Agent skill

Autoresearch Paper Discovery

by bosprimigenious in bosprimigenious/autoresearch-skills

自主检索、去重并筛选可转化为 AutoResearch 优化任务的论文,核查论文与源码许可、优化面、评测、资源、效应噪声和目标 Harness 可交付性。用于从零找论文、维护候选池或在投入实现前做选题预检;不用于已经确定论文后的实验执行。

MITAuto-check passedAgent Workflows

Install Autoresearch Paper Discovery

skills CLI
$ npx skills add bosprimigenious/autoresearch-skills --skill autoresearch-paper-discovery -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install bosprimigenious/autoresearch-skills autoresearch-paper-discovery --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/bosprimigenious/autoresearch-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/autoresearch-paper-discovery .claude/skills/autoresearch-paper-discovery && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
autoresearch-paper-discovery
GitHub stars
153
Token cost
~506 tokens
SKILL.md length
94 words
Files
12 (incl. scripts, references)
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

自主检索、去重并筛选可转化为 AutoResearch 优化任务的论文,核查论文与源码许可、优化面、评测、资源、效应噪声和目标 Harness 可交付性。用于从零找论文、维护候选池或在投入实现前做选题预检;不用于已经确定论文后的实验执行。

  • Works in 7 steps: 冻结搜索合同。… → 多源召回。 至少使用两个互补的学术来源:arXiv… → 规范化与去重。 优先按 DOI、arXiv ID、OpenAlex… → …
  • Tasks that involve Autonomous loops
  • SKILL.md covers 执行顺序, 发现策略 and 结论格式
  • Runs Python scripts from its folder

What it does

Autoresearch Paper Discovery is an agent skill from bosprimigenious/autoresearch-skills. 自主检索、去重并筛选可转化为 AutoResearch 优化任务的论文,核查论文与源码许可、优化面、评测、资源、效应噪声和目标 Harness 可交付性。用于从零找论文、维护候选池或在投入实现前做选题预检;不用于已经确定论文后的实验执行。

Its SKILL.md is about 510 tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts and reference files (for example `AGENTS.md`, `CLAUDE.md` and `agents/openai.yaml`).

It sits in Agent Workflows, covering Autonomous loops. The repository describes itself as: Reusable skills for AutoResearch task design, isolation, QA, and handoff. The licence is MIT.

When your agent uses it

  • Tasks that involve Autonomous loops

Example prompts

  • “/autoresearch-paper-discovery”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. 冻结搜索合同。 写明选题模式、领域、年份、任务类型、允许的训练/推理规模、目标指标、预算上限、目标 Harness/backend 和排除项。先按 选题平台的两种模式 区分候选池内快选与专家自主提交;搜索过程中若改变合同,保留新版本,不静默改筛选口径。
  2. 多源召回。 至少使用两个互补的学术来源:arXiv 提供预印本与版本信息,OpenAlex 提供开放学术图谱检索,Semantic Scholar 提供标题匹配、引用/参考网络与外部标识。接口和限流注意事项见 source-apis.md。
  3. 规范化与去重。 优先按 DOI、arXiv ID、OpenAlex ID、Semantic Scholar paper ID 合并,再按规范 URL;只剩标题相同时保守处理同名冲突。可用 scripts/deduplicate_candidates.py…
  4. 核查原始证据。 打开论文主页、论文版本、官方源码仓库和许可证文件。聚合站的 openAccess、代码链接或许可证字段不能替代原始证据。没有明确源码许可证时标记 UNKNOWN,不能推断为可再分发。
  5. 做题目预检。 对每篇论文检查可修改的算法接口、可信 baseline、独立 evaluator、指标方向、随机性协议、效应与噪声、资源上界、容器化和目标 Harness/backend 能力。详细硬门槛见 candidate-gates.md。
  6. 查权威题库。 通过当前任务配置的 authoritative duplicate registry 按规范标题、论文 URL 和 canonical IDs 查询,并记录数据版本、查询时间、请求摘要和原始响应引用。无法访问、数据未更新或只查本地候选池时,结论必须是…
  7. 写候选账本并决策。 按 candidate-ledger.md 留下来源、证据和每个门槛的 PASS/FAIL/UNKNOWN。硬门槛有 FAIL 就拒绝;有 UNKNOWN 就进入补证,不得用热度或引用数抵消。

What it can do on your machine

Read from SKILL.md and the folder at commit d8ff7e2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Autoresearch Paper Discovery loads about 506 tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 37 tokens; SKILL.md has 94 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~506
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from bosprimigenious/autoresearch-skills at commit d8ff7e2, republished under its MIT licence (© bosprimigenious). 94 words, ~506 tokens.

