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 experiments-launchdarkly-experiment-setupgit 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/experiments-launchdarkly-experiment-setup)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/experiments-launchdarkly-experiment-setup"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/experiments-launchdarkly-experiment-setup/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/experiments-launchdarkly-experiment-setup"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/experiments-launchdarkly-experiment-setup.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 | $0.00046 | $0.02776 |
| Opus 5.5 | $0.00018 | $0.01110 |
| Sonnet 5.5 | $0.00009 | $0.00555 |
| Haiku 4.5 | $0.00005 | $0.00278 |
Grade A, and why
launchdarkly-experiment-setup 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 1mo 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LaunchDarkly Experiment Setup
You're using a skill that guides you through setting up and running experiments in LaunchDarkly. Your job is to design the experiment, create it with the right metrics, treatments, and flag config, start data collection, evolve the design between iterations when needed, and stop with a winner.
Prerequisites
This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.
Required MCP tools:
create-experiment— create a new experiment with its initial iteration (hypothesis, metrics, treatments, flag config).start-experiment-iteration— begin collecting data for an experiment's current draft iteration.get-experiment— check experiment status, treatments, metrics, and current iteration.
Optional MCP tools:
list-experiments— browse existing experiments in the project.update-experiment— update fields on the experiment or its current iteration. HonoursmutableFieldsByStatus, so what's editable depends on whether the iteration isnot_started,running, orstopped. Returns rejected inputs underskipped.save-and-start-experiment-iteration— the API-recommended way to change locked fields on a running experiment. Stops the current iteration, creates a new draft with the supplied field updates, and starts it in one call.stop-experiment-iteration— stop the running iteration. You must declare a winner: pass thewinningTreatmentId(and awinningReason). If no variation outperformed, pick the baseline/control as the winner.list-metrics,create-metric,list-metric-events— manage metrics referenced by the experiment.
Core Concepts
What Are Experiments?
Experiments in LaunchDarkly measure the impact of feature flag variations on key metrics. An experiment consists of:
- Treatments: the flag variations being compared (control vs. test). Each treatment has an
allocationPercent; the values across treatments should sum to 100. - Metrics: what you're measuring (conversion rate, latency, revenue, etc.). One must be the primary metric.
- Flag config: the
flagKey,ruleId, andflagConfigVersionof the targeting rule that drives the experiment. - Iteration: a single data-collection window. Created in
not_startedstatus, becomesrunningwhen started, transitions tostoppedwhen ended. - Holdout (optional): a project-level group of users excluded from the experiment for baseline measurement (
holdoutId).
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.
- 1mo ago First seen · 239 lines · 46 tokens per session scan A 406c39faf919
launchdarkly-experiment-setup is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 2,776 once invoked, about $0.0002 per session on Opus 5.5. 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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