/designing-experiments
Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the
$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill designing-experiments --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/designing-experiments
Context preview
The summary Claude sees to decide when to auto-load this skill.
Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the
SKILL.md
designing-experiments.SKILL.mdname: designing-experiments
description: Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.
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
1. **Control Group?**
- **Yes**: Go to Step 2.
- **No**: Consider **Interrupted Time Series (ITS)**.
2. **Unit Structure?**
- **Single Treated Unit**:
- With multiple controls: **Synthetic Control (SC)**.
- No controls: **ITS**.
- **Multiple Treated Units**:
- With control group: **Difference-in-Differences (DiD)**.
3. **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.
Read more
name: designing-experiments description: Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.
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
1. **Control Group?**
- **Yes**: Go to Step 2.
- **No**: Consider **Interrupted Time Series (ITS)**.
2. **Unit Structure?**
- **Single Treated Unit**:
- With multiple controls: **Synthetic Control (SC)**.
- No controls: **ITS**.
- **Multiple Treated Units**:
- With control group: **Difference-in-Differences (DiD)**.
3. **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.
VibeSkills is a general-purpose Skill that automatically routes local Skills and intelligently orchestrates harness workflows.
Repo: foryourhealth111-pixel/Vibe-Skills
Other skills on vibe-skills.
- /LQF_Machine_Learning_Expert_Guide
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature
Open skill - /adaptyv
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use
Open skill - /aeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations
Open skill - /algorithmic-art
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing
Open skill - /alpha-vantage
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash
Open skill - /architecture-patterns
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
Open skill