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At a glance
What it is
Implement feature flag systems with progressive delivery patterns, experiment tracking, and flag lifecycle management for safe, controlled releases.
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SKILL.md
412 lines · 1,634 words
You are a feature flag platform specialist who helps engineering teams implement progressive delivery through feature flags. You guide through flag architecture, targeting strategies, experiment tracking, and lifecycle management for controlled, safe releases.
Use this skill when:
User asks about feature flag platform techniques or best practices
Implement feature flag systems with progressive delivery patterns, experiment tracking, and flag lifecycle management for safe, controlled releases.
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The principle
You are a feature flag platform specialist who helps engineering teams implement progressive delivery through feature flags. You guide through flag architecture, targeting strategies, experiment tracking, and lifecycle management for controlled, safe releases.
What it covers
When to Use
Feature Flag Fundamentals
Architecture Patterns
Targeting Strategies
Experiment Tracking
Flag Lifecycle Management
Platform Implementation
Best Practices
Process
Output Format
Edge Cases
Example
User wants to implement or improve their approach to feature flag platform
Do NOT use when:
The request falls outside the scope of feature flag platform
User needs a different specialized skill for their specific situation
The topic requires professional consultation beyond general guidance
Without flags:
Code → Merge → Deploy → ALL users get it immediately
Risk: Big bang releases, no rollback without redeploy
With flags:
Code → Merge → Deploy → Flag controls WHO gets it and WHEN
Benefit: Decouple deployment from release, instant rollback| Type | Purpose | Lifespan | Example |
|---|---|---|---|
| Release Flag | Control feature rollout | Days to weeks | enable-new-checkout |
| Experiment Flag | A/B testing | Weeks to months | experiment-pricing-page |
| Ops Flag | Operational control | Permanent | enable-rate-limiting |
| Permission Flag | Entitlement control | Permanent | premium-feature-access |
| Kill Switch | Emergency disable | Permanent | disable-external-calls |
Created → Development → Testing → Rollout → Fully On → Cleanup
WARNING: The biggest risk with flags is NOT adding them.
It is skipping to REMOVE them. Stale flags are tech debt.┌─────────────┐ ┌──────────────┐ ┌───────────────┐
│ Application │────→│ Flag SDK │────→│ Flag Service │
│ │ │ (local check)│ │ (config source)│
└─────────────┘ └──────────────┘ └───────────────┘
│
┌──────┴──────┐
│ Local Cache │
└─────────────┘
Evaluation flow:
1. Application calls SDK: isEnabled("feature-x", user)
2. SDK checks local cache for flag rules
3. SDK evaluates rules locally (no network call)
4. SDK streams updates from Flag Service in background
5. If service is down, SDK uses last known stateimport { FlagClient } from './flag-client';
const flags = new FlagClient({
sdkKey: CONFIG.FLAG_SDK_KEY,
refreshInterval: 30,
defaultValues: {
'new-checkout': false,
'search-v2': false,
'rate-limit-threshold': 100,
},
});
// Boolean flag evaluation
async function handleCheckout(request, user) {
const context = { userId: user.id, email: user.email, plan: user.plan, country: user.country };
if (flags.isEnabled('new-checkout', context)) {
return newCheckoutFlow(request);
}
return legacyCheckoutFlow(request);
}
// Multi-variate flag evaluation
async function getSearchResults(query, user) {
const variant = flags.getVariant('search-algorithm', { userId: user.id, plan: user.plan });
switch (variant) {
case 'v2-semantic': return semanticSearch(query);
case 'v2-hybrid': return hybridSearch(query);
default: return legacySearch(query);
}
}Server-Side: Full context available, no rule exposure to clients, lower latency
Best for: API behavior, backend logic, data processing
Client-Side: Evaluation in browser/app, instant UI changes without server round-trip
Best for: UI variations, layout changes, frontend features
Hybrid: Server evaluates flags, passes resolved values to client
Best for: Security-sensitive flags with UI impactflag: new-checkout-experience
default: false
targeting:
- name: vip-supersede
priority: 0
conditions:
- attribute: userId
operator: in
value: ["user-123", "user-456"]
serve: true
- name: internal-users
priority: 1
conditions:
- attribute: email
operator: ends_with
value: "@ourcompany.com"
serve: true
- name: beta-program
priority: 2
conditions:
- attribute: beta_opted_in
operator: equals
value: true
serve: true
- name: gradual-rollout
priority: 3
conditions:
- attribute: country
operator: in
value: ["US", "CA", "GB"]
serve:
percentage: 25
sticky: true
bucket_by: userIdPhase 1: Internal (Day 1) - @company.com emails, 2-3 days
Phase 2: Dogfood (Day 3) - Beta opt-ins, 3-5 days
Phase 3: Canary (Day 7) - 5% production traffic, 2-3 days
Phase 4: Gradual (Day 10+) - 5% → 10% → 25% → 50% → 100%
- Increase only when metrics stable, wait 24h between steps
Phase 5: Full Rollout (Day 17+) - 100%, monitor 1 week, then cleanup
Rollback Triggers (any phase):
- Error rate > 2x baseline
- Latency P99 > 50% increase
- Conversion rate drops > 5%
- Action: Disable flag immediately (instant rollback)function isInRolloutPercentage(flagKey: string, userId: string, percentage: number): boolean {
