autoresearch-curation

Autoresearch Curation

Use this skill to maintain awesome-autoresearch as a strict, high-signal list of direct autoresearch use cases.

Goal

Keep the repository focused on two questions:

1. Where is autoresearch actually being used in public?
2. Which autoresearch patterns transfer across domains?

This skill is for curation, not broad AI trend collection.

Source of truth

Read these files before making changes:

• README.md
• CONTRIBUTING.md
• every file under categories/

README.md is the homepage aggregate, not the primary editing surface. Update category files first, then refresh README.md from the current category files. If available, use scripts/build-readme.py instead of hand-editing the aggregate.

Hard inclusion rules

Only include items that satisfy at least one of these:

• explicitly mention autoresearch
• explicitly cite Karpathy's autoresearch
• clearly show a modify → verify → keep/discard → repeat loop

And all of these:

• source is public and citable
• description is concrete
• entry stays one sentence
• item is strictly autoresearch-relevant, not a generic research agent

Reject:

• generic agents
• vague AI commentary
• private or uncitable claims
• things that need a paragraph to justify inclusion

Category model

Use main category pages for stronger evidence such as:

• public repos
• project pages
• substantial write-ups
• clear README evidence of the loop

Use categories/related-practices-discussions.md for:

• X threads
• Reddit discussions
• Hacker News discussions
• interviews
• blog mentions

when they show credible real practice signals but do not yet have a strong standalone repo or case page.

Working strategy

1. Search broadly, classify narrowly

Use cross-platform searches, but keep inclusion strict.

Preferred evidence channels:

• GitHub
• X / Twitter
• Reddit
• Hacker News
• independent blogs / write-ups

2. Keep X queries simple

Prefer medium-complexity searches such as:

• autoresearch trading
• autoresearch benchmark
• autoresearch debugging
• Karpathy autoresearch robotics

Avoid very long advanced-search expressions when the adapter is unstable.

3. Chinese + English

Search in both languages when useful.

Useful Chinese patterns:

• autoresearch 回滚
• autoresearch 验证器
• autoresearch benchmark
• Karpathy autoresearch 工程

But keep Chinese queries narrow to avoid noisy generic matches.

Promotion workflow

Use this exact ladder:

1. Discussion lead found
   • Add to categories/related-practices-discussions.md if it is credible and directly autoresearch-related.
2. Evidence chain search
   • Look for repo, README, case page, blog post, or project page.
3. Promotion test
   • Promote only if public evidence clearly shows a real autoresearch loop or explicit autoresearch framing.
4. Promote
   • Move it into the best-fit main category.
5. Deduplicate
   • Remove the weaker discussion-only item if the main case now covers it.
6. Refresh counts
   • Update README.md counts if category totals changed.

Entry-writing rules

Main categories

Format:

- [Name](URL) - Domain: one-sentence description of the autoresearch use case.

Rules:

• one sentence only
• must mention scenario + loop/value
• prefer concrete verbs like applies, adapts, uses, iterates, keeps
• avoid hype

Discussions page

Format:

- [Name or thread title](URL) - Source/platform: one-sentence description of the autoresearch-related practice or discussion.

Rules:

• keep it factual
• describe the practice signal, not your opinion
• if it is mostly about transfer of the pattern, say that clearly

Periodic maintenance loop

When invoked for a recurring sweep:

1. Read the current category files.
2. Search for 3-10 new public leads.
3. Filter aggressively.
4. Add only high-signal entries.
5. Attempt promotion for the strongest discussion leads.
6. Remove duplicates.
7. Recount category totals.
8. Refresh README.md so the homepage aggregate matches the current category files and counts.
9. Summarize:
   • what was added
   • what was promoted
   • what remains discussion-only
   • what needs stronger evidence

Suggested commands

Count entries:

python - <<'PY'
from pathlib import Path
for p in sorted(Path('categories').glob('*.md')):
    cnt=sum(1 for line in p.read_text().splitlines() if line.startswith('- ['))
    print(f'{p}:{cnt}')
PY

Example searches:

bb-browser site twitter/search 'autoresearch benchmark' --json
bb-browser site twitter/search 'autoresearch debugging' --json
bb-browser site twitter/search 'autoresearch robotics' --json
bb-browser site google/search 'site:reddit.com autoresearch real codebase OR autoresearch debugging' | sed -n '1,220p'
bb-browser site google/search 'site:news.ycombinator.com autoresearch OR "Karpathy autoresearch"' | sed -n '1,220p'
bb-browser site google/search 'site:github.com "autoresearch" robotics' | sed -n '1,220p'
opencli gh api repos/<owner>/<repo>/readme

Quality bar

Promote slowly. Add discussions faster.

If evidence is good but not strong enough for a main case, keep it in discussions. Precision beats coverage.

Deliverable checklist

Before finishing, verify:

• entries are one sentence
• no generic agents slipped in
• promoted items have stronger evidence than discussion-only items
• discussions page remains useful as a map of emerging practice
• README homepage aggregate matches the current category files

Recommended invocation phrases

This skill should be used for prompts like:

• "继续搜集 awesome-autoresearch"
• "做一轮 autoresearch 证据巡检"
• "把 discussions 里强条目升格"
• "更新 autoresearch awesome list"
• "定期维护这个仓库"