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 TimboGP/timbogp-marketplace --skill run-experimentgit clone --depth 1 https://github.com/TimboGP/timbogp-marketplaceWrote 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/timbogp/timbogp-marketplace/run-experiment)<a href="https://agentmods.dev/skills/timbogp/timbogp-marketplace/run-experiment"><img src="https://agentmods.dev/badge/skills/timbogp/timbogp-marketplace/run-experiment/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/timbogp/timbogp-marketplace/run-experiment"><img src="https://agentmods.dev/badge/skills/timbogp/timbogp-marketplace/run-experiment.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 | $0.00150 | $0.00994 |
| Opus 5.5 | $0.00060 | $0.00398 |
| Sonnet 5.5 | $0.00030 | $0.00199 |
| Haiku 4.5 | $0.00015 | $0.00099 |
Grade A, and why
run-experiment 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run an experiment
A single trip around the Build-Measure-Learn loop. This skill turns a risky assumption into a falsifiable hypothesis, designs the smallest test that produces real learning, and logs the result. You work as the Analyst guide role (../../reference/roles.md). Read references/build-measure-learn.md before designing.
When to use
The user wants to test an assumption (usually the top one from prioritize-risks), needs help making it testable, or wants to record what an experiment taught them. Customer interviews are a kind of experiment but have their own skill (customer-interview); use this skill for landing-page / smoke / concierge / pricing / channel tests and for the general framing and logging of any experiment.
Core stance
- Maximize speed, learning, and focus together. Drop any one and you get a failure mode: no learning → chasing your tail; no speed → run out of resources; no focus → premature optimization.
- One key metric per experiment. Decide the single number you're moving and ignore the rest for this experiment.
- Smallest thing possible to learn. You rarely need code. Mock-ups, a landing page, a concierge ("Wizard of Oz") MVP, a manual back end, a fake-door button — pick the cheapest artifact that yields a real signal.
- Bold before product/market fit. Pre-fit, you learn most when the outcome is ~50/50, so prefer bold experiments (a different UVP, a different segment) over incremental tweaks. Save micro-optimization for after fit.
Procedure
- Load context. Read
.lean/risks.md(what's riskiest),.lean/PROGRESS.md(stage), and the canvas. Confirm which assumption this experiment attacks. - Write a falsifiable hypothesis. Form:
[specific repeatable action] will [expected measurable outcome]— a statement that can be clearly proven wrong. Make the outcome a concrete number and threshold ("at least 20% of landing-page visitors will enter their email"), not "people will like it." - Pick the single key metric and the success/fail threshold up front (pre-commit, so you can't rationalize a fuzzy result into a "yes").
- Design the smallest build. Choose the cheapest artifact that tests it (see the menu in
references/build-measure-learn.md). Define how you'll measure and the minimum sample/time to trust the signal. - Run / record. When results are in, record the measurement, then the learning: did it validate or refute the hypothesis, and what decision follows (proceed / pivot / iterate / kill)? Capture surprising side-learning loosely as insights to fold into the canvas later.
- Write
.lean/experiments/<id>.mdfromassets/experiment-template.md(<id>likeE1,E2). Update.lean/PROGRESS.md(a dated Journal line). If the result retires or moves a risk, update.lean/risks.md. - Close the loop. Name the next experiment the result implies, and route (back to
prioritize-risksif the riskiest assumption changed,lean-canvasto fold in learning,measure-fitif you're accumulating fit signal).
What ships with it
2 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.
- 1mo ago First seen · 42 lines · 150 tokens per session scan A b43e88c99743
run-experiment is a skill published in the GitHub repository TimboGP/timbogp-marketplace (3 stars, last pushed 13d ago), licensed MIT. It adds 150 tokens to every session and 994 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
gan-zhong-xue
A learning workflow that makes an AI explain code changes through questions, evidence, and underlying principles. It is designed to help developers understand work they asked an AI to do, rather than merely accept the result.
explain
Explain what a piece of code does — a specific file, class, or method in close detail, or a user-facing flow as a concise system overview. What it does and why, not whether it's good.
qector-educator
Teaching and documentation content for quantum error correction with QECTOR: beginner-to-advanced tutorials, ELI5 explanations of syndromes / decoders / parity checks, Tanner-graph visual explanations, and local Markdown teaching material. Load for explanations, course material, or documentation generation.
kb-friendica
Background knowledge about Friendica, the federated social networking platform (Facebook alternative). Covers Friendica's history and lineage (Mistpark 2010, Friendika, Friendica, the Red Matrix/Hubzilla fork), its multi-protocol architecture (ActivityPub, DFRN, Diaspora protocol simultaneously), the DFRN protocol…
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.
elixir-idioms
OTP/BEAM patterns and Elixir idioms — GenServer, Supervisor, Task, Registry, pattern matching, with chains, pipes. Use when designing processes or debugging BEAM issues.