analyze-experiment-results
SkillDev toolsLets 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.
No other account needed.
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.
- You MUST load skill
statistical-testingto run the preregistered statistical tests and retain effect uncertainty. - You MUST load skill
verify-reproducibilityto verify the declared reproduction level. If the results must be assembled into claims, evidence, and counterclaims, considerconstruct-argument-map. If several interventions or methods require comparative selection, considerrank-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
| source | criterion | treatment |
|---|---|---|
| resolved v3 entries above | node-specific criteria | retained and specialized to the v4 object contract |
| experiment-execution/statistical-testing | $\alpha$ = 0.05 | fixed value retained where applicable |
| experiment-execution/sample-size-estimation | power = 0.8 | fixed 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