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/analyze-experiment-results

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

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill analyze-experiment-results --agent claude-code

How 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/analyze-experiment-results

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

analyze-experiment-results.SKILL.md
name: analyze-experiment-results
description: "Interpret completed experimental outputs after host/runtime execution using pre-declared statistical tests, effect/uncertainty estimates, reproducibility checks, and calibrated synthesis."

analyze-experiment-results

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

| 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.

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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Repo: yogsoth-ai/de-anthropocentric-research-engine