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Eval Autoresearch Fit

Category: Science & Research  ·  Sub-category: research-methods  ·  Last updated:
Trigger with "evaluate autoresearch fit", "score this skill for karpathy loop", "is this a good autoresearch candidate", "assess autoresearch viability for", "which skills are best for autonomous loop optimization", "score skills for 3-file architecture", or when the user wants to determine if a skill is a good candidate for applying the Karpathy autoresearch autonomous optimization loop pattern.
Security AStatic scan found no risk patternsHow grading works ›

About this skill (catalog notes)

Eval Autoresearch Fit includes pricing or quota commentary; 7 code blocks for direct copy-paste. The SKILL.md runs to about 1,409 words, in the catalog's typical mid-range.

Original author
richfrem
Indexed lastmod
Catalog position
Science & Research · research-methods
Indexed related skills
10

How Eval Autoresearch Fit fits the catalog

Eval Autoresearch Fit sits in the Science & Research category under the research-methods sub-topic in the ClaudSkills catalog. There are 10 related skills indexed alongside it; comparing a few before installing usually reveals which fits your workflow best.

These notes are auto-generated from features detected in the SKILL.md file and from this catalog's structure — they aren't part of the source repository.

From the source SKILL.md

Assess whether a skill is a viable candidate for the Karpathy 3-File Autoresearch autonomous optimization loop. Scores each skill on four dimensions, proposes what the 3-file architecture would look like, and updates the canonical summary-ranked-skills.json via the update script.

What this skill does

Eval Autoresearch Fit is a community-contributed Claude Code skill in the research-methods sub-category. It ships as a SKILL.md file that Claude Code auto-discovers under ~/.claude/skills/eval-autoresearch-fit/ and loads when your prompt matches the skill's trigger.

Who uses this skill

The Eval Autoresearch Fit Claude Code skill is built for researchers, data scientists, academics, and analysts working with complex data and scientific literature. It's part of ClaudSkills (also referred to as Claude Skills or Claude Code Skills) — the open community-curated registry of 115,000+ SKILL.md files for Anthropic's Claude Code agent and the wider Claude ecosystem (Claude API, Claude Agent SDK).

How to install

Free

Manual install (2 steps)

mkdir -p ~/.claude/skills/eval-autoresearch-fit
curl -L https://claudskills.com/skills/eval-autoresearch-fit/SKILL.md \
  -o ~/.claude/skills/eval-autoresearch-fit/SKILL.md

Or just download SKILL.md directly and drop it into ~/.claude/skills/eval-autoresearch-fit/. Claude Code auto-discovers it on next session.

Skills live at ~/.claude/skills/eval-autoresearch-fit/SKILL.md on macOS/Linux, or %USERPROFILE%\.claude\skills\eval-autoresearch-fit\SKILL.md on Windows. See the full install guide for step-by-step instructions.

Telegram

📱 Install from your phone or desktop Telegram

Open @claudskills_bot on Telegram, tap Open Desktop App, and the desktop app installs this skill for you. Or share the bot link with a colleague — they get the same one-tap install. Learn more →

Pro

One-click install via the desktop app

The ClaudSkills desktop app installs any skill directly into ~/.claude/skills/ with one click — no terminal required. Pro starts at $9/mo or $149 lifetime.

Pro

For the full experience including quality scoring and one-click install features for each skill — upgrade to Pro.

Frequently asked questions

How do I install the Eval Autoresearch Fit Claude Code skill?
Install via the ClaudSkills desktop app (one click) or copy SKILL.md from the source repository to ~/.claude/skills/eval-autoresearch-fit/SKILL.md and restart Claude Code. Both flows are detailed at claudskills.com/install/.
What does the Eval Autoresearch Fit skill do?
Trigger with "evaluate autoresearch fit", "score this skill for karpathy loop", "is this a good autoresearch candidate", "assess autoresearch viability for", "which skills are best for autonomous loop optimization", "score skills for 3-file architecture", or when the user wants to determine if a skill is a good candidate for applying the Karpathy autoresearch autonomous optimization loop pattern.
Is this skill free to install?
Yes. ClaudSkills is an open registry — every skill keeps its source repository's license, and manual install via copy is free. ClaudSkills Pro ($9/mo, $79/yr, or $149 one-time) adds one-click install via the desktop app and a multi-signal Quality Score.
When should I use the Eval Autoresearch Fit skill?
Use Eval Autoresearch Fit when your Claude Code task falls under the Science & Research category — specifically in the research methods area. Claude Code auto-discovers installed skills and invokes the right one based on the task description, so you can also ask Claude directly (e.g. "use Eval Autoresearch Fit" or describe the task and let Claude pick). Browse related skills at /category/science/.
What is a Claude Code skill and how does the Eval Autoresearch Fit skill fit in?
A Claude Code skill is a SKILL.md file that lives under ~/.claude/skills/<name>/ and tells the Claude Code CLI agent how to perform a specific task (instructions, prompts, allowed tools). Skills are auto-discovered at session start. Eval Autoresearch Fit is one of 67,000+ skills indexed in the open ClaudSkills catalog, classified under the Science & Research category. Learn more at /learn/what-is-a-claude-skill/.

Attribution & license

Cite this skill

If you reference this skill in a blog post, paper, or documentation, you can cite it as:

APA
richfrem. (2026). Eval Autoresearch Fit [Claude Code skill]. ClaudSkills. https://claudskills.com/skills/eval-autoresearch-fit/
BibTeX
@misc{eval-autoresearch-fit-2026,
  author    = {richfrem},
  title     = {Eval Autoresearch Fit [Claude Code skill]},
  year      = {2026},
  publisher = {ClaudSkills},
  url       = {https://claudskills.com/skills/eval-autoresearch-fit/}
}

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Security scan

Grade A · scanned 2026-06-13 — free static scan against the OWASP Agentic Skills Top 10.

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