feature-flag-lifecycle-manager

feature-flag-lifecycle-manager is a skill for Claude Code, Codex from codebygarv/Ai-skills. It costs 28 tokens per session (400 once invoked), scanned A, original, MIT.

A lifecycle guide for feature flags, switches that turn code paths on or off for selected users or environments. It covers naming, safe defaults, staged rollouts, monitoring, and removing flags that are no longer needed.

In plain words
What is it for?
Use it when introducing feature-flag tools, planning canary or percentage rollouts, defining fallback behavior, and retiring fully released flags.
Why use it?
It helps teams release risky changes gradually and avoid leaving old switches and unreachable code in the project.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/codebygarv/ai-skills/feature-flag-lifecycle-manager
Any agent
npx skills add codebygarv/Ai-skills --skill feature-flag-lifecycle-manager
Clone the repo
git clone --depth 1 https://github.com/codebygarv/Ai-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for feature-flag-lifecycle-manager

README.md
[![agentmods](https://agentmods.dev/badge/skills/codebygarv/ai-skills/feature-flag-lifecycle-manager.svg)](https://agentmods.dev/skills/codebygarv/ai-skills/feature-flag-lifecycle-manager)
Your own site
<a href="https://agentmods.dev/skills/codebygarv/ai-skills/feature-flag-lifecycle-manager"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/feature-flag-lifecycle-manager.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 400 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.00400
Opus 5 $0.00014 $0.00200
Sonnet 5 $0.00006 $0.00080
Haiku 4.5 $0.00003 $0.00040

Measured 5d ago against content hash e16bdd75ff43, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

feature-flag-lifecycle-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/architecture/feature-flag-lifecycle-manager/SKILL.md · 34 lines

What it actually says

Purpose

Manage feature flags from creation to deprecation, establishing standardized flag taxonomy, safe fallback defaults, phased rollout rings (canary/percentage/user groups), and automated dead-code cleanup to prevent technical debt accumulation.

When to Use

  • Introducing feature flag tooling (LaunchDarkly, Unleash, PostHog, custom Redis flags).
  • Codebases clogged with hundreds of ancient, permanent boolean flags.
  • Releasing risky changes using percentage rollouts and progressive ring deployments.

What to Analyze

  1. Flag Taxonomy & Lifetime: Release Toggle (short-lived, days) vs Experiment Toggle (weeks) vs Kill Switch (permanent) vs Permission Toggle.
  2. Safe Fallback Defaults: Guaranteed fail-safe default value if the flag service is unreachable.
  3. Rollout Rings: Ring 0 (Internal Canary / Dogfood) $ ightarrow$ Ring 1 (5% Beta) $ ightarrow$ Ring 2 (25%) $ ightarrow$ Ring 3 (100% GA).
  4. Observability & Blast Radius: Metric dashboards linked to flag states (auto-rollback on elevated 5xx errors).
  5. Retirement & Dead Code Removal: Automated Jira/GitHub cleanup tickets created upon 100% rollout.

Output Format

  • Feature Flag Specification Sheet: Name, Type, Owner, Fallback Value, Expiration Date, Rollout Schedule.
  • Type-Safe Wrapper Code: TypeScript / backend typed flag client with fallback safety.
  • Cleanup PR Plan: Instructions to remove flag checks once fully deployed.

Avoid

  • Leaving release toggles in production code months after 100% rollout (creates spaghetti debt).
  • Making synchronous network calls to flag servers on critical database write paths.
Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 34 lines · 28 tokens per session scan A e16bdd75ff43

Subscribe to this mod's changes

feature-flag-lifecycle-manager is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 16d ago), licensed MIT. It adds 28 tokens to every session and 400 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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