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ScavieFae
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experiment-complete

Close AI/ML experiments by updating run cards, PRs, and logs.

Updated Feb 24, 2026
Search Tags:#automation#versioning#documentation#experiment#research-log#run-card#pr-close
One-click install
npx skills add https://github.com/ScavieFae/autonomous-world-model --skill experiment-complete
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-complete
Source: https://github.com/ScavieFae/autonomous-world-model/tree/main/.claude/skills/experiment-complete
Command: npx skills add https://github.com/ScavieFae/autonomous-world-model --skill experiment-complete

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Close experiments reliably by ensuring all records are updated and traceable at completion. This process updates run cards with results, rebuilds the docs index, closes open PRs, logs to the research log, and proposes program.md updates to reflect outcomes.

Core Features & Use Cases

  • Run-card updates: write final results and a decision (Kept/Discarded) into the run card frontmatter.
  • Results section: append a structured ## Results section with metrics and qualitative notes.
  • Docs index refresh: trigger docs_prebuild to update experiments/index.md and run-cards/index.md.
  • PR management: close the associated PR after summarizing outcomes.
  • Research log: append a brief entry to docs/RESEARCH-LOG.md.
  • Program updates: propose changes to program.md when a result changes priorities or baselines.

Quick Start

Provide the experiment ID and results to automatically update the run card, refresh docs, close any open PR, and log the outcome.

Frequently Asked Questions about experiment-complete

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

FAQPage Schema
How do I close an experiment run and update its run card with final results?▼

To close an experiment run, you provide the experiment ID and results so the Skill can write final metrics and a Kept/Discarded decision into the run card frontmatter, append a structured Results section, and close associated PRs.

What is the best way to automate logging outcomes to a research log after an AI training cycle?▼

Automating research log updates involves appending a brief entry to docs/RESEARCH-LOG.md with the experiment outcome, while simultaneously refreshing the docs index and proposing updates to program.md to reflect changed priorities or baselines.

Does closing an experiment automatically rebuild the docs experiments index?▼

Yes, closing an experiment automatically triggers a docs index refresh that updates experiments/index.md and run-cards/index.md, ensuring all documentation reflects the latest completed run and its recorded outcomes.

Can I propose program.md updates automatically when a data science experiment changes baselines?▼

Yes, when a completed experiment changes priorities or baselines, the Skill proposes changes to program.md alongside updating the run card, logging results, and closing the associated pull request to maintain traceability.

What do I need to have set up before I can automate experiment closure for ML training cycles?▼

You need access to docs/run-cards, associated PRs, the docs/experiments index, and docs/RESEARCH-LOG.md, as the Skill requires these to append results, refresh documentation, and propose program.md updates.

Why should I use an automated workflow for closing data science experiments instead of manual tracking?▼

An automated experiment closure workflow ensures reliable traceability by uniformly updating run cards, refreshing docs indexes, closing PRs, and logging outcomes, preventing the gaps and inconsistencies common in manual tracking.

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