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result-to-claim

Translate experimental results into structured research claim assessments.

2|1|Updated Apr 19, 2026
Search Tags:#workflow routing#claim verification#multi-agent research#experiment evaluation#paper readiness#literature-grounded evidence#W&B metrics
One-click install
npx skills add https://github.com/raja21068/AutoResearch --skill result-to-claim-raja21068
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: result-to-claim
Source: https://github.com/raja21068/AutoResearch/tree/main/skills/aris/result-to-claim
Command: npx skills add https://github.com/raja21068/AutoResearch --skill result-to-claim-raja21068

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents research from drifting from evidence by deciding which claims experimental results actually support, which they do not, and what evidence is still missing.

Core Features & Use Cases

  • Verdict-driven claim gating: Uses Codex to judge whether results support a specific intended claim as supported, partially supported, or not supported.
  • Evidence collection and normalization: Gathers results from W&B, experiment logs/trackers, raw training logs, and the research contract to provide consistent inputs to the judge.
  • Action routing after judgment: Automatically routes the workflow toward confirm, supplement (additional experiments), or pivot (alternative ideas) and records the outcome for later paper writing.

Quick Start

Use result-to-claim right after your experiment batch finishes to collect metrics, send them to Codex for a verdict, then route the project based on supported/partial/none.

Frequently Asked Questions about result-to-claim

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

FAQPage Schema
How do I verify if my experiment results support a research claim?▼

Claim verification evaluates completed experiment metrics against your intended research statement, assigning a verdict of supported, partially supported, or not supported to prevent evidence drift before paper writing.

What is the best way to identify evidence gaps in ambiguous experiment outcomes?▼

Identifying evidence gaps in ambiguous outcomes involves evaluating execution-grounded metrics to flag missing data, allowing you to revise scoped claims and route follow-up supplementary experiments.

How do I route multi-agent research workflows after an experiment batch finishes?▼

Routing multi-agent research workflows after an experiment batch requires evaluating collected metrics to decide whether to confirm the claim, supplement with additional experiments, or pivot to alternative ideas based on the verdict.

Can I use W&B metrics and Codex evaluation to assess paper readiness?▼

Yes, you can use W&B metrics and Codex evaluation to assess paper readiness by normalizing collected experiment data and project logs to determine if your results are defensible enough for publication.

Do I need an EXPERIMENT_AUDIT integrity check to validate research claims?▼

An EXPERIMENT_AUDIT integrity check is not strictly required but can be optionally applied to validate research claims, ensuring the collected training logs and metrics accurately support your final verdict.

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