Borrowing it
Nothing to install: this file belongs to irahardianto/awesome-agv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/irahardianto/awesome-agv/main/.agents/skills/feature-flags/SKILL.mdgit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote 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/irahardianto/awesome-agv/feature-flags)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/feature-flags"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/feature-flags/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/feature-flags"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/feature-flags.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 131 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 5.1 | $0.00068 | $0.01226 |
| Opus 5 | $0.00034 | $0.00613 |
| Sonnet 5 | $0.00014 | $0.00245 |
| Haiku 4.5 | $0.00007 | $0.00123 |
Grade A, and why
feature-flags 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 10d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Flags Principles
[!CAUTION] Do NOT implement feature flags unless explicitly required.
Feature flags add real operational complexity: flag evaluation infrastructure, lifecycle management, and a multiplicative increase in test permutations. The cost is non-trivial for solo developers or simple projects.
Only introduce feature flags when the PRD or technical architecture document explicitly specifies one of:
- Gradual / percentage rollout of a risky feature
- A/B testing or experimentation
- Emergency kill switches for production code paths
- Feature entitlement gating by user tier or permission
If none of these are specified, deploy code directly. Do not introduce flags speculatively.
When to Use Feature Flags
Feature flags decouple deployment from release. Code ships to production but stays dormant until the flag enables it. This is valuable for:
| Use case | Flag type | Example |
|---|---|---|
| Gradual rollout | Release flag | new-checkout-flow: 0→5→25→100% |
| Emergency kill switch | Ops flag | use-legacy-payment-provider |
| A/B test / experiment | Experiment flag | button-color-blue-vs-green |
| Tier / permission gating | Permission flag | pro-tier-analytics |
Infrastructure Requirements
[!IMPORTANT] The flag evaluation backend must be specified in the technical architecture document before implementation begins. Do NOT choose a provider independently — ask the user which infrastructure to use.
| Approach | When to use | Notes |
|---|---|---|
| Managed SaaS | Teams > 5, multi-environment, complex targeting | LaunchDarkly, Flagsmith, Unleash Cloud |
| Self-hosted | Full control, no SaaS dependency | Unleash (OSS), Flagsmith (OSS) |
| Firebase Remote Config | Mobile apps in the Firebase ecosystem | Firebase SDK required |
| Static config (YAML / env) | Solo dev, simple on/off, single environment | Loaded at startup; no runtime targeting |
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.
- 10d ago First seen · 137 lines · 68 tokens per session scan A 007e6afda7a7
feature-flags is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 19d ago), licensed MIT. It adds 68 tokens to every session and 1,226 once invoked, about $0.0003 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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