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experiment-results-planning

Design experiment protocols and traceability gates for research papers before final metrics exist.

Updated May 15, 2026
Search Tags:#experiment design#peer review#research writing#table schema#data manifest#results planning#mock data governance
One-click install
npx skills add https://github.com/lth0/codexSkill --skill experiment-results-planning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-results-planning
Source: https://github.com/lth0/codexSkill/tree/main/skills/agents/research-writing/experiment-results-planning
Command: npx skills add https://github.com/lth0/codexSkill --skill experiment-results-planning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents weak, untraceable, or prematurely written Results/Discussion sections by forcing an experiment plan, tables/figures data contracts, and traceability from contributions to evidence.

Core Features & Use Cases

  • Experiment protocol design: Define dataset/splits, fair baselines, metrics (including imbalance handling), main comparisons, efficiency evaluation, and ablations aligned to each claimed module.
  • Traceability gates for claims: Ensure every Introduction contribution maps to at least one experiment, limitation note, or future-work boundary and stays traceable through tables/figures.
  • Mock data containment: Allow synthetic or mock values only for planning artifacts (with strict naming and explicit replacement placeholders), never as real evidence.
  • Table/figure handoff preparation: Produce table schema and a data manifest and specify a figure-generation pipeline so results prose can later be updated cleanly.

Quick Start

Use the experiment-results-planning skill to create the planning files and gates for your paper’s Results section before you finalize any real metrics.

Frequently Asked Questions about experiment-results-planning

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

FAQPage Schema
How do I plan experiment tables and figures before final metrics exist?▼

Plan experiment tables and figures before final metrics exist by defining strict table schemas, generating a data manifest, and specifying a figure-generation pipeline so results prose can be updated cleanly later.

What is the best way to ensure traceability from research contributions to evidence?▼

Ensuring traceability from research contributions to evidence requires locked protocol gates mapping every Introduction contribution to at least one experiment, limitation note, or future-work boundary through specified tables and figures.

How do I design an experiment protocol with fair baselines and ablations?▼

Design an experiment protocol with fair baselines and ablations by defining dataset splits, metrics with imbalance handling, main comparisons, efficiency evaluation, and ablations aligned to each claimed module before writing results.

Can I use mock data for planning artifacts in research writing?▼

You can use mock data for planning artifacts in research writing under strict containment rules, applying explicit naming and replacement placeholders, but never as real evidence in final Results or Discussion sections.

How do I prevent unverified claims in my Results and Discussion sections?▼

Prevent unverified claims in Results and Discussion sections by applying decontamination rules that block prematurely written prose and enforce required plan, review, table, and figure handoffs before finalizing metrics.

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