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LaunchDarkly Agent Skills

Publisher: launchdarkly/ai-tooling (20⭐) Skills.sh: npx skills add launchdarkly/agent-skills Installs: ~26,000+ combined across 25+ skills Quality: 🟢 Production — official LaunchDarkly repository

LaunchDarkly's public collection of agent skills and playbooks. Modular, text-based playbooks that teach an agent how to execute feature flag workflows safely and consistently — from flag creation through cleanup, plus experiment setup, AgentControl for LLM prompt management, and full onboarding sequences.

What It Does

Feature Flags (9 skills)

Skill Installs Purpose
launchdarkly-flag-discovery 2,966 Audit flags, find stale/launched flags, assess removal readiness
launchdarkly-flag-cleanup 2,964 Safely remove flags from code using LaunchDarkly as source of truth
launchdarkly-flag-create 2,953 Create new feature flags fitting existing codebase patterns
launchdarkly-flag-targeting 2,950 Control targeting, rollouts, rules, cross-environment config
launchdarkly-flag-command 1,439 Resolve /flag style requests into fast lookup and disambiguation
launchdarkly-guarded-rollout 2,397 Configure progressive traffic rollouts with metric monitoring and rollback
should-flag-change — Advisory: whether a code change (diff/PR) should ship behind a flag
flag-release — Record a flag's automated release for a PR
flag-and-release-change — End-to-end PR orchestrator: decide → create + wire → record release

AgentControl (10 skills)

Skill Purpose
configs-create Create configs with variations for agent or completion mode
configs-update Update and delete configs, manage lifecycle
configs-variations Manage config variations for A/B testing
configs-targeting Configure targeting rules for config rollouts
tools Create and attach tools for function calling
projects Create and manage projects to organize configs
online-evals Attach LLM-as-a-judge evaluators to configs
snippets Create and manage reusable prompt snippets across configs
agent-graphs Create and manage multi-agent graphs with routing and handoffs
migrate Migrate hardcoded LLM prompts to AgentControl in 5 stages

Experiments & Metrics (5 skills)

Skill Installs Purpose
launchdarkly-experiment-setup 2,402 Set up experiments with metrics, treatments, data collection
launchdarkly-metric-choose 2,696 Select the right metric type for an experiment
launchdarkly-metric-create 2,688 Create metrics and instrument tracking events
launchdarkly-metric-instrument 2,688 Add tracking calls to code for existing metrics

Onboarding (4 skills)

Skill Purpose
onboarding End-to-end LaunchDarkly setup: kickoff roadmap, MCP, SDK, first flag
mcp-configure Configure LaunchDarkly hosted MCP server (OAuth, no API keys)
sdk-install Install and initialize the correct SDK (detect → plan → apply)
first-flag Create a boolean flag, evaluate it, toggle for end-to-end proof

Why This Matters for Hermes Agents

LaunchDarkly skills transform feature flag management from manual dashboard clicking into agent-native workflows. A Hermes agent with these skills can autonomously audit stale flags, create new ones matching existing patterns, and configure guarded rollouts — all without leaving the codebase context. The AgentControl skills are uniquely positioned: they manage LLM prompts themselves as feature-flagged configurations, enabling A/B testing of agent prompts, multi-agent graph orchestration, and online eval attachment.

Installation

Prerequisites

  • A LaunchDarkly account with API access
  • An API access token with appropriate permissions
  • Claude Code, Codex CLI, Cursor, or Hermes agent with MCP support
# Add LaunchDarkly as a plugin marketplace
/plugin marketplace add launchdarkly/ai-tooling

# Install the plugin (includes all skills + MCP server)
/plugin install launchdarkly@launchdarkly-ai-tooling

# Authenticate the MCP server when prompted

Method 2: skills.sh CLI

# Install all LaunchDarkly skills
npx skills add launchdarkly/ai-tooling --full-depth -y

# Or install specific skill categories
npx skills add launchdarkly/ai-tooling \
  --skill feature-flags/launchdarkly-flag-discovery \
  --skill feature-flags/launchdarkly-flag-cleanup \
  --skill feature-flags/launchdarkly-flag-create \
  --full-depth -y

