feature-flags-architect

Overview of feature-flags-architect skill

What feature-flags-architect is for

feature-flags-architect is a Software Architecture skill for designing, rolling out, auditing, and retiring feature flags as a controlled lifecycle rather than scattered if statements. It is most useful when you are adding a risky flag, planning a progressive rollout, comparing providers, validating a kill switch, or cleaning up stale flag debt before a release.

Best-fit users and decisions

This skill fits staff engineers, platform teams, release managers, SREs, and product engineers who need practical flag governance without building a whole internal framework from scratch. It helps answer questions like: “Should this be a release, experiment, operational, or permission flag?”, “How slowly should we ramp?”, “What abort thresholds should we define?”, and “Which flags are old enough to remove?”

What makes the skill useful

The main differentiator is that the repository includes executable support, not just advice. The scripts/ directory provides stdlib Python tools for flag debt scanning, rollout planning, and kill-switch auditing. The references/ directory covers flag taxonomy, lifecycle, provider comparison, and rollout strategies, while assets/flag_request_template.md gives teams a concrete request format for PRs or internal reviews.

When this skill is not the right fit

Avoid treating feature-flags-architect as a replacement for your feature flag platform, observability stack, or incident process. It can guide architecture and generate plans, but it cannot know your real production baselines unless you provide them. It is also less useful for tiny apps with one or two temporary flags unless you specifically need cleanup discipline or a kill-switch checklist.

How to Use feature-flags-architect skill

feature-flags-architect install context

For Claude-compatible skill workflows, install from the GitHub skill directory with:

npx skills add alirezarezvani/claude-skills --skill feature-flags-architect

The skill path is engineering/feature-flags-architect/skills/feature-flags-architect. After install, inspect SKILL.md first, then read references/flag_taxonomy.md, references/flag_lifecycle.md, references/rollout_strategies.md, and references/provider_comparison.md based on your task. Preview assets/flag_request_template.md if you want a repeatable intake form.

Inputs that produce better architecture output

For strong feature-flags-architect usage, include the flag’s purpose, affected code path, risk level, provider, target users, expected lifespan, owner, metrics, rollback method, and cleanup criteria. A weak prompt is: “Create a rollout plan for checkout.” A stronger prompt is: “Use feature-flags-architect to design a release flag for new-checkout-flow in a TypeScript checkout service using LaunchDarkly. Payments and conversion are affected. Baseline error rate is 0.2%, p99 latency is 450ms, and conversion is 4.8%. We need a kill switch, abort thresholds, registry entry, and cleanup plan after 100% rollout.”

Practical workflow for new flags

Start by classifying the flag using references/flag_taxonomy.md; misclassification is a major source of debt. Then fill assets/flag_request_template.md before coding. Ask the skill for a rollout plan matched to risk, not preference: ring/canary for payments, auth, data integrity, or performance-sensitive paths; linear or faster strategies only for lower-risk changes. Finally, keep the flag decision at a module boundary where possible, and document the owner, dashboard, kill-switch trigger, and cleanup date.

Practical workflow for audits and cleanup

For cleanup work, use the repository’s scripts as implementation references or run them against a local repo after reviewing arguments. scripts/flag_debt_scanner.py detects common flag patterns across Python, JavaScript, TypeScript, Go, Ruby, Java, Kotlin, C#, Rust, and PHP, then uses git history to identify older candidates. scripts/kill_switch_audit.py compares discovered code flags against a markdown registry and checks for required fields such as owner, type, kill switch, and dashboard. Use these findings as a review queue, not as an automatic deletion list.

feature-flags-architect skill FAQ

Is feature-flags-architect for Software Architecture or release ops?

Both, but its strongest value is architectural release discipline. The feature-flags-architect skill helps design where the flag decision should live, what lifecycle the flag should follow, which provider tradeoffs matter, and how cleanup will happen. Release operations teams can also use it to standardize rollout and rollback checklists.

How is this better than an ordinary prompt about feature flags?

A generic prompt may produce plausible rollout advice, but it usually misses lifecycle governance: owner, type, expected lifespan, abort threshold, dashboard, kill switch, and cleanup trigger. feature-flags-architect gives the agent a structured taxonomy, documented lifecycle, provider comparison, rollout strategy references, and scripts that anchor the answer in repeatable practices.

Which providers does it cover?

The included provider guide compares LaunchDarkly, GrowthBook, Statsig, Unleash, Flipt, and DIY approaches. It is especially useful when you need to explain why an enterprise tool is worth it, when open source self-hosting matters, or when a simple in-house config is enough. You should still verify current pricing, compliance claims, and SDK behavior directly with the vendor before committing.

Can beginners use this skill safely?

Yes, if they use the templates and avoid skipping the cleanup and monitoring sections. Beginners should start with the taxonomy and lifecycle references before asking for code changes. The key beginner mistake is creating a flag without a named owner, expiry date, or kill-switch threshold; this skill is designed to prevent exactly that.

How to Improve feature-flags-architect skill

Improve prompts with real operational constraints

The best way to improve feature-flags-architect results is to provide production context. Include baseline metrics, acceptable error budgets, traffic volume, deployment frequency, on-call coverage, provider name, and compliance constraints. “Roll this out safely” is too vague; “abort if p99 latency exceeds baseline by 20% for 15 minutes or payment failures increase by 1 percentage point” gives the skill concrete decision boundaries.

Watch for common failure modes

The most common failure modes are overusing flags for low-risk cosmetic changes, leaving release flags at 100% indefinitely, scattering flag checks across a function, and treating permission flags like temporary rollout flags. When reviewing output, check that every temporary flag has a removal condition, every risky launch has an observable abort trigger, and every operational flag has a tested runbook.

Adapt the scripts to your codebase

The included Python scripts use common regex patterns for providers and flag helper functions. If your codebase wraps flags behind custom APIs, extend the regexes before trusting audit coverage. Also tune age thresholds, ignored directories, registry path, and minimum-use settings so the scanner reflects your repository rather than the skill author’s defaults.

Iterate after the first output

After the first plan, ask the skill to critique it against the lifecycle reference: “Find missing owner, dashboard, kill-switch, test, and cleanup fields.” Then ask for a PR checklist, registry entry, or migration plan from scattered checks to a single decision point. For stale flags, ask it to rank candidates by risk: remove simple dead branches first, then handle flags touching payments, auth, permissions, or data writes with extra tests.