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result-to-claim

Judges whether experimental results support claims and routes to next steps.

Updated Apr 18, 2026
Search Tags:#automation#workflow#evaluation#decision#claims#experiments#results
One-click install
npx skills add https://github.com/THUFanZd/Sewed_pipeline --skill result-to-claim-thufanzd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: result-to-claim
Source: https://github.com/THUFanZd/Sewed_pipeline/tree/main/.agents/skills/result-to-claim
Command: npx skills add https://github.com/THUFanZd/Sewed_pipeline --skill result-to-claim-thufanzd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the interpretation of experimental results to determine which claims they support, which they do not, and what evidence is still missing.

Core Features & Use Cases

  • Collects results from multiple sources (W&B history, EXPERIMENT_LOG.md, EXPERIMENT_TRACKER.md, log files, and notes) to form a complete evidence set.
  • Performs an objective, secondary evaluation to judge claim support and route to next steps (pivot, supplement, or confirm).
  • Parses and records verdicts, guides post-evaluation actions like postmortems, ablations, or paper writing.

Quick Start

Follow the process to evaluate results and route outcomes to the next action.

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 experimental results to see if they support my claims?▼

To evaluate experimental results and verify claims, you can collect data from sources like wandb and EXPERIMENT_LOG.md, then apply a secondary evaluation to judge claim support and route to pivot, supplement, or confirm actions.

What is the best way to decide next steps after running a full set of experiments?▼

Deciding next steps after experiments involves judging whether results support intended claims and routing outcomes to pivot, supplement, or confirm based on structured verdicts parsed from collected evidence.

Can I use wandb history and markdown logs together to identify unsupported claims?▼

Yes, you can aggregate wandb history, EXPERIMENT_TRACKER.md, log files, and notes to form a complete evidence set, enabling a secondary Codex agent to identify what remains unsupported.

How do I automate post-evaluation actions like ablations or postmortems?▼

Automating post-evaluation actions requires parsing structured verdicts from experiment results to automatically guide subsequent workflows like postmortems, ablations, or paper writing.

Why does my experiment evaluation route to pivot instead of confirm?▼

Experiment evaluation routes to pivot instead of confirm when the collected results fail to support the intended claims, requiring a structured verdict to redirect the workflow.

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