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 EvoScientist/EvoSkills --skill experiment-pipelinegit clone --depth 1 https://github.com/EvoScientist/EvoSkillsWrote 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/evoscientist/evoskills/experiment-pipeline)<a href="https://agentmods.dev/skills/evoscientist/evoskills/experiment-pipeline"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/experiment-pipeline/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/evoscientist/evoskills/experiment-pipeline"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/experiment-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00129 | $0.03718 |
| Opus 5 | $0.00064 | $0.01859 |
| Sonnet 5 | $0.00026 | $0.00744 |
| Haiku 4.5 | $0.00013 | $0.00372 |
Grade A, and why
experiment-pipeline 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 9d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Pipeline
A structured 4-stage framework for executing research experiments from initial implementation through ablation study, with attempt budgets and gate conditions that prevent wasted effort. This follows the Experiment Tree Search design from the EvoScientist paper, where the engineer agent iteratively generates executable code, runs experiments, and records structured execution results at each stage.
When to Use This Skill
- User has a planned experiment and needs to organize the execution workflow
- User wants to systematically validate a novel method against baselines
- User asks about experiment stages, attempt budgets, or when to move on
- User needs to reproduce baseline results before testing their method
- User mentions "experiment pipeline", "baseline first", "ablation study", "stage budget", "experiment execution"
The Pipeline Mindset
Experiments fail for two reasons: wrong order and no stopping criteria. Most researchers jump straight to testing their novel method without verifying their baseline setup, then wonder why results don't make sense. Others spend weeks tuning hyperparameters without a budget, hoping the next run will work.
The 4-stage pipeline solves both problems. It enforces a strict order (each stage validates assumptions the next stage depends on) and assigns attempt budgets (forcing systematic thinking over brute-force iteration).
Before Starting: Load Prior Knowledge
If coming from research-ideation, your research proposal (Step 7) provides the experiment plan — datasets, baselines, metrics, and ablation design — that maps directly to Stages 1-4 below.
Before entering the pipeline, load Experimentation Memory (M_E) from prior cycles:
- Refer to the evo-memory skill → Read M_E at
/memory/experiment-memory.md - Select the top-1 entry (k_E=1) most relevant to the current experiment domain by comparing each entry's Context and Category against the current problem
- The selected strategy informs hyperparameter ranges (Stage 2), debugging approaches (Stages 1-3), and training configurations across all stages
- If M_E doesn't exist yet (first cycle), skip this step and proceed — your results will seed M_E via ESE after pipeline completion
What ships with it
5 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.
- 9d ago First seen · 260 lines · 129 tokens per session scan A 88e68f4c1334
experiment-pipeline is a skill published in the GitHub repository EvoScientist/EvoSkills (436 stars, last pushed 8d ago), licensed Apache-2.0. It adds 129 tokens to every session and 3,718 once invoked, about $0.0006 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 2026-08-30.
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