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Autoresearch Paper Discovery

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bosprimigenious
autoresearch-paper-discovery

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。

Overview

Publisherbosprimigenious
Repositoryautoresearch-skills
Skill nameautoresearch-paper-discovery
Stars
57
Forks
6
Bundled files
11
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bosprimigenious on GitHub. Read the source before you install it.

Installation

Install the Autoresearch Paper Discovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bosprimigenious/autoresearch-skills/tree/main/skills/autoresearch-paper-discovery
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bosprimigenious/autoresearch-skills.git /tmp/autoresearch-skills
mkdir -p .claude/skills
cp -r /tmp/autoresearch-skills/skills/autoresearch-paper-discovery .claude/skills/autoresearch-paper-discovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autoresearch Paper Discovery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Autoresearch Paper Discovery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Autoresearch Paper Discovery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Autoresearch Paper Discovery AI skill 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。

Why use Autoresearch Paper Discovery on TypingMind?

Because you install it once and use it with any model. Autoresearch Paper Discovery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Autoresearch Paper Discovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bosprimigenious/autoresearch-skills/tree/main/skills/autoresearch-paper-discovery. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Autoresearch Paper Discovery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Autoresearch Paper Discovery?

As many as you like. As long as a model supports skills, you can use Autoresearch Paper Discovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Autoresearch Paper Discovery AI skill free?

Yes. It is published on GitHub by bosprimigenious under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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