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 skills add j4flmao/agent-skills --skill feature-flagsgit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/feature-flags)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/feature-flags"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/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/j4flmao/agent-skills/feature-flags"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/feature-flags.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high System Prompt Leakage · line 426 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00086 | $0.05351 |
| Opus 5 | $0.00043 | $0.02676 |
| Sonnet 5 | $0.00017 | $0.01070 |
| Haiku 4.5 | $0.00009 | $0.00535 |
Grade A, and why
backend-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 8d 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 — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend Feature Flags
Purpose
Design feature flag systems with lifecycle management, targeting, and risk controls.
Agent Protocol
Trigger
Exact user phrases: "feature flag", "feature toggle", "canary release", "gradual rollout", "A/B test", "kill switch", "flag management", "LaunchDarkly", "Unleash", "flag evaluation", "targeting rule", "percentage rollout", "feature gate".
Input Context
Before activating, verify:
- Number of flags expected (10s, 100s, 1000s)
- Targeting requirements (user ID, group, percentage, custom attributes)
- Flag lifespan (short-lived release toggles vs long-lived ops toggles)
- Evaluation performance needs (latency budget, cache TTL tolerance)
- Flag management platform (LaunchDarkly, Unleash, Flagsmith, custom)
Output Artifact
Feature flag strategy as formatted text.
Response Format
# Flag definitions with targeting rules
# Evaluation configuration
// Flag evaluation code
// SDK setup
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Flag types classified (release/experiment/ops/permission)
- Evaluation strategy with caching and bulk evaluation
- Targeting rules defined (user, group, percentage, prerequisites)
- Flag lifecycle with creation, evaluation, stabilization, cleanup
- Risk controls (kill switch, auto-rollback, audit log)
- Stale flag detection and removal deadline configured
Max Response Length
200 lines of configuration and code.
Decision Tree
What Type of Flag?
How long does this flag live?
├── Days to weeks (feature under development)
│ └── Release toggle — remove immediately after full rollout
├── Weeks to months (A/B test, experiment)
│ └── Experiment toggle — remove after analysis complete
├── Months to years (kill switch, operational control)
│ └── Ops toggle — highest risk, requires kill switch
└── Permanent (access control, early access)
└── Permission toggle — effectively access control, use RBAC instead
What ships with it
6 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.
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.
- 8d ago First seen · 561 lines · 86 tokens per session scan A 4fa9c6415628
backend-feature-flags is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 5d ago), licensed MIT. It adds 86 tokens to every session and 5,351 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
opentelemetry
OpenTelemetry observability patterns: traces, metrics, logs, context propagation, OTLP export, Collector pipelines, and troubleshooting.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
agui-dotnet-protobuf
Use the protobuf wire transport (instead of the default Server-Sent Events) for an AG-UI connection with the AG-UI .NET SDK — a compact binary event stream negotiated via the Accept header. USE FOR: making an AGUIChatClient prefer protobuf by wiring an AGUIEventStreamHandler with ProtobufEventStreamFormatter (then…
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".
fastapi-router-py
Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.
aws-sdk-java-v2-core
Provides AWS SDK for Java 2.x client configuration, credential resolution, HTTP client tuning, timeout, retry, and testing patterns. Use when creating or hardening AWS service clients, wiring Spring Boot beans, debugging auth or region issues, or choosing sync vs async SDK usage.