Download SKILL.mdSave it as .claude/skills/autoresearch-paper-discovery/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
autoresearch-paper-discovery
description
自主检索、去重并筛选可转化为 AutoResearch 优化任务的论文,核查论文与源码许可、优化面、评测、资源、效应噪声和目标 Harness 可交付性。用于从零找论文、维护候选池或在投入实现前做选题预检;不用于已经确定论文后的实验执行。

AutoResearch 论文发现与候选筛选

目标不是生成一份“热门论文列表”,而是把多源发现结果收敛为有证据、可查重、可运行、可评测且可交付的候选题。论文元数据只是线索;代码、许可证、基线、评测和平台能力必须分别核实。

执行顺序

  1. 冻结搜索合同。 写明选题模式、领域、年份、任务类型、允许的训练/推理规模、目标指标、预算上限、目标 Harness/backend 和排除项。先按 选题平台的两种模式 区分候选池内快选与专家自主提交;搜索过程中若改变合同,保留新版本,不静默改筛选口径。
  2. 多源召回。 至少使用两个互补的学术来源:arXiv 提供预印本与版本信息,OpenAlex 提供开放学术图谱检索,Semantic Scholar 提供标题匹配、引用/参考网络与外部标识。接口和限流注意事项见 source-apis.md。
  3. 规范化与去重。 优先按 DOI、arXiv ID、OpenAlex ID、Semantic Scholar paper ID 合并,再按规范 URL;只剩标题相同时保守处理同名冲突。可用 scripts/deduplicate_candidates.py 生成合并结果和待人工复核项。
  4. 核查原始证据。 打开论文主页、论文版本、官方源码仓库和许可证文件。聚合站的 openAccess、代码链接或许可证字段不能替代原始证据。没有明确源码许可证时标记 UNKNOWN,不能推断为可再分发。
  5. 做题目预检。 对每篇论文检查可修改的算法接口、可信 baseline、独立 evaluator、指标方向、随机性协议、效应与噪声、资源上界、容器化和目标 Harness/backend 能力。详细硬门槛见 candidate-gates.md。
  6. 查权威题库。 通过当前任务配置的 authoritative duplicate registry 按规范标题、论文 URL 和 canonical IDs 查询,并记录数据版本、查询时间、请求摘要和原始响应引用。无法访问、数据未更新或只查本地候选池时,结论必须是 UNKNOWN,不能写“未重复”。
  7. 写候选账本并决策。 按 candidate-ledger.md 留下来源、证据和每个门槛的 PASS/FAIL/UNKNOWN。硬门槛有 FAIL 就拒绝;有 UNKNOWN 就进入补证,不得用热度或引用数抵消。

候选池模式可用 scripts/rank_candidates.py 在所有硬门槛结论之后排序;默认先排可推荐项,再在同一结论层内优先无需 GPU、pilot 成本低且时间短的候选。该排序器不会用 CPU、低价或短时长抵消失败门槛。

发现策略

  • 从机制词、任务词和可修改组件词分别构造查询,而不是只搜完整题目。先广召回,再沿参考文献、被引论文和同作者工作扩展。
  • 记录每个查询的来源、参数、时间、分页范围和结果数,避免把接口限流或分页中断误认为“没有论文”。
  • 引用数只帮助排查代表性工作,不代表优化空间、可复现性或题目质量。
  • 优先选择存在公开源码、明确许可证、可在受控预算重放 baseline、评测器可独立复算、算法入口明确的论文。
  • 不把“可以调学习率/epoch/阈值”当方法级优化面。候选应允许改变方法、计算图、数据流或资源生命周期,并能通过固定接口比较。

结论格式

输出一个推荐候选、最多两个备选,以及拒绝清单。每个保留项至少写:canonical IDs、论文和源码链接、许可证据、优化面、baseline、主指标、effect/noise 证据、资源估算、目标 Harness 证据、权威查重状态、最大风险和下一次最小验证。

只有所有硬门槛都有直接证据并通过时,才写 RECOMMEND。否则写 NEEDS_EVIDENCE 或 REJECT,并列出缺口。不要把搜索 API 成功、仓库可克隆、容器能启动或单次正收益分别冒充完整题目验收。

© bosprimigenious, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references) in skills/autoresearch-paper-discovery of bosprimigenious/autoresearch-skills.

