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Falirezarezvani on GitHub

Feature Flags Architect

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Streamline your feature flag lifecycle management.

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What Feature Flags Architect does

Feature Flags Architect provides a comprehensive approach to managing feature flags throughout their lifecycle. Unlike typical implementations that treat flags as simple conditional statements, this skill emphasizes a structured lifecycle that includes classification, rollout, and eventual retirement of flags. This disciplined approach helps teams avoid the pitfalls of flag debt, which can lead to untested code paths and unnecessary complexity in the codebase.

The skill is particularly useful for developers and product teams who frequently use feature flags for progressive delivery. It includes three essential Python scripts: the flag debt scanner, rollout planner, and kill-switch auditor. These tools facilitate the auditing of existing flags, the planning of new flag rollouts, and the verification of kill-switch documentation, ensuring that every flag has a defined exit strategy. By following the provided workflows, teams can effectively manage flags from inception to cleanup.

Additionally, the skill offers references on flag taxonomy, provider comparisons, and rollout strategies, making it a valuable resource for teams looking to optimize their feature flagging practices. Whether you're deciding on a flag provider or assessing the lifecycle of existing flags, Feature Flags Architect equips you with the necessary insights and tools to make informed decisions. This skill is designed for teams that prioritize controlled feature releases and aim to minimize technical debt associated with feature flags.

When to use it

Use this skill when implementing new feature flags, auditing existing ones, or planning rollouts to ensure a structured approach to feature management.

When not to use it

This skill may not be suitable for projects that do not utilize feature flags or for teams looking for a quick, ad-hoc solution without a structured approach.

What you can build with it

Auditing Existing Flags

Run the flag debt scanner to identify stale flags in your codebase and plan for their cleanup.

Planning a Rollout

Use the rollout planner to create a phased rollout strategy for a new feature flag, ensuring controlled exposure.

Verifying Kill Switch Documentation

Run the kill-switch auditor to ensure that all feature flags have documented exit strategies before deployment.

How to install Feature Flags Architect

View source

1. Install with the skills CLI

npx skills add alirezarezvani/claude-skills/feature-flags-architect --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by alirezarezvani

Feature Flags Architect

End-to-end discipline for feature flags: classify them, ship them, ramp them, and retire them. Most teams treat flags as throwaway if-statements; this skill treats them as a controlled lifecycle with measurable debt.

When to use

  • Adding a new flag and need a rollout plan
  • Auditing a codebase for stale or orphaned flags
  • Choosing a flag provider (LaunchDarkly vs GrowthBook vs Statsig vs Unleash vs Flipt vs build-your-own)
  • Designing a kill-switch path for a risky launch
  • Cleaning up flag debt before a release freeze
  • Reviewing whether a feature should ship behind a flag at all

Core principle: flags are a lifecycle, not an if

request → design → ship → ramp → cleanup → archive

Flags that skip cleanup become debt: dead branches, stale defaults, untested code paths, unbounded blast radius. The three scripts in this skill enforce the lifecycle.

Quick start

# 1. Audit the repo for flag debt
python scripts/flag_debt_scanner.py --repo . --max-age-days 90

# 2. Plan a progressive rollout for a new flag
python scripts/rollout_planner.py --population 100000 --target-percent 100 --duration-days 14 --strategy ring

# 3. Verify every flag has a documented kill switch
python scripts/kill_switch_audit.py --repo . --flag-doc docs/feature-flags.md

The 4 flag types (taxonomy)

Different flag types have different lifespans and ownership. Misclassifying creates debt.

TypePurposeTypical lifespanOwnerCleanup trigger
ReleaseHide unfinished features in productiondays–weeksEng100% rollout reached
ExperimentA/B test variantsweeksProduct/MarketingTest concluded; winner picked
OperationalCircuit breakers, perf toggles, kill switchesmonths–yearsEng/SREReplaced by autoscaling/feature retirement
PermissionEntitlements per user/account/planyears (permanent)ProductPlan/role removed

Only Release and Experiment flags should be on a debt-scanner watchlist. Operational and Permission flags are by design long-lived. See references/flag_taxonomy.md for decision tree.

The 3 Python tools

All three are stdlib-only. Run with --help.

flag_debt_scanner.py

Finds flags older than --max-age-days with low usage, suggesting candidates for cleanup.

python scripts/flag_debt_scanner.py --repo . --max-age-days 90 --format text
python scripts/flag_debt_scanner.py --repo . --max-age-days 60 --format json > debt.json

Detection heuristic:

  1. Walk --repo for code references matching common flag-call patterns:
    • flag("..."), isFlagEnabled("..."), featureFlag("..."), getFlag("...")
    • client.variation("...", ...), unleash.isEnabled("..."), growthbook.feature("...")
  2. For each unique flag identifier, find the oldest commit that introduced it (git log --diff-filter=A -S <name>).
  3. Flag as DEBT if introduced > --max-age-days ago AND used in ≤--min-uses places.

Outputs flag name, age in days, file references, suggested action. JSON mode is CI-friendly.

rollout_planner.py

Generates a phased rollout schedule from population size, target percent, duration, and strategy.

python scripts/rollout_planner.py --population 100000 --target-percent 100 --duration-days 14 --strategy ring
python scripts/rollout_planner.py --population 50000 --target-percent 25 --duration-days 7 --strategy linear
python scripts/rollout_planner.py --population 1000000 --target-percent 100 --duration-days 30 --strategy log

Strategies:

  • ring: 1% → 5% → 25% → 50% → 100%, evenly spaced. Default for risky launches.
  • linear: constant rate per day. Default for medium-risk.
  • log: rapid early, slow tail. Default for low-risk launches with confidence.
  • cohort: by named cohort (internal → beta → free → paid → all).

