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- brycewang-stanford/Auto-Empirical-Research-Skills
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- 2026年4月3日 02:07
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安装方式
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
检查来源文件
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
菜单
默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。
决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill nasa-ads-api命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
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基于 SOC 职业分类
| name | nasa-ads-api |
| description | Search astrophysics and physics literature via NASA ADS bibliographic database |
| metadata | {"openclaw":{"emoji":"🔍","category":"domains","subcategory":"physics","keywords":["astrophysics","observational astronomy","cosmology","high energy physics"],"source":"https://ui.adsabs.harvard.edu/help/api/","requires":{"env":["ADS_API_TOKEN"]}}} |
The NASA Astrophysics Data System (ADS) is a digital library operated by the Smithsonian Astrophysical Observatory under a NASA grant. It is the primary bibliographic database for astronomy and astrophysics, also covering significant portions of physics, geophysics, and related disciplines. ADS indexes over 16 million records and provides access to full-text articles, citations, and usage metrics.
ADS is indispensable for astronomers and physicists. Nearly every paper in astrophysics is indexed in ADS, and the system provides powerful search capabilities including full-text search, citation and reference tracking, author disambiguation, and object-level queries (search by astronomical object name). The database integrates with SIMBAD, NED, and other astronomical databases to link publications to the celestial objects they study.
The ADS API requires a free API token and supports 3,000 requests per day. It returns JSON and supports a rich query language with field-specific searches, boolean operators, and positional queries.
Authentication is required via a free API token. Register at https://ui.adsabs.harvard.edu/user/settings/token to generate your token. Include it in every request as a header:
Authorization: Bearer YOUR_ADS_API_TOKEN
Tokens do not expire but can be regenerated from the settings page. Each token is associated with a user account and is subject to per-account rate limits.
GET https://api.adsabs.harvard.edu/v1/search/query| Param | Type | Required | Description |
|---|---|---|---|
| q | string | Yes | Search query (ADS query syntax with field qualifiers) |
| fl | string | No | Fields to return (comma-separated: title, author, year, bibcode, doi, citation_count, abstract, etc.) |
| rows | integer | No | Results per page (default: 10, max: 2000) |
| start | integer | No | Pagination offset (default: 0) |
| sort | string | No | Sort field and order (e.g., citation_count desc, date desc) |
| fq | string | No | Filter queries for faceting (e.g., database:astronomy) |
curl -H "Authorization: Bearer YOUR_TOKEN" \
"https://api.adsabs.harvard.edu/v1/search/query?q=gravitational+waves&fl=title,author,year,bibcode,citation_count,doi&rows=10&sort=citation_count+desc"
response.numFound (total hits) and response.docs array. Each doc contains the requested fields. The bibcode is the unique ADS identifier (19-character string encoding journal, year, volume, and page).GET https://api.adsabs.harvard.edu/v1/search/query| Param | Type | Required | Description |
|---|---|---|---|
| q | string | Yes | Author query using author: or first_author: fields |
| fl | string | No | Fields to return |
| rows | integer | No | Results per page |
| sort | string | No | Sort order |
curl -H "Authorization: Bearer YOUR_TOKEN" \
"https://api.adsabs.harvard.edu/v1/search/query?q=author:%22Hawking,+S%22&fl=title,year,bibcode,citation_count&rows=20&sort=citation_count+desc"
GET https://api.adsabs.harvard.edu/v1/search/query| Param | Type | Required | Description |
|---|---|---|---|
| q | string | Yes | Object query using object: field (e.g., object:"M31") |
| fl | string | No | Fields to return |
| rows | integer | No | Results per page |
curl -H "Authorization: Bearer YOUR_TOKEN" \
"https://api.adsabs.harvard.edu/v1/search/query?q=object:%22Sgr+A*%22&fl=title,author,year,bibcode,citation_count&rows=10&sort=date+desc"
POST https://api.adsabs.harvard.edu/v1/metrics| Param | Type | Required | Description |
|---|---|---|---|
| bibcodes | array | Yes | Array of ADS bibcodes (JSON body) |
| types | array | No | Metric types: basic, citations, indicators, histograms |
curl -X POST -H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
"https://api.adsabs.harvard.edu/v1/metrics" \
-d '{"bibcodes": ["2016PhRvL.116f1102A"], "types": ["basic", "citations", "indicators"]}'
The API allows 3,000 requests per day (resets at midnight UTC) and 15 requests per second burst limit. If limits are exceeded, the API returns HTTP 429 with X-RateLimit-Reset header. For large bibliometric analyses, use the ADS bulk export or the myADS notification system. Monitor usage via X-RateLimit-Remaining response headers.
Search for recent highly-cited papers on a topic:
curl -H "Authorization: Bearer YOUR_TOKEN" \
"https://api.adsabs.harvard.edu/v1/search/query?q=dark+energy+AND+year:2023-2026&fl=title,author,year,bibcode,citation_count,doi,abstract&rows=20&sort=citation_count+desc"
Retrieve complete publication list with metrics:
# Get publications
curl -H "Authorization: Bearer YOUR_TOKEN" \
"https://api.adsabs.harvard.edu/v1/search/query?q=author:%22Perlmutter,+S%22&fl=title,year,bibcode,citation_count&rows=200&sort=date+desc"
# Get aggregate metrics
curl -X POST -H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
"https://api.adsabs.harvard.edu/v1/metrics" \
-d '{"bibcodes": ["1999ApJ...517..565P", "2012ApJ...746...85S"], "types": ["basic", "indicators"]}'
Find papers about a specific object and link to SIMBAD/NED:
curl -H "Authorization: Bearer YOUR_TOKEN" \
"https://api.adsabs.harvard.edu/v1/search/query?q=object:%22Crab+Nebula%22+AND+year:2024-2026&fl=title,author,year,bibcode,doi&rows=10&sort=date+desc"