- Simple EnglishWrite or rewrite text in plain, layman-readable English in the spirit of ASD-STE100 Simplified Technical English: short sentences, active voice, simple tenses, one word one meaning, condition before command, every technical term defined at first use, no AI slop. Default mode is Plain. Strict mode applies full STE vocabulary compliance when the user names STE, ASD-STE100, or compliance. Use for documentation, READMEs, runbooks, procedures, error messages, release notes, incident reports, API guidlaunchdarkly/ldcli30
- Terraform Provider Add ResourceImplement a new resource (and optionally data source) in the LaunchDarkly Terraform provider source code. Use when the user asks to implement a new Terraform resource, add Terraform support for a LaunchDarkly feature, scaffold a new resource, or extend the provider with new resource types. Also use when the user says "add resource", "new resource", "implement resource", or references the terraform-provider-launchdarkly repo in the context of building something new. Do NOT use when the user wantslaunchdarkly/terraform-provider-launchdarkly28
- Terraform Provider Block To Nested AttrsMigrate LaunchDarkly Terraform provider HCL configs between block syntax (provider v2.x and earlier) and nested-attribute syntax (v3.x+). Use when a user upgrades the launchdarkly provider to v3 and hits "Unsupported block type" / "Missing required argument" plan errors, when downgrading from v3 to v2.x, when porting a v2 example to v3 syntax (or vice versa), or when the user mentions "block to nested attribute", "= [{...}]", "v3 plan errors", or pastes errors that reference `inline_roles`, `stalaunchdarkly/terraform-provider-launchdarkly28
- Terraform Provider V3 MigrationMigrate a full LaunchDarkly Terraform v2.x setup to v3 end-to-end, and try/verify the v3 provider's new resources against a real LaunchDarkly account. Use when the user wants to (1) upgrade an entire v2.x configuration to v3 — run migrate-tf-syntax, fix the manual follow-ups, build the v3 provider, apply, and confirm an idempotent state upgrade with zero forced replacements; or (2) exercise and validate the new v3 resources (context_kind, announcement, oauth_client, ai_agent_graph, metric_group,launchdarkly/terraform-provider-launchdarkly28
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- Alert InvestigationInvestigates a triggered observability alert and returns a structured diagnosis with likely cause, scope, and next steps.launchdarkly/ai-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- Create Fix PrInvestigates a root cause and files a minimal fix PR for a reported bug or observability finding.launchdarkly/ai-tooling25
- Create GraphCreates observability dashboards and graphs from logs, traces, errors, sessions, metrics, and events data by previewing charts inline and saving them to a dashboard.launchdarkly/ai-tooling25
- 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-tooling25
- 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-tooling25
- Flag And Release ChangeDrive a pull request's change end to end: decide it's flag-worthy, create the guarding flag, wire the new code path behind it on the PR branch, and record an automated release so the change ships safely when the PR merges. A portable orchestrator that composes should-flag-change, launchdarkly-flag-create, and flag-release. Keywords: flag a PR, wrap change in a flag, dark launch, kill switch, auto-release, automated rollout, end-to-end flag workflow.launchdarkly/ai-tooling25
- Flag ReleaseRecord an automated rollout for an existing LaunchDarkly flag that guards a pull request's change, so the change releases safely when the PR merges. Honors a stated release intent (release now / hold / notBefore / segment / prerequisite) and defers per-environment to the project's release policies. Use as the release step once the guarding flag exists and its code is wired. Keywords: record release, automated rollout, release policy, guarded rollout, staged rollout, simple vs policy, release intlaunchdarkly/ai-tooling25
- InvestigateAnalyzes observability data — logs, traces, errors, sessions, and metrics — to find root cause and actionable evidence. Use when the user reports a bug, an unexpected behavior, or asks about patterns across application data.launchdarkly/ai-tooling25
- 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-tooling25
- Launchdarkly Factory SettingsConfigure LaunchDarkly Factory settings (GitHub App auto-flagging and auto-releasing) via the hosted MCP, or diagnose why a pull request was not classified / auto-flagged. Use when the user wants to turn on auto-flagging, map a GitHub repo to a LaunchDarkly project, change Factory account defaults, unmap a repo, or asks why Factory did not classify their PR.launchdarkly/ai-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- Launchdarkly Flag DriftDetect and reconcile drift between a feature flag's in-code SDK fallback default and its LaunchDarkly default rule (fallthrough). Use when a flag's default rule changed, when the user asks to detect flag drift, check whether a hardcoded default still matches LaunchDarkly, sync an in-code default, or open a PR reconciling a fallback value, without removing the flag or its evaluation.launchdarkly/ai-tooling25
- Launchdarkly Flag Qualitative Feedback SetupIntegrate LaunchDarkly qualitative user feedback into a JavaScript/TypeScript codebase. Guides framework and design system detection, builds the sendFeedback utility and feedback widget matching existing project patterns. Use when the user wants to add a Give Feedback widget, collect user sentiment tied to feature flags, set up feedback collection, or wire up the $ld:feedback tracking event.launchdarkly/ai-tooling25
- 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-tooling25
- Launchdarkly Guarded RolloutConfigure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.launchdarkly/ai-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- Mcp ConfigureConfigure the LaunchDarkly hosted MCP server during onboarding. Use when the parent LaunchDarkly onboarding skill reaches the MCP offer, after the first flag works. Supports Cursor, Claude Code, Windsurf, GitHub Copilot, and other MCP-compatible agents. OAuth authentication; no API keys for the hosted server.launchdarkly/ai-tooling25
- 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-tooling25
- OnboardingScripted onboarding for LaunchDarkly: quiet execution, fixed sequence, SDK install, first flag with a live reveal, MCP offered afterwards. Enforces step completion before advancing and redirects drift. Use when adding LaunchDarkly, setting up or integrating feature flags in a project, SDK integration, or 'onboard me'.launchdarkly/ai-tooling25
- 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-tooling25
- 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-tooling25
- 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-tooling25
- Sdk InstallInstall and initialize the correct LaunchDarkly SDK during onboarding by running nested skills in order: detect, plan, apply. Parent onboarding Step 4 is first flag.launchdarkly/ai-tooling25
- Should Flag ChangeDecide whether a given code change should be placed behind a LaunchDarkly feature flag. Use when a developer asks whether a change should be behind a flag, when reviewing a diff or pull request, or when running in CI on a PR. Reads the diff and surrounding code, then emits a structured advisory recommendation. Read-only: it never creates or modifies flags.launchdarkly/ai-tooling25
- 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-tooling25
- 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-tooling25
- 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