Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill designing-experimentsgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-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/foryourhealth111-pixel/vibe-skills/designing-experiments)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/designing-experiments"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/designing-experiments/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/foryourhealth111-pixel/vibe-skills/designing-experiments"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/designing-experiments.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 | $0.00074 | $0.00499 |
| Opus 5 | $0.00037 | $0.00249 |
| Sonnet 5 | $0.00015 | $0.00100 |
| Haiku 4.5 | $0.00007 | $0.00050 |
Grade A, and why
designing-experiments 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 12d 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.
What it actually says
Designing Experiments
Helps choose and specify a research design before data analysis starts. This skill owns study-design decisions: what is treated, what is compared, what outcome is measured, which assumptions are required, which validation or recovery experiment should follow a failed scientific experiment, and which design is defensible.
It does not fit causal models, estimate treatment effects, interpret fitted model output from existing data, or debug software/build failures.
Decision Framework
-
Control Group?
- Yes: Go to Step 2.
- No: Consider Interrupted Time Series (ITS).
-
Unit Structure?
- Single Treated Unit:
- With multiple controls: Synthetic Control (SC).
- No controls: ITS.
- Multiple Treated Units:
- With control group: Difference-in-Differences (DiD).
- Single Treated Unit:
-
Time Structure?
- Panel Data (Multiple units over time): Required for DiD and SC.
- Time Series (Single unit over time): Required for ITS.
Method Quick Reference
- Difference-in-Differences (DiD): Compares trend changes between treated and control groups. Assumes Parallel Trends.
- Interrupted Time Series (ITS): Analyzes trend/level change for a single unit after intervention. Assumes Trend Continuity.
- Synthetic Control (SC): Constructs a synthetic counterfactual from weighted control units. Assumes Convex Hull (treated unit within range of controls).
Failed Experiment Recovery
When a scientific experiment or optimization plan produces weak or contradictory results, use the same design surface to:
- Separate implementation or measurement errors from design-assumption failures.
- Identify which assumption should be tested next.
- Define a minimal validation experiment before abandoning the approach.
- State the decision rule for continuing, revising, or stopping the line of work.
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.
- 12d ago First seen · 43 lines · 74 tokens per session scan A 6d8432ef4448
designing-experiments is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 11d ago), licensed Apache-2.0. It adds 74 tokens to every session and 499 once invoked, about $0.0004 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.
Other skills, from other repositories
data-scientist
Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.
statistical-analyst
Applied statistics for business and product questions — test selection, assumption checks, power planning, effect sizes with intervals, multiplicity correction. Use when interpreting an experiment, sizing a study, or vetting a claim.
jupyter-live-kernel
Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a…
neuroskill-bci
Connect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40+ derived EXG scores) into responses. Requires a BCI wearable (Muse 2/S or OpenBCI) and the NeuroSkill desktop app…
sparse-autoencoder-training
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
research-paper-writing
End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.