talont-org/exp-lens-error-budget

Error Budget Experimental Design Lens

Philosophical Mode: Statistical Primary Question: "Are error risks sized and controlled?" Focus: Type I/II Errors, Power, Minimum Detectable Effect, Multiplicity, Sequential Monitoring

Arguments

/autoskillit:exp-lens-error-budget [context_path] [experiment_plan_path]

• context_path (optional positional arg 1) — Absolute path to a lens context file containing IV/DV tables, H0/H1 hypotheses, controlled variables, and success criteria. If provided, read this file before beginning analysis to obtain structured context. If omitted, discover context by exploring the CWD.
• experiment_plan_path (optional positional arg 2) — Absolute path to the full experiment plan. If provided, read for complete experimental methodology and design. If omitted, locate the experiment plan by exploring the CWD.

When to Use

• Need to verify statistical power before running an experiment
• Multiple comparisons are performed without a stated correction strategy
• Sequential testing or interim analysis is in use without defined stopping rules
• User invokes /autoskillit:exp-lens-error-budget or /autoskillit:make-experiment-diag error

Critical Constraints

NEVER:

• Modify any source code files
• Do not litter the codebase with useless comments, TODO markers, or explanatory annotations — the skill output and diagram speak for themselves
• Accept default alpha=0.05 without checking whether it is appropriate for the decision context
• Create files outside {{AUTOSKILLIT_TEMP}}/exp-lens-error-budget/
• Run subagents in the background (run_in_background: true is prohibited)

ALWAYS:

• Enumerate every statistical test and account for its error contribution

• Distinguish per-test error rates from family-wise error rates

• Flag any sequential peeking without a formal stopping rule as a critical defect

• Evaluate whether the minimum detectable effect is practically meaningful, not just statistically chosen

• BEFORE creating any diagram, LOAD the /autoskillit:mermaid skill using the Skill tool - this is MANDATORY

• If the Skill tool cannot be used (disable-model-invocation) or refuses this invocation, do NOT proceed with diagram creation. Abort this step and omit the diagram from output.

• Write output to {{AUTOSKILLIT_TEMP}}/exp-lens-error-budget/exp_diag_error_budget_{YYYY-MM-DD_HHMMSS}.md

• After writing the file, emit the structured output token as literal plain text with no markdown formatting on the token name (the adjudicator performs a regex match):

  diagram_path = /absolute/path/to/{{AUTOSKILLIT_TEMP}}/exp-lens-error-budget/exp_diag_error_budget_{...}.md
  

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Analysis Workflow

Step 0: Parse optional arguments

If positional arg 1 (context_path) is provided and the file exists, read it to obtain IV/DV tables, H0/H1 hypotheses, controlled variables, and success criteria. If positional arg 2 (experiment_plan_path) is provided and exists, read the experiment plan for full methodology. Use this structured context as the foundation for Steps 1-5; skip the CWD exploration for these fields if the context file supplies them.

Step 1: Launch Parallel Exploration Subagents

Spawn Explore subagents to investigate:

Sample Size & Power

• Find power calculations or sample size justifications
• Look for: power, sample_size, n_samples, effect_size, minimum_detectable, mde

Multiple Comparisons

• Find all statistical tests performed and correction strategies
• Look for: bonferroni, fdr, holm, bh, correction, multiple, comparisons, tests

Sequential Analysis

• Find interim analyses, stopping rules, or sequential monitoring
• Look for: interim, early_stopping, sequential, alpha_spending, peek, monitor

Decision Thresholds

• Find significance thresholds and decision rules
• Look for: alpha, p_value, threshold, significance, reject, null, hypothesis

Effect Size Context

• Find practical significance alongside statistical significance
• Look for: effect_size, cohen, practical, meaningful, magnitude, difference

Step 2: Build the Error Budget

For each statistical claim:

1. What is the per-test Type I error rate?
2. What is the family-wise Type I error rate?
3. What is the power (1 - Type II error)?
4. What is the minimum detectable effect?
5. Is sequential monitoring in use, and if so, what stopping rule is defined?
6. Is the chosen alpha appropriate for the decision context?

Step 3: Analyze Error Allocation

For each test, rate alignment as: ALIGNED / CONVENTIONAL / MISALIGNED

Step 4: Create Optional Decision-Flow Diagram

If a diagram adds value, show Data → Tests → Thresholds → Conclusions, with labeled error rates.

Step 5: Write Output

Write the analysis to: {{AUTOSKILLIT_TEMP}}/exp-lens-error-budget/exp_diag_error_budget_{YYYY-MM-DD_HHMMSS}.md (relative to the current working directory)

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Pre-Diagram Checklist

Before creating the diagram, verify:

• [ ] LOADED /autoskillit:mermaid skill using the Skill tool
• [ ] Using ONLY classDef styles from the mermaid skill (no invented colors)
• [ ] Diagram will include a color legend table

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Related Skills

• /autoskillit:make-experiment-diag - Parent skill
• /autoskillit:mermaid - MUST BE LOADED before creating diagram
• /autoskillit:exp-lens-severity-testing
• /autoskillit:exp-lens-variance-stability