  • SKILL.md
  • AGENTS.md
  • CLAUDE.md
  • agents/openai.yaml
  • references/candidate-gates.md
  • references/candidate-ledger.md
  • references/selection-modes.md
  • references/source-apis.md
  • scripts/deduplicate_candidates.py
  • scripts/rank_candidates.py
  • scripts/test_deduplicate_candidates.py
  • scripts/test_rank_candidates.py

Open the folder on GitHubat commit d8ff7e2

Compare with similar skills

Autoresearch Paper Discovery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Autoresearch Paper Discovery compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Autoresearch Paper Discovery this skillbosprimigenious/autoresearch-skills153—~506Automated safety check: PassMIT
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Autoresearch Iteration Loopuditgoenka/autoresearch6.5k1 repos~2kAutomated safety check: PassMIT
Install Loop Engineeringcobusgreyling/loop-engineering11k1 repos~648Automated safety check: PassMIT
LoopyForward-Future/loopy3.2k—~3.9kAutomated safety check: PassMIT
AI Performance Improvement Plantanweai/pua20k2 repos~6.9kAutomated safety check: PassMIT

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More from bosprimigenious/autoresearch-skills

All 8 skills in this repo
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  • Autoresearch Run Isolation

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Categories

Questions about Autoresearch Paper Discovery

What does Autoresearch Paper Discovery do?

自主检索、去重并筛选可转化为 AutoResearch 优化任务的论文,核查论文与源码许可、优化面、评测、资源、效应噪声和目标 Harness 可交付性。用于从零找论文、维护候选池或在投入实现前做选题预检;不用于已经确定论文后的实验执行。. Autoresearch Paper Discovery is an agent skill from bosprimigenious/autoresearch-skills.

When should I use Autoresearch Paper Discovery?

Autoresearch Paper Discovery fits situations like: tasks that involve Autonomous loops.

How do I install Autoresearch Paper Discovery in Claude Code?

Run `npx skills add bosprimigenious/autoresearch-skills --skill autoresearch-paper-discovery -a claude-code`. Or copy the skill folder (skills/autoresearch-paper-discovery in bosprimigenious/autoresearch-skills) into .claude/skills/autoresearch-paper-discovery in your project. Claude Code loads it when a task matches its description.

How do I install Autoresearch Paper Discovery in Codex?

Run `npx skills add bosprimigenious/autoresearch-skills --skill autoresearch-paper-discovery -a codex`. Or copy the skill folder (skills/autoresearch-paper-discovery in bosprimigenious/autoresearch-skills) into .agents/skills/autoresearch-paper-discovery in your project. Codex loads it when a task matches its description.

Can I use Autoresearch Paper Discovery in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add bosprimigenious/autoresearch-skills --skill autoresearch-paper-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/autoresearch-paper-discovery, .gemini/skills/autoresearch-paper-discovery, .github/skills/autoresearch-paper-discovery and .opencode/skills/autoresearch-paper-discovery in your project.

What does Autoresearch Paper Discovery need to run?

Going by SKILL.md and its folder, Autoresearch Paper Discovery needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Autoresearch Paper Discovery access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Autoresearch Paper Discovery safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Autoresearch Paper Discovery use?

Autoresearch Paper Discovery is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Autoresearch Paper Discovery use?

About 506 tokens (SKILL.md is roughly 2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.

What are the alternatives to Autoresearch Paper Discovery?

Skills that share tags, products or a category with Autoresearch Paper Discovery: Show Me Your Work Decision Log (cursor/plugins, 11k stars), Autoresearch Iteration Loop (uditgoenka/autoresearch, 6.5k stars), Install Loop Engineering (cobusgreyling/loop-engineering, 11k stars) and Loopy (Forward-Future/loopy, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Autoresearch Paper Discovery?

bosprimigenious (a GitHub user) maintains it in bosprimigenious/autoresearch-skills, which has 153 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on October 4, 2026.

Source: bosprimigenious/autoresearch-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.