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result-to-claim

Evaluate experimental results against intended claims and obtain Codex verdicts.

Updated Mar 1, 2026
Search Tags:#codex#claims#experiments#evaluate#data-interpretation#research-workflow
One-click install
npx skills add https://github.com/hve4638/hve-cc-marketplace --skill result-to-claim-hve4638
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: result-to-claim
Source: https://github.com/hve4638/hve-cc-marketplace/tree/main/aris/skills/result-to-claim
Command: npx skills add https://github.com/hve4638/hve-cc-marketplace --skill result-to-claim-hve4638

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Codex MCP evaluates results against intended claims and routes to the next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.

Core Features & Use Cases

  • Collect and consolidate results from multiple sources (W&B, EXPERIMENT_LOG.md, EXPERIMENT_TRACKER.md, logs, and design docs) to form a complete evidence map.
  • Submit collected results to Codex for objective judgment on whether the intended claim is supported, partially supported, or not supported.
  • Parse and normalize Codex outputs into structured directives to guide routing, postmortems, or experimental planning.
  • Route based on verdict to pivot to new ideas, supplement with additional experiments, or confirm the claim for publication.

Quick Start

After experiments finish, feed your results to Codex through the pipeline to obtain a verdict and an action plan.

Frequently Asked Questions about result-to-claim

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate experiment results against research claims?▼

To evaluate experiment results against research claims, you submit consolidated data from logs and trackers to Codex for objective judgment, obtaining a structured verdict on whether claims are supported, partially supported, or not supported.

How do I consolidate multi-source experiment data for interpretation?▼

Consolidating multi-source experiment data for interpretation involves collecting and normalizing results from W&B, EXPERIMENT_LOG.md, EXPERIMENT_TRACKER.md, logs, and design docs to form a complete evidence map for objective evaluation.

When do I need to decide whether to pivot, supplement, or confirm an experiment?▼

You need to decide whether to pivot, supplement, or confirm an experiment after experiments finish but before writing the paper or running ablations, using parsed Codex verdicts and supporting evidence to route your next action.

Can I use Codex to generate structured postmortem directives from logs?▼

Yes, you can use Codex to generate structured postmortem directives from logs by parsing and normalizing Codex outputs into actionable routing instructions, postmortem summaries, or experimental planning guidance.

What is the best way to route research workflow actions after data interpretation?▼

The best way to route research workflow actions after data interpretation is to apply the structured Codex verdict, using supporting evidence to programmatically pivot to new ideas, supplement with additional experiments, or confirm the claim for publication.

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