Feature Flags Architect

SkillAI & models

This skill gives your AI a way to manage feature flags from creation to cleanup. Once added, it can plan safe rollouts for new features, scan your code for stale flags, and check that kill switches are ready if something goes wrong.

Available today. Use it from your connected AI after setup.

Add the skill, then ask your AI about a flag task, such as 'plan a rollout for the new checkout flow' or 'find stale flags in our code'.

Then ask your AI: use the Feature Flags Architect skill

What your AI can do with it

  • Plan a gradual rollout for shipping a feature behind a flag
  • Scan your code for stale flags and flag debt
  • Verify that kill switches are in place before you need them
  • Decide when an old flag is ready to be retired
  • Act on plain requests like 'add a flag', 'rollout plan', or 'stale flags'

What this skill tells your AI

The instructions your AI receives, as published by borghei/claude-skills in engineering/feature-flags-architect/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
740
Forks
135
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
feature-flags-architect
Source
github.com/borghei/claude-skills