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.
npx agentmods add skills/rampstackco/claude-skills/feature-flaggingnpx skills add rampstackco/claude-skills --skill feature-flagginggit clone --depth 1 https://github.com/rampstackco/claude-skillsWrote 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.
[](https://agentmods.dev/skills/rampstackco/claude-skills/feature-flagging)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/feature-flagging"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/feature-flagging.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00047 | $0.05370 |
| Opus 5 | $0.00023 | $0.02685 |
| Sonnet 5 | $0.00009 | $0.01074 |
| Haiku 4.5 | $0.00005 | $0.00537 |
Grade A, and why
feature-flagging 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.
How it starts
The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Flagging
A senior engineer's playbook for using feature flags well, not just frequently.
Feature flags are infrastructure. Treated as such, they enable kill switches, gradual rollouts, A/B experiments, permission gates, and operational toggles without redeploys. Treated casually, they become the largest accumulating technical debt in your codebase: thousands of dead flags, conflicting evaluation logic, brittle targeting, and a permission surface no one fully understands.
This skill is the discipline that prevents the second outcome. It assumes you have a feature flag platform (LaunchDarkly, Flagsmith, Split.io, VWO FME, GrowthBook, Statsig, PostHog, Optimizely; the platform does not matter for the principles). It assumes your engineering team can implement targeting rules and SDK integration. The hard part is the operational discipline, and that is what is here.
When to use this skill: any time you are about to introduce a flag, modify a flag, audit existing flags, or design a flag-related governance policy.
What this skill is for
The skill spans the operational lifecycle of a flag from creation through retirement. Flag types and the discipline of not mixing them. Naming conventions that survive code review. Lifecycle expectations baked in at creation, not bolted on later. Targeting rules that compose without fragility. Rollout strategies that match the risk profile of the launch. Stale flag management on a quarterly cadence. Governance and permissions that balance access with audit. Performance considerations so flags do not become a latency tax. Testing patterns that cover both branches. Rollback discipline. Observability across rollouts.
The skill does not cover experiment design; for hypothesis writing, sample size, MDE, and the discipline of arriving at a defensible decision, see the experiment-design skill. It does not cover statistical analysis or variance reduction; those live in the experimentation-analytics skill. It does not cover platform-specific tooling; for MCP commands, auth, and platform-specific configuration, consult the chosen platform's documentation. This skill produces the operational shape; the platform implements it.
What ships with it
7 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.
- references/flag-lifecycle-checklist.md 4.9 KB
- references/flag-naming-conventions.md 5.0 KB
- references/flag-rollout-strategies.md 6.9 KB
- references/flag-types-reference.md 6.6 KB
- references/governance-and-permissions.md 6.8 KB
- references/stale-flag-cleanup-playbook.md 6.0 KB
- references/targeting-rule-patterns.md 6.5 KB
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.
- 5d ago First seen · 288 lines · 47 tokens per session scan A 597cb210221f
feature-flagging is a skill published in the GitHub repository rampstackco/claude-skills (817 stars, last pushed 7d ago), licensed MIT. It adds 47 tokens to every session and 5,370 once invoked, about $0.0002 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.
Other skills, from other repositories
r3f-animation
React Three Fiber animation - useFrame, useAnimations, spring physics, keyframes. Use when animating objects, playing GLTF animations, creating procedural motion, or implementing physics-based movement.
workflow
Professional AI programming assistant with structured workflow (Research -> Ideate -> Plan -> Execute -> Optimize -> Review) for developers.
r3f-best-practices
React Three Fiber (R3F) and Poimandres ecosystem best practices. Use when writing, reviewing, or optimizing R3F code. Triggers on tasks involving @react-three/fiber, @react-three/drei, zustand, @react-three/postprocessing, @react-three/rapier, or leva.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
golden-rss
Use when testing the rss golden build.
agile-product-owner
../../../product-team/agile-product-owner/skills/agile-product-owner/SKILL.md.