analyze-experiment-results

SkillDev tools

Lets your agent analyze claude skill experiment results using pre-declared statistical tests and reproducibility checks.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the analyze-experiment-results skill

About this skill

Interpret completed experimental outputs after host/runtime execution using pre-declared statistical tests, effect/uncertainty estimates, reproducibility checks, and calibrated synthesis.

What this skill tells your AI

The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/analyze-experiment-results/SKILL.md and read by ahel’s review.

Purpose

Interpret completed experimental outputs after host/runtime execution using pre-declared statistical tests, effect/uncertainty estimates, reproducibility checks, and calibrated synthesis.

Input contract

required: [experiment_results, predeclared_analysis_plan, reproducibility_target]
optional: [assumptions, prior_findings, evidence_updates]
constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

  1. You MUST load skill statistical-testing to run the preregistered statistical tests and retain effect uncertainty.
  2. You MUST load skill verify-reproducibility to verify the declared reproduction level. If the results must be assembled into claims, evidence, and counterclaims, consider construct-argument-map. If several interventions or methods require comparative selection, consider rank-candidates.

Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.

Output contract

produces: [effect_estimates, uncertainty_summary, reproducibility_assessment, interpretation]
delta_fields: [uncertainties]

Thresholds and quality gates

  • Each output is traceable to an input object, operation, and evidence reference.
  • Scope, assumptions, and unresolved alternatives remain explicit.
  • Retain $\alpha$ 0.05 and power 0.8 wherever the predeclared statistical design requires them.

Failure and counterexamples

Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.

Provenance map

  • intermediate: experiment-execution/result-analysis [strategy]
  • resolved: result-validation-loop
  • resolved: statistical-testing
  • resolved: reproducibility-verification
  • resolved: execution-synthesis
  • resolved: result-collection

Preserved source criteria ledger

sourcecriteriontreatment
resolved v3 entries abovenode-specific criteriaretained and specialized to the v4 object contract
experiment-execution/statistical-testing$\alpha$ = 0.05fixed value retained where applicable
experiment-execution/sample-size-estimationpower = 0.8fixed value retained where applicable

Context checkpoint / Delta notes

Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.

Signals

GitHub stars
501
Forks
41
Last commit
Sep 2026
Advanced
Catalog kind
skill
Key
analyze-experiment-results
Source
github.com/yogsoth-ai/de-anthropocentric-research-engine