkly/ai-tooling19
- OnboardingOnboard a project to LaunchDarkly: kickoff roadmap, resumable log, explore repo, MCP, companion flag skills, nested SDK install (detect/plan/apply), first flag. Use when adding LaunchDarkly, setting up or integrating feature flags in a project, SDK integration, or 'onboard me'.launchdarkly/ai-tooling19
- Online EvalsAttach judges to config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.launchdarkly/ai-tooling19
- PlanGenerate a minimal LaunchDarkly SDK integration plan from detected stack: choose SDK type(s), dual-SDK server+client when required, files to change, env conventions. Nested under sdk-install; follows detect, precedes apply.launchdarkly/ai-tooling19
- ProjectsGuide for setting up LaunchDarkly projects in your codebase. Helps you assess your stack, choose the right approach, and integrate project management that makes sense for your architecture.launchdarkly/ai-tooling19
- Sdk InstallInstall and initialize the correct LaunchDarkly SDK during onboarding by running nested skills in order: detect, plan, apply. Parent onboarding Step 6 is first flag.launchdarkly/ai-tooling19
- SnippetsCreate and manage prompt snippets — reusable text blocks referenced inside config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.launchdarkly/ai-tooling19
- ToolsGive your agents capabilities through tools (function calling). Helps you identify what your agent needs to do, create tool definitions, and attach them to config variations.launchdarkly/ai-tooling19
- Aiconfig Agent GraphsCreate and manage agent graphs — directed graphs of AI Configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.launchdarkly/launchdarkly-github-agent3
- Aiconfig Ai MetricsInstrument 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.launchdarkly/launchdarkly-github-agent3
- Aiconfig CreateCreate and configure AI Configs in LaunchDarkly. Helps you choose between agent vs completion mode, create the config, add variations with models and prompts, and verify the setup.launchdarkly/launchdarkly-github-agent3
- Aiconfig Custom MetricsCreate, 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.launchdarkly/launchdarkly-github-agent3
- Aiconfig MigrateMigrate an application with hardcoded LLM prompts to a full LaunchDarkly AI Configs 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 AI Config, or stage a full hardcoded-to-LaunchDarkly migration.launchdarkly/launchdarkly-github-agent3
- Aiconfig Online EvalsAttach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.launchdarkly/launchdarkly-github-agent3
- Aiconfig ProjectsGuide for setting up LaunchDarkly projects in your codebase. Helps you assess your stack, choose the right approach, and integrate project management that makes sense for your architecture.launchdarkly/launchdarkly-github-agent3
- Aiconfig SnippetsCreate and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.launchdarkly/launchdarkly-github-agent3
- Aiconfig TargetingConfigure AI Config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.launchdarkly/launchdarkly-github-agent3
- Aiconfig ToolsGive your AI agents capabilities through tools (function calling). Helps you identify what your AI needs to do, create tool definitions, and attach them to AI Config variations.launchdarkly/launchdarkly-github-agent3
- Aiconfig UpdateUpdate, 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.launchdarkly/launchdarkly-github-agent3
- Aiconfig VariationsExperiment with AI configurations by creating and managing variations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.launchdarkly/launchdarkly-github-agent3
- Launchdarkly Flag CleanupSafely remove a feature flag from code while preserving production behavior. Use when the user wants to remove a flag from code, delete flag references, or create a PR that hardcodes the winning variation after a rollout is complete.launchdarkly/launchdarkly-github-agent3
- Launchdarkly Flag CreateCreate and configure LaunchDarkly feature flags in a way that fits the existing codebase. Use when the user wants to create a new flag, wrap code in a flag, add a feature toggle, or set up an experiment. Guides exploration of existing patterns before creating.launchdarkly/launchdarkly-github-agent3
- Launchdarkly Flag DiscoveryAudit your LaunchDarkly feature flags to understand the landscape, find stale or launched flags, and assess removal readiness. Use when the user asks about flag debt, stale flags, cleanup candidates, flag health, or wants to understand their flag inventory.launchdarkly/launchdarkly-github-agent3
- Launchdarkly Flag TargetingControl LaunchDarkly feature flag targeting including toggling flags on/off, percentage rollouts, targeting rules, individual targets, and copying flag configurations between environments. Use when the user wants to change who sees a flag, roll out to a percentage, add targeting rules, or promote config between environments.launchdarkly/launchdarkly-github-agent3
- Launchdarkly Guarded RolloutConfigure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.launchdarkly/launchdarkly-github-agent3
- Agent GraphsCreate 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.launchdarkly/agent-skills
- Aiconfig Agent GraphsDEPRECATED redirect — this skill was renamed to agent-graphs. Do not use this skill; invoke agent-graphs instead. Kept only so old references to aiconfig-agent-graphs still point users to the new name.launchdarkly/agent-skills