daily-papers-fetch: Workflow Automation for AI Agents | OpenAgentSkill

Overview

论文抓取（3 步流水线的第 1 步）。抓取 arXiv + HuggingFace 最新论文，打分筛选，富化信息，输出到 /tmp/daily_papers_enriched.json 供后续 skill 使用。触发词："论文抓取"、"跑一下论文抓取" 支持多天模式："过去3天论文推荐"、"过去一周论文推荐"、"过去一周的论文"、"抓 3 天的论文"、"最近5天"

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开始前: 先说一声 "开始抓取论文 🐕" 并告知今天日期。如果是多天模式，告知抓取范围。

论文抓取 (Fetch + Score + Enrich)

你是用户的论文抓取系统（3 步流水线的第 1 步）。抓取最新论文 → 打分筛选 → 富化信息 → 保存到临时文件。

Step 0: 读取共享配置

先读取 ../_shared/user-config.json，如果 ../_shared/user-config.local.json 存在，再用它覆盖默认值。

显式生成并在后续统一使用这些变量：

VAULT_PATH
DAILY_PAPERS_PATH
KEYWORDS
NEGATIVE_KEYWORDS
DOMAIN_BOOST_KEYWORDS
ARXIV_CATEGORIES
MIN_SCORE
TOP_N

其中：

DAILY_PAPERS_PATH = {VAULT_PATH}/{daily_papers_folder}
所有关键词、分类、阈值都以共享配置为准

后续统一以共享配置和上面的变量为准。

解析天数

从用户输入中解析 --days N 参数。匹配规则：

"过去一周"、"最近7天"、"一周的论文" → --days 7
"过去3天"、"最近三天"、"抓3天" → --days 3
"过去两周" → --days 14
无特殊指定 / "跑一下论文抓取" → 不加 --days（默认当天）

将解析出的天数存为变量 DAYS_ARG，在后续脚本调用中使用。

配置来源

默认配置在 ../_shared/user-config.json
个人覆盖配置放在 ../_shared/user-config.local.json
如果两者都存在，以 local 为准

工作流程

Phase 1+2: 抓取 + 打分 + 合并去重（纯 Python 脚本）

用 fetch_and_score.py 一步完成 HF + arXiv 抓取、打分、合并去重、历史去重、选 Top 30。零 token 消耗。

# 默认：当天
python3 ../daily-papers/fetch_and_score.py > /tmp/daily_papers_top30.json

# 多天模式（将 N 替换为解析出的天数）
python3 ../daily-papers/fetch_and_score.py --days N > /tmp/daily_papers_top30.json

根据前面解析的 DAYS_ARG，如果用户指定了天数就加 --days N，否则不加。

脚本自动完成：

并行抓取 HuggingFace Daily + Trending API 和 arXiv API
关键词打分（正向/负向/领域加分/trending 加分）
按 arXiv ID 合并去重
读取 .history.json 跨天去重（含周末模式放宽规则）
不足 20 篇时从历史回填
按 score 降序取 Top 30

进度日志输出到 stderr，JSON 结果输出到 stdout。

检查输出：确认 /tmp/daily_papers_top30.json 存在且包含有效 JSON 数组。如果为空数组或文件不存在，检查 stderr 诊断问题。

Phase 3: 批量富化（enrich_papers.py 脚本）

用 enrich_papers.py 脚本一次性富化所有论文。脚本使用 asyncio + curl 子进程并发请求，纯 regex 解析 HTML，无需 WebFetch。

先把 Phase 2 的 Top 30 结果保存到临时文件，然后运行：

python3 ../daily-papers/enrich_papers.py /tmp/daily_papers_top30.json /tmp/daily_papers_enriched.json

注意：使用两个文件路径参数（输入 + 输出），避免 sandbox 环境下 stdout/stderr 混淆。脚本会把第一个 .json 参数当作输入路径、第二个当作输出路径；如果只传一个 .json 它会被当作输入路径，结果走 stdout。

脚本自动完成以下工作（Semaphore(10) 限制并发，单篇超时 30 秒）：

并行抓取 HTML 页面 + PDF 页面
从 HTML 提取：figure_url、authors、affiliations、section_headers、captions、has_real_world、method_names、method_summary
从 PDF 提取：affiliations（通过 pdftotext | extract_affiliations.py）
如果 HTML authors 为空，fallback 到 abs 页面 <meta> 标签提取 authors/affiliations
合并优先级（脚本内部处理）：
- figure_url: HTML curl
- affiliations: PDF > HTML > abs fallback > Phase 1 data
- authors: HTML > abs fallback > Phase 1 data
- 其他字段: HTML regex 提取

输出格式：与输入相同的 JSON 数组，每篇论文增加以下字段：

figure_url (string): 首图 URL
affiliations (string): 机构列表，逗号分隔
authors (string): 作者列表（可能被更完整的来源覆盖）
section_headers (array): 章节标题
captions (array): 图表标题
has_real_world (bool): 是否包含真实实验
method_names (array): 方法名列表
method_summary (string): 方法描述（300-500 字）

