Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AaronCIH/Awesome-AutoSkill-AutoRubric --skill paper-scoutgit clone --depth 1 https://github.com/AaronCIH/Awesome-AutoSkill-AutoRubricWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aaroncih/awesome-autoskill-autorubric/paper-scout)<a href="https://agentmods.dev/skills/aaroncih/awesome-autoskill-autorubric/paper-scout"><img src="https://agentmods.dev/badge/skills/aaroncih/awesome-autoskill-autorubric/paper-scout/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aaroncih/awesome-autoskill-autorubric/paper-scout"><img src="https://agentmods.dev/badge/skills/aaroncih/awesome-autoskill-autorubric/paper-scout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01056 |
| Opus 5 | $0.00036 | $0.00528 |
| Sonnet 5 | $0.00014 | $0.00211 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
paper-scout scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Fetch the arxiv abstract page to get full details (title, authors, date, abstract)
- 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
- 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]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 111 lines · 72 tokens per session scan A 7a05d1678c6e
paper-scout is a skill published in the GitHub repository AaronCIH/Awesome-AutoSkill-AutoRubric (7 stars, last pushed 3mo ago), licensed MIT. It adds 72 tokens to every session and 1,056 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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