agentsclimarketplace

Paper scout

Skill AaronCIH/Awesome-AutoSkill-AutoRubric/.github/skills/paper-scout

A curated collection of papers & repos on Auto-Skill (self-evolving agents) and Auto-Rubric (rubric learning from preferences) for LLM alignment & customization.

Install
npx -y skills add AaronCIH/Awesome-AutoSkill-AutoRubric --skill paper-scout

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Daily paper scout for Auto-Skill and Auto-Rubric research. Use when: searching for new papers on self-evolving agents, skill evolution, rubric learning, preference alignment, reward modeling, agentic evolution. Searches arxiv for latest papers, recommends noteworthy ones, and updates the Awesome-AutoSkill-AutoRubric repo README.

SKILL.md

4.0 KB, as published. Nobody here has run it

Paper Scout: Daily Auto-Skill & Auto-Rubric Paper Finder

When to Use

  • Daily check for new papers in the Auto-Skill / Auto-Rubric domain
  • When you want to update the awesome list with recent publications
  • When you want a summary of noteworthy new papers on self-evolving agents or rubric learning

Search Topics

Search arxiv for papers matching these keyword groups. Combine multiple queries to maximize coverage.

Auto-Skill Keywords

  • "self-evolving" agent skill
  • "skill evolution" LLM agent
  • "skill creation" agent
  • "skill library" agent
  • "skill discovery" agent
  • "agentic evolution"
  • "self-improving" agent skill
  • "harness evolution"
  • "skill reuse" LLM

Auto-Rubric Keywords

  • "Auto-Rubric" reward
  • "rubric" "preference" reward LLM
  • "rubric learning" alignment
  • "rubric-based" reward
  • "rubric generation" LLM
  • "criteria" "preference" reward modeling

Procedure

Step 1: Search for New Papers

Search arxiv for recent papers using the keyword groups above. Use the fetch_webpage tool to query:

https://arxiv.org/search/?query=KEYWORDS&searchtype=all&order=-announced_date_first

Run multiple searches across both Auto-Skill and Auto-Rubric keyword groups. Focus on papers from the last 7-14 days.

Step 2: Filter and Evaluate

For each candidate paper found:

  1. Fetch the arxiv abstract page to get full details (title, authors, date, abstract)
  2. Evaluate relevance — must be directly about:
    • Self-evolving agent skills / skill libraries / skill creation / skill evolution, OR
    • Rubric learning from preferences / rubric-based reward modeling / auto-rubric generation
  3. Skip papers that only tangentially mention these topics

Step 3: Present Recommendations

Present findings to the user in this format:

## New Papers Found (Date Range)

### Auto-Skill
1. **Paper Name** (arXiv:XXXX.XXXXX, Date)
   - TL;DR: One-sentence summary
   - Why it's relevant: Brief note

### Auto-Rubric
1. **Paper Name** (arXiv:XXXX.XXXXX, Date)
   - TL;DR: One-sentence summary
   - Why it's relevant: Brief note

### Verdict
- 🔥 Must-add: [list]
- 👀 Worth watching: [list]
- ⏭️ Skip: [list with reasons]

Step 4: Update the Repo (upon user confirmation)

After the user confirms which papers to add:

  1. Read the current README.md at c:\Users\t-ihchen\Downloads\IHChen\Github\Awesome-AutoSkill-AutoRubric\README.md
  2. Determine the correct section for each paper:
    • Auto-Skill > Skill Learning & Evolution: Papers about skill creation, evolution, reuse
    • Auto-Skill > Benchmarks & Evaluation: Benchmarks for skill evaluation
    • Auto-Skill > Surveys & Frameworks: Survey papers, frameworks
    • Auto-Rubric > Rubric Learning from Preference Data: Papers on learning rubrics from preferences
    • Auto-Rubric > Rubric-Guided RL & Reward Modeling: Papers using rubrics for RL/reward
    • Auto-Rubric > Rubric-Based Evaluation & Judges: Papers on rubric-based evaluation
  3. Insert the new entry in chronological order within the section
  4. Follow the table format: | **Name** | Date | [arXiv:ID](link) | [Code](link) or - | TL;DR |
  5. Commit and push:
    git add .
    git commit -m "Add: Paper1, Paper2, ..."
    git push
    

Important Notes

  • Always check if a paper is already in the README before adding
  • Use the paper's v1 submission date for the "Date" column
  • If a paper has a GitHub repo, include it; otherwise use -
  • Note venue acceptance if mentioned (e.g., ICML 2026, ACL 2026)
  • TL;DR should be 1-2 sentences, focusing on the key contribution

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.