输出

完成后检查 /tmp/daily_papers_enriched.json 存在且包含有效 JSON 数组。告知用户：

抓取了多少篇论文
富化成功多少篇
提示运行下一步：跑一下论文点评

注意事项

Phase 1+2 使用 fetch_and_score.py 脚本，不启动 Task Agent，零 token 消耗
Phase 3 使用 enrich_papers.py 脚本，同样不启动 Task Agent
如果脚本执行失败，检查 stderr 输出诊断问题
如果 arXiv API 抓取失败，脚本自动 fallback 到仅 HuggingFace 源
如果总论文数不足 20 篇，有多少处理多少
周末策略：arXiv 周末不更新，HF daily 周末基本为空，但 HF trending 持续更新。周末主要依赖 trending 来源
不做 git 操作，不生成推荐文件，只输出临时 JSON

View source ↗

File metadata

name: daily-papers-fetch
description: |
  论文抓取（3 步流水线的第 1 步）。抓取 arXiv + HuggingFace 最新论文，打分筛选，富化信息，
  输出到 /tmp/daily_papers_enriched.json 供后续 skill 使用。

  触发词："论文抓取"、"跑一下论文抓取"
  支持多天模式："过去3天论文推荐"、"过去一周论文推荐"、"过去一周的论文"、"抓 3 天的论文"、"最近5天"

View original text

---
name: daily-papers-fetch
description: |
  论文抓取（3 步流水线的第 1 步）。抓取 arXiv + HuggingFace 最新论文，打分筛选，富化信息，
  输出到 /tmp/daily_papers_enriched.json 供后续 skill 使用。

  触发词："论文抓取"、"跑一下论文抓取"
  支持多天模式："过去3天论文推荐"、"过去一周论文推荐"、"过去一周的论文"、"抓 3 天的论文"、"最近5天"
---

> **开始前**: 先说一声 "开始抓取论文 🐕" 并告知今天日期。如果是多天模式，告知抓取范围。

# 论文抓取 (Fetch + Score + Enrich)

你是 用户的论文抓取系统（3 步流水线的第 1 步）。抓取最新论文 → 打分筛选 → 富化信息 → 保存到临时文件。

## Step 0: 读取共享配置

先读取 `../_shared/user-config.json`，如果 `../_shared/user-config.local.json` 存在，再用它覆盖默认值。

显式生成并在后续统一使用这些变量：

- `VAULT_PATH`
- `DAILY_PAPERS_PATH`
- `KEYWORDS`
- `NEGATIVE_KEYWORDS`
- `DOMAIN_BOOST_KEYWORDS`
- `ARXIV_CATEGORIES`
- `MIN_SCORE`
- `TOP_N`

其中：

- `DAILY_PAPERS_PATH = {VAULT_PATH}/{daily_papers_folder}`
- 所有关键词、分类、阈值都以共享配置为准

后续统一以共享配置和上面的变量为准。

## 解析天数

从用户输入中解析 `--days N` 参数。匹配规则：
- "过去一周"、"最近7天"、"一周的论文" → `--days 7`
- "过去3天"、"最近三天"、"抓3天" → `--days 3`
- "过去两周" → `--days 14`
- 无特殊指定 / "跑一下论文抓取" → 不加 `--days`（默认当天）

将解析出的天数存为变量 `DAYS_ARG`，在后续脚本调用中使用。

## 配置来源

- 默认配置在 `../_shared/user-config.json`
- 个人覆盖配置放在 `../_shared/user-config.local.json`
- 如果两者都存在，以 `local` 为准

## 工作流程

### Phase 1+2: 抓取 + 打分 + 合并去重（纯 Python 脚本）

用 `fetch_and_score.py` 一步完成 HF + arXiv 抓取、打分、合并去重、历史去重、选 Top 30。**零 token 消耗。**

```bash
# 默认：当天
python3 ../daily-papers/fetch_and_score.py > /tmp/daily_papers_top30.json

# 多天模式（将 N 替换为解析出的天数）
python3 ../daily-papers/fetch_and_score.py --days N > /tmp/daily_papers_top30.json
```

根据前面解析的 `DAYS_ARG`，如果用户指定了天数就加 `--days N`，否则不加。

脚本自动完成：
- 并行抓取 HuggingFace Daily + Trending API 和 arXiv API
- 关键词打分（正向/负向/领域加分/trending 加分）
- 按 arXiv ID 合并去重
- 读取 `.history.json` 跨天去重（含周末模式放宽规则）
- 不足 20 篇时从历史回填
- 按 score 降序取 Top 30

