launchdarkly-flag-discovery

작성자: launchdarkly

LaunchDarkly 기능 플래그를 감사하여 전체 현황을 파악하고, 오래되었거나 출시된 플래그를 찾으며, 제거 준비 상태를 평가합니다. 사용자가 다음에 대해 질문할 때 사용하세요.

npx skills add https://github.com/launchdarkly/ai-tooling --skill launchdarkly-flag-discovery

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:

SignalWhat it tells you
Lifecycle stateWhere the flag is in its journey (new -> active -> launched -> inactive)
Last requestedWhen an SDK last evaluated this flag: staleness indicator
Targeting complexityNumber of rules and targets: removal complexity indicator
Cross-environment consistencyWhether the flag behaves the same everywhere
Flag age + temporary statusOld 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

launchdarkly의 다른 스킬

aiconfig-agent-graphs
launchdarkly
에이전트 그래프를 생성하고 관리합니다 — 핸드오프 로직이 있는 엣지로 연결된 AI 구성의 방향 그래프입니다. 구성이 포함된 다중 에이전트 워크플로우를 구축할 때 사용합니다.
official
aiconfig-ai-metrics
launchdarkly
기존 코드베이스에 LaunchDarkly AI Config 추적을 계측합니다. 4단계 사다리(관리형 러너 → 제공자 패키지 → 사용자 정의 추출기 +…)를 따라 진행됩니다.
official
aiconfig-create
launchdarkly
LaunchDarkly에서 AI Config를 생성하고 구성합니다. 에이전트 모드와 완료 모드 중 선택하고, config를 생성하며, 모델과 프롬프트가 포함된 변형을 추가하는 데 도움을 줍니다.
official
aiconfig-custom-metrics
launchdarkly
AI Configs에 대한 맞춤형 비즈니스 메트릭을 생성, 추적, 검색, 업데이트 및 삭제합니다. 전체 수명 주기를 다룹니다: API를 통해 메트릭 종류를 정의하고, SDK를 통해 이벤트를 전송하며,…
official
aiconfig-migrate
launchdarkly
하드코딩된 LLM 프롬프트가 포함된 애플리케이션을 완전한 LaunchDarkly AI Configs 구현체로 마이그레이션하는 다섯 단계: 코드 감사, 호출 래핑, 이동…
official
aiconfig-online-evals
launchdarkly
AI Config 변형에 판정기를 연결하여 자동 LLM-as-a-judge 평가를 수행합니다. 맞춤형 판정기를 생성하고, 샘플링 비율을 구성하며, 품질 점수를 모니터링할 수 있습니다.
official
aiconfig-projects
launchdarkly
코드베이스에서 LaunchDarkly 프로젝트를 설정하기 위한 가이드입니다. 스택을 평가하고, 적절한 접근 방식을 선택하며, 프로젝트 관리를 통합하는 데 도움을 줍니다…
official
aiconfig-snippets
launchdarkly
프롬프트 스니펫을 생성하고 관리합니다 — AI Config 변형 프롬프트 내에서 참조되는 재사용 가능한 텍스트 블록입니다. 일반적인 지침, 페르소나, 가드레일을 유지합니다…
official