Outputs a markdown table with date, percent, expected user count, abort criteria, and verification step per phase.

kill_switch_audit.py

Cross-references code-discovered flags against documentation to verify each has a kill switch path written down.

python scripts/kill_switch_audit.py --repo . --flag-doc docs/feature-flags.md
python scripts/kill_switch_audit.py --repo . --flag-doc runbooks/flags.md --format json

What it checks:

  1. Every code-discovered flag has an entry in --flag-doc
  2. Each entry declares: owner, type, kill-switch trigger, monitoring dashboard
  3. Reports flags missing documentation (FAIL) or missing fields (WARN)

Use as a pre-merge gate before any new flag ships.

Provider chooser (5 + DIY)

ProviderBest forPricing modelLock-in riskOSS option
LaunchDarklyEnterprise, complex targeting, audit/compliancePer-MAU, expensiveHighNo
GrowthBookMid-market, A/B testing focused, OSS-friendlyPer-MAU + OSSLowYes (self-host)
StatsigGrowth/product teams, advanced experimentationFree tier + per-MAUMediumNo
UnleashOSS-first, self-hosted, dev-friendlyOSS + EnterpriseLowYes
FliptLightweight, k8s-native, simple needsOSS-onlyNoneYes
DIY<100 flags, no targeting, full controlNoneNoneN/A

Decision rules:

  • <50 flags + no targeting → DIY with config file or env vars
  • Need analytics + experimentation → Statsig or GrowthBook
  • Compliance/SOC2 audit logs required → LaunchDarkly
  • Self-hosting required (data residency / air-gapped) → Unleash or Flipt
  • See references/provider_comparison.md for detail.

Workflows

Workflow 1: Ship a new feature behind a flag

1. Classify: which of the 4 flag types?
   → Release (most common for engineering work)
2. Run rollout_planner.py to design the ramp
3. Add flag entry to docs/feature-flags.md BEFORE writing code:
   - name, owner, type, kill-switch trigger, dashboard URL
4. Write the code with the flag
5. Run kill_switch_audit.py — must pass before merge
6. Deploy at 0%; verify kill switch works
7. Execute rollout schedule; abort if abort criteria met
8. At 100% for 7+ days: remove flag, delete dead branch, archive doc entry

Workflow 2: Quarterly flag cleanup

1. Run flag_debt_scanner.py --repo . --max-age-days 90 > debt.md
2. For each flagged item:
   a. Confirm it reached 100% (or was killed)
   b. Find the issue/PR that introduced it; verify owner agrees to remove
   c. Delete dead branches; remove flag config
   d. Run kill_switch_audit.py — should now show one fewer flag
3. Update CHANGELOG: "Removed N stale flags"

Workflow 3: Choose a provider

1. Estimate flag count (current + 12-month projection)
2. Required features:
   - Targeting rules (user, account, geo, %)?
   - A/B testing + stats?
   - Audit log / SOC2?
   - Self-hosting / data residency?
3. Pricing budget (MAU * cost-per-MAU)
4. See provider_comparison.md decision tree
5. Build a 30-day proof-of-concept before signing

Workflow 4: Design a kill switch

1. Identify the failure modes:
   - Latency spike (which threshold?)
   - Error rate spike (which threshold?)
   - Business metric regression (which threshold?)
2. Wire each to an abort:
   - Manual: dashboard link + on-call playbook
   - Automated: alert threshold flips flag back to 0%
3. Test the kill switch in staging BEFORE production rollout
4. Document in flag-doc; pass kill_switch_audit.py

References

  • references/flag_taxonomy.md — 4 types, decision tree, ownership, lifespan
  • references/provider_comparison.md — LaunchDarkly / GrowthBook / Statsig / Unleash / Flipt / DIY trade-offs
  • references/rollout_strategies.md — ring / linear / log / cohort / geo, abort criteria, monitoring
  • references/flag_lifecycle.md — request → design → ship → ramp → cleanup → archive

Slash command

/flag-cleanup — Run the full cleanup workflow on the current repo: scan for debt, generate a removal plan, audit kill switches.

Asset templates

  • assets/flag_request_template.md — fill-in form for new flag requests (name, owner, type, kill switch, rollout plan)

Anti-patterns

  • Permanent flag with if (FLAG_FOO) 50 places — should be a Permission flag with a runtime config, not a Release flag
  • Flag with no owner — when the original engineer leaves, no one cleans it up
  • No kill switch documented — when the feature breaks, no one knows how to disable it
  • A/B test that ran 6 months — pick a winner; running indefinitely is debt
  • Flags as feature toggles for cosmetic changes — ship via deploy, not flag

Verifiable success

A team using this skill should achieve:

  • 100% of new flags pass kill_switch_audit.py at merge time
  • flag_debt_scanner.py --max-age-days 90 returns ≤5 stale flags repo-wide
  • Every flag has a documented owner, type, and kill switch
  • Mean time to retire a Release flag: <60 days from 100% rollout

Frequently asked questions about Feature Flags Architect

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