Feature Flags Architect
SkillAI & modelsYour AI can plan, audit, and clean up feature flags, the switches teams use to turn features on or off. It works through rollout schedules and kill-switch checks, so launches stay controlled and leftover flags don't pile up. The result is a tidier flag setup and releases that are easier to manage.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add the skill, then ask your agent to plan the flags for your next release or audit the ones you already have. When you're ready, have it clean up flags you no longer use.
Then ask your AI: use the Feature Flags Architect skill
What your AI can do with it
- Plan feature flags for an upcoming release
- Audit the feature flags you already have
- Clean up flags that are no longer needed
- Set up rollout schedules for new features
- Run kill-switch checks on your flags
What this skill tells your AI
The instructions your AI receives, as published by alirezarezvani/claude-skills in .gemini/skills/skills-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.
| Type | Purpose | Typical lifespan | Owner | Cleanup trigger |
|---|---|---|---|---|
| Release | Hide unfinished features in production | days–weeks | Eng | 100% rollout reached |
| Experiment | A/B test variants | weeks | Product/Marketing | Test concluded; winner picked |
| Operational | Circuit breakers, perf toggles, kill switches | months–years | Eng/SRE | Replaced by autoscaling/feature retirement |
| Permission | Entitlements per user/account/plan | years (permanent) | Product | Plan/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:
- Walk
--repofor code references matching common flag-call patterns:flag("..."),isFlagEnabled("..."),featureFlag("..."),getFlag("...")client.variation("...", ...),unleash.isEnabled("..."),growthbook.feature("...")
- For each unique flag identifier, find the oldest commit that introduced it (
git log --diff-filter=A -S <name>). - Flag as DEBT if introduced >
--max-age-daysago AND used in ≤--min-usesplaces.
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:
- Every code-discovered flag has an entry in
--flag-doc - Each entry declares: owner, type, kill-switch trigger, monitoring dashboard
- Reports flags missing documentation (FAIL) or missing fields (WARN)
Use as a pre-merge gate before any new flag ships.
Provider chooser (5 + DIY)
| Provider | Best for | Pricing model | Lock-in risk | OSS option |
|---|---|---|---|---|
| LaunchDarkly | Enterprise, complex targeting, audit/compliance | Per-MAU, expensive | High | No |
| GrowthBook | Mid-market, A/B testing focused, OSS-friendly | Per-MAU + OSS | Low | Yes (self-host) |
| Statsig | Growth/product teams, advanced experimentation | Free tier + per-MAU | Medium | No |
| Unleash | OSS-first, self-hosted, dev-friendly | OSS + Enterprise | Low | Yes |
| Flipt | Lightweight, k8s-native, simple needs | OSS-only | None | Yes |
| DIY | <100 flags, no targeting, full control | None | None | N/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.mdfor 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, lifespanreferences/provider_comparison.md— LaunchDarkly / GrowthBook / Statsig / Unleash / Flipt / DIY trade-offsreferences/rollout_strategies.md— ring / linear / log / cohort / geo, abort criteria, monitoringreferences/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.pyat merge time flag_debt_scanner.py --max-age-days 90returns ≤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
- 27k
- Forks
- 4k
- Last commit
- Aug 2026
Advanced
- Item type
- skill
- Key
skills-feature-flags-architect- Source
- github.com/alirezarezvani/claude-skills
github.com/alirezarezvani/claude-skills
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