Method 3: Cursor Plugin

  1. Open Cursor → Settings > Plugins
  2. Search for LaunchDarkly in the marketplace
  3. Or install from URL: https://github.com/launchdarkly/ai-tooling

Method 4: Manual Copy

git clone https://github.com/launchdarkly/ai-tooling.git
cd ai-tooling

# Copy specific skills to your agent's skills directory
cp -r skills/feature-flags/launchdarkly-flag-cleanup ~/.hermes/skills/
cp -r skills/experiments/launchdarkly-experiment-setup ~/.hermes/skills/

Quick Reference

Workflow Trigger Phrase
Flag audit "Which feature flags are stale and should be cleaned up?"
Flag creation "Create a feature flag for the new checkout flow"
Flag cleanup "Remove the new-checkout-flow feature flag from this codebase"
Rollout "Roll out dark-mode to 25% of users in production"
Experiment setup "Set up an experiment comparing the old vs new recommendation algorithm"
Guarded rollout "Configure a guarded rollout for the payment service migration — 10% traffic, monitor error rate"
AgentControl migration "Migrate our hardcoded support agent prompt to AgentControl"
Multi-agent graph "Create an agent graph that routes product questions to the product agent and billing to the support agent"

Usage Examples

Feature Flag Lifecycle

"Audit all feature flags in this repo — which ones are stale?"
"Create a boolean flag called 'new-search-ui' following our existing patterns"
"Wire the flag into the search component and create a PR with the flag change"
"Roll out new-search-ui to 10% of users, monitor for errors"
"Clean up the 'old-search-backend' flag — it's 100% launched everywhere"

Experimentation

"Set up an A/B experiment: new recommendation algo vs current — measure conversion rate"
"Choose the right metric type for measuring signup completion rate"
"Instrument the signup flow with a conversion tracking event"

AgentControl (AI Agent Configuration)

"Migrate our customer support agent's hardcoded system prompt to AgentControl"
"Create a prompt variation for the support agent with a more empathetic tone"
"Attach an LLM-as-judge evaluator to measure response quality"
"Set up an agent graph: triage agent → routes to product/support/billing specialist agents"
"Create a reusable prompt snippet for our brand voice guidelines"

Verification

# Verify MCP server is configured
# In Claude Code: /status should show launchdarkly MCP connected

# Quick smoke test — ask the agent:
"List my LaunchDarkly feature flags"
"Show me any stale flags in production"

# AgentControl: verify config management
"Show my AgentControl projects"

Pro Tips

  1. Start with the onboarding skill. It provides an end-to-end setup sequence: MCP configuration → SDK installation → first flag creation. Complete this once and subsequent skills work without friction.

  2. flag-and-release-change is the highest-leverage single skill. It orchestrates the entire PR workflow — deciding whether a change needs a flag, creating it, wiring it into code, and recording the release. Use it as your default entry point for any code change that might need flagging.

  3. AgentControl is LaunchDarkly for LLM prompts. If you're A/B testing system prompts, managing multi-agent routing, or attaching evals to agent configurations, these skills replace manual prompt engineering workflows with version-controlled, feature-flagged configurations.

  4. The MCP server gives direct flag access. Unlike skills.sh-only installs that require API token management, the Claude Code plugin and Cursor plugin include the LaunchDarkly MCP server for OAuth-based, keyless authentication.

  5. launchdarkly-flag-cleanup uses LaunchDarkly as source of truth. It checks actual flag states (not just code references) to determine what's safe to remove — preventing the common mistake of removing a flag that's still active in one environment.

Source: skills.sh — launchdarkly/agent-skills · GitHub · ~26,000 combined installs across 25+ skills