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 Bilal140202/the-lord-of-the-skills --skill feature-flags-launchdarkly-guarded-rolloutgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-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/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-guarded-rollout)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-guarded-rollout"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-guarded-rollout/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/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-guarded-rollout"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/feature-flags-launchdarkly-guarded-rollout.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00034 | $0.01503 |
| Opus 5 | $0.00017 | $0.00751 |
| Sonnet 5 | $0.00007 | $0.00301 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
launchdarkly-guarded-rollout 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 6d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LaunchDarkly Guarded Rollouts
You're using a skill that will guide you through configuring guarded rollouts in LaunchDarkly. Your job is to design rollout stages, select monitoring metrics, configure regression thresholds, and start the rollout.
Prerequisites
This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.
Required MCP tools:
start-guarded-rollout-- start a progressive rollout with monitoringget-flag-- inspect the flag and its variationslist-metrics-- find metrics to monitor during the rollout
Optional MCP tools:
stop-guarded-rollout-- halt an active rollout immediatelytoggle-flag-- ensure the flag is turned on before startingcreate-metric-- create metrics if they don't exist
Core Concepts
What Are Guarded Rollouts?
A guarded rollout progressively increases traffic to a new feature flag variation through a series of stages. At each stage, LaunchDarkly monitors selected metrics for regressions. If a regression is detected, the rollout can automatically pause and notify the team — or even roll back.
Key Components
| Component | Description |
|---|---|
| Test variation | The new variation being rolled out |
| Control variation | The existing/baseline variation |
| Stages | Steps with increasing traffic percentage and monitoring windows |
| Metrics | What to monitor for regressions (error rate, latency, etc.) |
| Regression threshold | How much a metric can degrade before triggering action |
| On regression | Whether to notify, rollback, or both when a threshold is breached |
Rollout Weight Units
Rollout weights use thousandths (basis points):
1000= 1%10000= 10%50000= 50%100000= 100%
Monitoring Window
The monitoring window is specified in milliseconds:
3600000= 1 hour86400000= 24 hours604800000= 7 days
Core Principles
- Start Small: Begin with a low percentage (1-5%) to catch issues early
- Monitor What Matters: Choose metrics that reflect user experience
- Set Realistic Thresholds: Too tight = false alarms; too loose = missed regressions
- Allow Time: Each stage needs enough monitoring time for signal to emerge
- Have a Rollback Plan: Always configure at least notification on regression
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.
- 6d ago First seen · 174 lines · 34 tokens per session scan A 88bf123a7f9b
launchdarkly-guarded-rollout is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 34 tokens to every session and 1,503 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 2026-09-06.
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