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 AlexisMarasigan/coldoutboundskills --skill experiment-designgit clone --depth 1 https://github.com/AlexisMarasigan/coldoutboundskillsWrote 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/alexismarasigan/coldoutboundskills/experiment-design)<a href="https://agentmods.dev/skills/alexismarasigan/coldoutboundskills/experiment-design"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/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/alexismarasigan/coldoutboundskills/experiment-design"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/experiment-design.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 | $0.00061 | $0.02395 |
| Opus 5 | $0.00030 | $0.01197 |
| Sonnet 5 | $0.00012 | $0.00479 |
| Haiku 4.5 | $0.00006 | $0.00239 |
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 11d 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.
This is a copy
100% identical to experiment-design — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Design
If you change your list, your copy, and your offer at the same time, you learn nothing. This skill forces you to isolate one variable per experiment so you actually learn what's working.
Why this exists
Most cold email operators run "throw-everything" experiments. Campaign 1 gets a new list, new copy, and a new offer. It works better. They declare victory. But they can't tell you WHY — was it the list? The copy? The offer?
Then campaign 2 changes all three again. Regression. Nobody knows why.
This skill is the antidote: plan each experiment around ONE variable, keep everything else constant, and confidence-weight the results.
The three experiment types
A. List-only experiment
- What varies: the list (targeting criteria)
- What stays fixed: copy, offer, sending infrastructure, sequence timing
- What you learn: whether this segment is a better fit than the baseline
- Confidence on learnings: HIGH on targeting, LOW on copy (because copy wasn't tested)
B. Copy-only experiment
- What varies: the copy (subject, body, sequence, or A/B variant)
- What stays fixed: list, offer, infrastructure
- What you learn: whether this copy resonates with this audience
- Confidence on learnings: HIGH on copy, LOW on targeting
C. Combined experiment (use sparingly)
- What varies: list AND copy (and sometimes offer)
- What stays fixed: only infrastructure
- When to use: launching a whole new campaign for a new ICP. You can't isolate because everything is new.
- Confidence on learnings: MEDIUM on everything. Use as hypothesis-generation, not conclusion.
The Framework
Step 1: Name your hypothesis
Every experiment starts with a one-sentence hypothesis:
"Targeting Heads of Marketing at 50-200 person B2B SaaS companies will get a higher positive reply rate than our current VP Sales baseline, because [reason]."
Or:
"Leading with a question about their recent product launch will get a higher reply rate than our current benefit-focused opener, because [reason]."
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.
- 11d ago First seen · 243 lines · 61 tokens per session scan A 22a29205cfc1
experiment-design is a skill published in the GitHub repository AlexisMarasigan/coldoutboundskills (4 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 2,395 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to experiment-design, differing in 0 lines, and is treated as a copy.
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