LaunchDarkly Agent 技能
管理功能开关、AgentControl、指标和发布流程。
LaunchDarkly 的公开仓库目录覆盖开关创建、定向、清理、受监控灰度、Agent 配置、评估、可观测性、指标、实验与接入。
适用场景
- 配置功能开关并推进灰度
- 管理 AgentControl 配置与评估
- 接入指标并调查发布
使用方法
按上游说明连接 LaunchDarkly 工具并完成认证。修改前说明项目和环境；仓库完整目录比当前插件清单更广。
Skills
agent-graphs
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.
launchdarkly/ai-toolingaiconfig-agent-graphs
DEPRECATED 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-toolingaiconfig-ai-metrics
DEPRECATED 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-toolingaiconfig-create
DEPRECATED 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-toolingaiconfig-custom-metrics
DEPRECATED 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-toolingaiconfig-migrate
DEPRECATED 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-toolingaiconfig-online-evals
DEPRECATED 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-toolingaiconfig-projects
DEPRECATED 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-toolingaiconfig-snippets
DEPRECATED 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-toolingaiconfig-targeting
DEPRECATED 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-toolingaiconfig-tools
DEPRECATED 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-toolingaiconfig-update
DEPRECATED 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-toolingaiconfig-variations
DEPRECATED 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-toolingbuilt-in-metrics
Instrument 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-toolingconfigs-create
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.
launchdarkly/ai-toolingconfigs-targeting
Configure config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.
launchdarkly/ai-toolingconfigs-update
Update, 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-toolingconfigs-variations
Experiment 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-toolingcustom-metrics
Create, 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-toolingmigrate
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.
launchdarkly/ai-toolingonline-evals
Attach judges to config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
launchdarkly/ai-toolingprojects
Guide 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-toolingsnippets
Create and manage prompt snippets — reusable text blocks referenced inside config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.
launchdarkly/ai-toolingtools
Give 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-toolinglaunchdarkly-experiment-setup
Set 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-toolinglaunchdarkly-factory-settings
Configure 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-toolingflag-and-release-change
Drive 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-toolingflag-release
Record 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 intent, hold release, dark launch.
launchdarkly/ai-toolinglaunchdarkly-flag-cleanup
Safely 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-toolinglaunchdarkly-flag-command
Resolve `/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-toolinglaunchdarkly-flag-create
Create 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-toolinglaunchdarkly-flag-discovery
Audit 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-toolinglaunchdarkly-flag-drift
Detect 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-toolinglaunchdarkly-flag-qualitative-feedback-setup
Integrate 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-toolinglaunchdarkly-flag-targeting
Control 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-toolinglaunchdarkly-guarded-rollout
Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.
launchdarkly/ai-toolingshould-flag-change
Decide 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-toolinglaunchdarkly-metric-choose
Choose 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-toolinglaunchdarkly-metric-create
Create 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-toolinglaunchdarkly-metric-instrument
Instrument 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-toolingalert-investigation
Investigates a triggered observability alert and returns a structured diagnosis with likely cause, scope, and next steps.
launchdarkly/ai-toolingcreate-fix-pr
Investigates a root cause and files a minimal fix PR for a reported bug or observability finding.
launchdarkly/ai-toolingcreate-graph
Creates 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-toolinginvestigate
Analyzes 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-toolingonboarding
Scripted 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-toolingmcp-configure
Configure 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-toolingsdk-install
Install 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-tooling