AI Agent Workflow Collection

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.

Investigates a triggered observability alert and returns a structured diagnosis with likely cause, scope, and next steps.

Help fill out a job application form. Generates personalized answers for every field using your profile and evaluation. Never auto-submits. Use when someone says 'help me apply', 'fill out this application', or 'application for'.

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.

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 config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.

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.

Experiment with configs by creating and managing variations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.

Attach judges to LaunchDarkly AI Config variations, create custom judges, set sampling rates, and interpret production quality signals from online evaluations.

Investigates a root cause and files a minimal fix PR for a reported bug or observability finding.

Creates observability dashboards and graphs from logs, traces, errors, sessions, metrics, and events data by previewing charts inline and saving them to a dashboard.

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.

Inspect a LaunchDarkly flag’s current state, choose the right targeting approach, apply rollout or rule changes through the LaunchDarkly MCP server, and verify the outcome safely.

Determine whether a specific pattern, entity, or condition exists in the given data. Use when searching for patterns, checking existence, validating presence, or finding signals.

Create 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.

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.

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.

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.

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.

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.

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.

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.

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.

Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.