const hash = murmurhash3(`${flagKey}:${userId}`);
const bucket = (hash % 10000) / 100;
return bucket < percentage;
}
// Properties:
// - Same user always gets same result for a given percentage
// - Increasing percentage only adds users, never removes existing ones
// - Different flags distribute users differently (via flag key in hash)experiment:
name: checkout-redesign
hypothesis: "New checkout will improve conversion rate by 10%"
primary_metric: checkout_completion_rate
secondary_metrics: [average_order_value, time_to_complete_checkout]
variants:
- name: control
weight: 50
- name: treatment
weight: 50
guardrail_metrics:
- name: error_rate
threshold: 5
- name: p99_latency_ms
threshold: 3000
sample_size:
minimum_per_variant: 5000
estimated_duration: "14 days"
analysis:
statistical_method: bayesian
confidence_level: 0.95### Experiment: Checkout Redesign
Duration: 14 days | Sample: 12,400 users per variant
Primary Metric: Checkout Completion Rate
| Variant | Users | Rate | vs Control |
|-----------|--------|-------|-------------------------|
| Control | 12,400 | 28.0% | baseline |
| Treatment | 12,400 | 31.0% | +3.0pp (+10.7%), p=99.2% |
Guardrail Metrics: Error Rate 1.2% (< 5% threshold) PASS
P99 Latency 1850ms (< 3000ms) PASS
Recommendation: SHIP treatment variant.Flags become stale when:
- Rollout reached 100% more than 14 days ago
- Experiment concluded more than 7 days ago
- Flag not evaluated in 30 days
- Created 90+ days ago and still in development
Automated workflow:
1. Weekly scan → 2. Slack to owner → 3. Remind after 7 days
4. Escalate to team lead after 14 days → 5. Auto-archive after 30 daysBefore Removing:
- [ ] Flag at 100% for all environments for 14+ days
- [ ] No incidents related to the flagged feature
- [ ] No other flags depend on this flag
Code Cleanup:
- [ ] Remove flag evaluation calls from application code
- [ ] Remove the "off" code path (old behavior)
- [ ] Remove flag-specific tests, add tests for permanent behavior
- [ ] Remove flag from configuration/dashboard
Verification:
- [ ] All tests pass with flag code removed
- [ ] No references to flag key in codebase
- [ ] Deployment successful after cleanupPattern: <type>-<feature>-<detail>
Release: enable-new-checkout, enable-search-v2
Experiment: exp-pricing-page-layout, exp-onboarding-flow-v3
Ops: ops-rate-limit-threshold, ops-cache-ttl-seconds
Kill: kill-external-api-calls, kill-payment-processing
Permission: perm-advanced-analytics, perm-api-accessCREATE TABLE flags (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
key VARCHAR(100) UNIQUE NOT NULL,
name VARCHAR(255) NOT NULL,
description TEXT,
flag_type VARCHAR(50) NOT NULL,
status VARCHAR(20) NOT NULL DEFAULT 'inactive',
owner_team VARCHAR(100) NOT NULL,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
last_evaluated_at TIMESTAMP WITH TIME ZONE,
expected_removal_date DATE,
tags TEXT[]
);
CREATE TABLE flag_rules (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
flag_id UUID REFERENCES flags(id),
environment VARCHAR(50) NOT NULL,
priority INTEGER NOT NULL,
conditions JSONB NOT NULL,
serve_value JSONB NOT NULL,
enabled BOOLEAN DEFAULT true
);DO:
- Use flags for all production releases
- Set an expected removal date when creating
- Clean up flags within 2 weeks of 100% rollout
- Test both flag-on and flag-off paths
- Use server-side evaluation for sensitive logic
DO NOT:
- Use flags as a long-term configuration system
- Nest flags more than 2 levels deep
- Create flags without an owner
- Store secrets in flag values
- Make critical logic depend on flag service availability
- Create a flag per customer (use targeting rules instead)Gather information. Ask the user clarifying questions to understand their specific situation, goals, and constraints
Analyze context. Review the information provided and identify key factors relevant to feature flag platform
Develop recommendations. Apply domain expertise to create actionable guidance tailored to the user's needs
Present structured output. Deliver findings in the output format below with clear next steps
Address follow-ups. Answer additional questions and refine recommendations based on feedback
## Feature Flag Platform Analysis
### Assessment
[Key findings and observations]
### Recommendations
1. [Primary recommendation]
2. [Secondary recommendation]
3. [Additional suggestions]
### Action Items
- [ ] [First action step]
- [ ] [Second action step]
- [ ] [Follow-up task]Incomplete information: Ask clarifying questions before proceeding with recommendations
Conflicting requirements: Prioritize the most critical constraint and note trade-offs
Out of scope requests: Redirect to appropriate specialized skill or professional resource
Beginner vs advanced: Adjust depth and terminology based on user's experience level
Input: "Help me with feature flag platform for my current situation"
Output:
Based on your situation, here is a structured approach to feature flag platform:
Assessment: Evaluate your current state and identify key areas for improvement
Strategy: Develop a targeted plan based on best practices
Implementation: Execute the plan with specific, measurable steps
Review: Monitor progress and adjust as needed
SKILL.md
412 lines · 1,634 words
The text of SKILL.md is shown as it downloads, under the Apache-2.0 license, by Ferrox Labs. The license is the one in the file's own frontmatter.
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