launchdarkly-flag-discovery

launchdarkly/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/launchdarkly/agent-skills --skill launchdarkly-flag-discovery
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summary

You're using a skill that will guide you through auditing and understanding the feature flag landscape in a LaunchDarkly project. Your job is to explore the project, assess the health of its flags, identify what needs attention, and provide actionable recommendations.

skill.md

LaunchDarkly Flag Discovery

You're using a skill that will guide you through auditing and understanding the feature flag landscape in a LaunchDarkly project. Your job is to explore the project, assess the health of its flags, identify what needs attention, and provide actionable recommendations.

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

Required MCP tools:

  • list-flags — search and browse flags with filtering by state, type, tags
  • get-flag — get full configuration for a single flag in a specific environment
  • get-flag-status-across-envs — check a flag's lifecycle status across all environments

Optional MCP tools (enhance depth):

  • find-stale-flags — find flags that are candidates for cleanup, sorted by staleness
  • get-flag-health — get combined health view for a single flag (merges status + config)
  • check-removal-readiness — detailed safety check for a specific flag

Workflow

Step 1: Understand the Project

Before diving into flag data, establish context:

  1. Identify the project. Confirm the projectKey with the user. If they haven't specified one, ask.
  2. Understand scope. Ask the user what they're trying to accomplish:
    • Broad audit? ("What's the state of our flags?")
    • Targeted investigation? ("Is this specific flag still needed?")
    • Cleanup planning? ("What flags can we remove?")

Step 2: Explore the Flag Landscape

Adapt your approach to the user's goal:

For a broad audit:

  • Use list-flags scoped to a critical environment (default to production).
  • Note the total count — this tells you the scale of the flag surface area.
  • Filter by state (active, inactive, launched, new) to segment the landscape.
  • Filter by type (temporary vs permanent) — temporary flags are the primary cleanup targets.

For cleanup planning:

  • Use find-stale-flags — this is the most efficient entry point. It returns a prioritized list of cleanup candidates sorted by staleness, categorized as:
    • never_requested — created but never evaluated (possibly abandoned)
    • inactive_30d — no SDK evaluations in the specified period
    • launched_no_changes — fully rolled out, no recent changes
  • Default inactiveDays is 30. Increase for conservative cleanup (60, 90) or decrease for aggressive cleanup (7, 14).
  • Default includeOnly is temporary. Set to all to include permanent flags.

For a targeted investigation:

  • Use get-flag-health for a single-flag deep dive. It merges status data with configuration context in one call, returning lifecycle state, last-requested timestamp, targeting summary, age, and whether it's temporary.
  • Or use get-flag for the full configuration including rules, targets, and fallthrough details.

Step 3: Assess Flag Health

For flags that need deeper investigation, assess health signals. See Flag Health Signals for the full interpretation guide.

Key signals to evaluate:

Signal What it tells you
Lifecycle state Where the flag is in its journey (new → active → launched → inactive)
Last requested When an SDK last evaluated this flag — staleness indicator
Targeting complexity Number of rules and targets — removal complexity indicator
Cross-environment consistency Whether the flag behaves the same everywhere
Flag age + temporary status Old temporary flags are strong cleanup candidates

Use get-flag-status-across-envs to check if a flag is consistent across environments. A flag inactive in production but active in staging tells a different story than one inactive everywhere.

Step 4: Categorize and Prioritize

Group flags into actionable categories:

  1. Ready to remove — Inactive everywhere, temporary, no dependencies. Direct the user to the flag cleanup skill for code removal.
  2. Likely safe, needs verification — Launched (fully rolled out), no rule changes recently. The user should confirm the rollout is intentionally complete.
  3. Needs investigation — Active in some environments but not others, or has complex targeting. Don't recommend action without more context.
  4. Leave alone — Active flags doing their job, or permanent flags that are intentionally long-lived.

Step 5: Assess Removal Readiness (When Applicable)

If the user wants to know whether a specific flag can be removed, use check-removal-readiness. This tool orchestrates multiple API calls in parallel and returns a structured verdict:

  • safe — No blockers or warnings. Proceed with cleanup.
  • caution — Warnings exist (code references, expiring targets, permanent flag type). Present and let the user decide.
  • blocked — Hard blockers (dependent flags, active requests, targeting rules). Must resolve first.

See Removal Readiness Checklist for the full details on interpreting each signal.

Step 6: Present Findings

Structure your response based on what the user asked for:

For audits: Lead with a summary (total flags, breakdown by state and type), then highlight what needs attention, then provide specific recommendations.

For specific flags: Lead with the verdict (healthy / needs attention / ready to remove), then support it with the signals you found.

For cleanup planning: Lead with the count of cleanup candidates, prioritize by confidence (safest removals first), and link to the cleanup workflow for execution.

Important Context

  • "Launched" means fully rolled out — targeting is on, a single variation is served to everyone, and no changes have been made recently. It doesn't mean "recently deployed."
  • "Inactive" doesn't always mean safe to remove. The flag might be used in code that hasn't shipped yet, or referenced as a prerequisite by another flag.
  • Permanent flags can be inactive on purpose. Some flags are designed to be dormant until needed (kill switches, emergency toggles). Don't automatically flag these for cleanup.
  • Weights are scaled by 1000 in the API. A weight of 60000 means 60%. Always convert to human-readable percentages.
  • This skill is for discovery, not action. If the user wants to remove a flag from code, direct them to the flag cleanup skill. If they want to change targeting, direct them to the flag targeting skill.

References

how to use launchdarkly-flag-discovery

How to use launchdarkly-flag-discovery on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add launchdarkly-flag-discovery
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/launchdarkly/agent-skills --skill launchdarkly-flag-discovery

The skills CLI fetches launchdarkly-flag-discovery from GitHub repository launchdarkly/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/launchdarkly-flag-discovery

Reload or restart Cursor to activate launchdarkly-flag-discovery. Access the skill through slash commands (e.g., /launchdarkly-flag-discovery) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.560 reviews
  • Kiara Robinson· Dec 28, 2024

    launchdarkly-flag-discovery reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kiara Patel· Dec 28, 2024

    I recommend launchdarkly-flag-discovery for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Xiao Gupta· Dec 20, 2024

    Registry listing for launchdarkly-flag-discovery matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ren Zhang· Dec 20, 2024

    We added launchdarkly-flag-discovery from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Pratham Ware· Dec 12, 2024

    We added launchdarkly-flag-discovery from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Dhruvi Jain· Dec 8, 2024

    I recommend launchdarkly-flag-discovery for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Oshnikdeep· Nov 27, 2024

    Useful defaults in launchdarkly-flag-discovery — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Xiao Kapoor· Nov 19, 2024

    launchdarkly-flag-discovery has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kiara Wang· Nov 19, 2024

    Useful defaults in launchdarkly-flag-discovery — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Min Khanna· Nov 11, 2024

    launchdarkly-flag-discovery fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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