Claude Skills is a library of reusable capability packages that give Claude task-specific instructions, terminology, and output formats for building, launching, operating, and growing websites and brands. People working on branding, design, content, SEO, development, operations, growth, or research can use the skills across different website stacks. The catalogue contains selected skills and plugins from the library.
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 rampstackco/claude-skills --skill experiment-designgit 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/experiment-design)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/experiment-design"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/experiment-design/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/rampstackco/claude-skills/experiment-design"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/experiment-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00194 | $0.06363 |
| Opus 5.5 | $0.00078 | $0.02545 |
| Sonnet 5.5 | $0.00039 | $0.01273 |
| Haiku 4.5 | $0.00019 | $0.00636 |
Grade A, and why
experiment-design 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 — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Design
A senior product manager's playbook for running experiments that produce trustworthy decisions.
The default state of experimentation in most companies is sloppy. PMs run tests against vague hypotheses, look at results too early, ignore guardrails, stratify into noise, and ship features whose lift is mostly measurement error. The cost is real: ship the wrong thing, kill the right thing, learn the wrong lesson, repeat.
This skill is the discipline that prevents most of those mistakes. It assumes you have a working experimentation platform (Statsig, PostHog, GrowthBook, Optimizely, Amplitude, Eppo, Kameleoon; the platform does not matter for the principles). It assumes you have product-design and engineering pipelines that can deliver real treatment changes. The hard part is the thinking, and that is what is here.
When to use this skill: any time you are about to design or interpret an experiment. Read the relevant section before you start, not after the test is running.
What this skill covers
The skill spans the full experiment lifecycle. Pre-experiment readiness (is this thing even worth testing). Hypothesis design (cause, effect, magnitude, mechanism). Sample size and minimum detectable effect (do you have enough traffic to learn anything). Duration (how long is long enough, when does the cycle bias the result). Running discipline (no peeking, guardrails, sequential testing). Interpretation (the three buckets and the inconclusive case). Decision-making (matching the result to a pre-committed rule).
The skill does not cover feature flag operational mechanics; those live in the feature-flagging skill, which handles flag taxonomy, environment management, and stale-flag cleanup as a separate discipline. The skill does not cover statistical analysis depth; for delta methods, variance reduction techniques like CUPED, and Bayesian alternatives, see the experimentation-analytics skill. The skill does not cover platform-specific tooling; for MCP commands, auth models, and platform-specific configuration, consult the chosen platform's official documentation. This skill produces the experiment design; the platform implements it.
What ships with it
8 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.
- README.md 358 B
- references/common-failures.md 11 KB
- references/hypothesis-templates.md 8.5 KB
- references/platform-comparison.md 10 KB
- references/post-experiment-decision-framework.md 6.3 KB
- references/pre-experiment-readiness-checklist.md 5.3 KB
- references/results-interpretation-checklist.md 7.5 KB
- references/sample-size-tables.md 6.2 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.
- 1mo ago First seen · 262 lines · 194 tokens per session scan A 357daa03e27b
experiment-design is a skill published in the GitHub repository rampstackco/claude-skills (1,001 stars, last pushed yesterday), licensed MIT. It adds 194 tokens to every session and 6,363 once invoked, about $0.0008 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 2026-08-30.
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