进度日志输出到 stderr，JSON 结果输出到 stdout。

**检查输出**：确认 `/tmp/daily_papers_top30.json` 存在且包含有效 JSON 数组。如果为空数组或文件不存在，检查 stderr 诊断问题。

### Phase 3: 批量富化（enrich_papers.py 脚本）

用 `enrich_papers.py` 脚本一次性富化所有论文。脚本使用 `asyncio` + `curl` 子进程并发请求，纯 regex 解析 HTML，无需 WebFetch。

**先把 Phase 2 的 Top 30 结果保存到临时文件**，然后运行：

```bash
python3 ../daily-papers/enrich_papers.py /tmp/daily_papers_top30.json /tmp/daily_papers_enriched.json
```

注意：使用**两个文件路径参数（输入 + 输出）**，避免 sandbox 环境下 stdout/stderr 混淆。脚本会把第一个 `.json` 参数当作输入路径、第二个当作输出路径；如果只传一个 `.json` 它会被当作输入路径，结果走 stdout。

脚本自动完成以下工作（Semaphore(10) 限制并发，单篇超时 30 秒）：
- 并行抓取 HTML 页面 + PDF 页面
- 从 HTML 提取：figure_url、authors、affiliations、section_headers、captions、has_real_world、method_names、method_summary
- 从 PDF 提取：affiliations（通过 `pdftotext | extract_affiliations.py`）
- 如果 HTML authors 为空，fallback 到 abs 页面 `<meta>` 标签提取 authors/affiliations
- 合并优先级（脚本内部处理）：
  - figure_url: HTML curl
  - affiliations: PDF > HTML > abs fallback > Phase 1 data
  - authors: HTML > abs fallback > Phase 1 data
  - 其他字段: HTML regex 提取

**输出格式**：与输入相同的 JSON 数组，每篇论文增加以下字段：
- `figure_url` (string): 首图 URL
- `affiliations` (string): 机构列表，逗号分隔
- `authors` (string): 作者列表（可能被更完整的来源覆盖）
- `section_headers` (array): 章节标题
- `captions` (array): 图表标题
- `has_real_world` (bool): 是否包含真实实验
- `method_names` (array): 方法名列表
- `method_summary` (string): 方法描述（300-500 字）

## 输出

完成后检查 `/tmp/daily_papers_enriched.json` 存在且包含有效 JSON 数组。告知用户：
- 抓取了多少篇论文
- 富化成功多少篇
- 提示运行下一步：`跑一下论文点评`

## 注意事项

- Phase 1+2 使用 `fetch_and_score.py` 脚本，**不启动 Task Agent**，零 token 消耗
- Phase 3 使用 `enrich_papers.py` 脚本，同样不启动 Task Agent
- 如果脚本执行失败，检查 stderr 输出诊断问题
- 如果 arXiv API 抓取失败，脚本自动 fallback 到仅 HuggingFace 源
- 如果总论文数不足 20 篇，有多少处理多少
- **周末策略**：arXiv 周末不更新，HF daily 周末基本为空，但 HF trending 持续更新。周末主要依赖 trending 来源
- **不做 git 操作**，不生成推荐文件，只输出临时 JSON

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Review before install: Avoid automatic install

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License: Apache-2.0
Version: 1.0.0
Last GitHub push: Aug 23, 2026
Registry updated: Sep 4, 2026
Instruction path: skills/daily-papers-fetch/SKILL.md @ b2f29bb4cae7

Version reported in registry metadata; check source releases before relying on it.

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Trust

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      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "automation",
      "agent-skill"
    ],
    "known_risks": [
      "The skill depends on external scripts (fetch_and_score.py, enrich_papers.py) and shared config files located outside the skill directory. This is acceptable if the repository includes them, but it reduces portability.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 79,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "The skill depends on external scripts (fetch_and_score.py, enrich_papers.py) and shared config files located outside the skill directory. This is acceptable if the repository includes them, but it reduces portability.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 78,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Document processing",
    "maintenance": "16d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The skill depends on external scripts (fetch_and_score.py, enrich_papers.py) and shared config files located outside the skill directory. This is acceptable if the repository includes them, but it reduces portability.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use daily-papers-fetch in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 72/100 Strong shortlist",
      "Audit: 79/100 Needs review",
      "Safety: 39/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "huangkiki-daily-papers-fetch (daily-papers-fetch)",
      "install_command": "npx skills add huangkiki/dailypaper-skills --skill daily-papers-fetch",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "huangkiki-daily-papers-fetch",
      "task": "Use daily-papers-fetch in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/huangkiki-daily-papers-fetch",
    "api": "https://www.openagentskill.com/api/agent/skills/huangkiki-daily-papers-fetch",
    "audit": "https://www.openagentskill.com/skills/huangkiki-daily-papers-fetch/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=huangkiki-daily-papers-fetch&task=Use%20daily-papers-fetch%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20daily-papers-fetch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20daily-papers-fetch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/huangkiki-daily-papers-fetch/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/huangkiki-daily-papers-fetch"
  }
}

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Creator: huangkiki
Source: huangkiki/dailypaper-skills
Indexed by: OpenAgentSkill community index

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