Skip to content
cccskills
FreePublished on GitHub

sn-search-code

Search GitHub, Stack Overflow, Hacker News, and HuggingFace for code examples, projects, technical answers, discussions, models, datasets, and Spaces. Filter supported searches and receive structured JSON results containing titles, links, and snippets.

OriginalShow the original description

用于查找代码示例、开源项目、GitHub Issue、技术问答、开发者讨论、HuggingFace 模型/数据集/Space。

See how to install

Good for

  • Find code examples and open-source projects
  • Search technical answers and GitHub Issues
  • Explore developer news and discussions
  • Find AI models, datasets, and Spaces

Files included (7)

  • SKILL.md4.7 KB
  • requirements.txt12 B
  • scripts/github_search.py3.1 KB
  • scripts/hackernews_search.py2.8 KB
  • scripts/huggingface_search.py4.3 KB
  • scripts/search_utils.py4.3 KB
  • scripts/stackoverflow_search.py2.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

sn-search-code - 开发者搜索

凭证配置

API key、token 与 cookie 统一建议写在仓库根目录 .env(参考 .env.example),并由 runtime 或用户在执行前加载为同名环境变量。脚本仍只从环境变量或显式 CLI 参数读取凭证;不要把真实密钥写入 skill payload、报告、日志或提交。

搜索 GitHub、Stack Overflow、Hacker News、HuggingFace 四个开发者核心平台。所有脚本无需 API 密钥 即可使用,但 GitHub --type code 搜索是例外(见下方说明)。

可用脚本

脚本平台用途API 密钥
github_search.pyGitHub仓库、代码、Issue 搜索code 类型必须;其他类型可选(提高限额)
stackoverflow_search.pyStack Overflow技术问答搜索无需
hackernews_search.pyHacker News技术新闻和讨论无需
huggingface_search.pyHuggingFace模型、数据集、Space 搜索可选 HF_TOKEN(提高限额)

依赖

首次运行或脚本提示缺库时,使用本技能的依赖清单安装到当前 Python 环境:

python3 -m pip install -r requirements.txt

不要在脚本内部自动安装依赖。若安装失败、网络不可用或包不可用,停止使用对应脚本并改用网页搜索,说明缺少依赖。

参数说明

github_search.py

python3 scripts/github_search.py <query> [选项]
参数说明默认值
query搜索关键词(必填)—
--limit, -n返回结果数量10
--type, -t搜索类型:repositories, code, issues, repo, issuerepositories
--tokenGitHub Token(也可通过 GITHUB_TOKEN 环境变量设置)—

注意:--type code 必须提供 token。
GitHub API 对代码搜索接口强制要求认证,未提供 token 会返回 401。
repositories 和 issues 类型无需 token,但有 token 可提高速率限制(未认证 10 次/分钟 → 认证 30 次/分钟)。

python3 scripts/github_search.py "machine learning framework" --type repositories --limit 5
python3 scripts/github_search.py "import asyncio" --type code --token ghp_xxx --limit 5
# 或通过环境变量:
GITHUB_TOKEN=ghp_xxx python3 scripts/github_search.py "import asyncio" --type code --limit 5

stackoverflow_search.py

python3 scripts/stackoverflow_search.py <query> [选项]
参数说明默认值
query搜索关键词(必填)—
--limit, -n返回结果数量10
--sort排序方式:relevance, votes, creation, activityrelevance
--tagged按标签过滤,多个用分号分隔(如 python;asyncio)—
--api-keyStack Exchange API 密钥(也可通过 SO_API_KEY 环境变量设置,可选,提高限额)—
python3 scripts/stackoverflow_search.py "python async await" --limit 5
python3 scripts/stackoverflow_search.py "rust lifetime" --sort votes --tagged rust --limit 10

huggingface_search.py

python3 scripts/huggingface_search.py <query> [选项]
参数说明默认值
query搜索关键词(必填)—
--limit, -n返回结果数量10
--type, -t搜索类型:models, datasets, spaces(及别名 model, dataset, space)models
--tokenHuggingFace Token(也可通过 HF_TOKEN 环境变量设置,可选,提高限额)—
python3 scripts/huggingface_search.py "bert" --type models --limit 5
python3 scripts/huggingface_search.py "text classification" --type datasets --limit 5
python3 scripts/huggingface_search.py "stable diffusion" --type spaces --limit 5

hackernews_search.py

python3 scripts/hackernews_search.py <query> [选项]
参数说明默认值
query搜索关键词(必填)—
--limit, -n返回结果数量10
--sort排序方式:relevance, daterelevance
--tagsHN 标签过滤:story, comment, ask_hn, show_hn—
python3 scripts/hackernews_search.py "LLM agents" --limit 10
python3 scripts/hackernews_search.py "GPT-5" --sort date --tags story --limit 5

输出格式

所有脚本输出标准 JSON:

{
  "success": true,
  "query": "...",
  "provider": "github|stackoverflow|hackernews",
  "items": [
    {"title": "...", "url": "...", "snippet": "...", ...}
  ],
  "error": null
}

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Compare counts and shares for two categories in Excel data. The skill cleans the selected columns, calculates differences and percentages, and produces a summary spreadsheet plus bar and pie charts.

  • Comparing counts for two categories
  • Visualizing category shares
  • Cleaning merged-cell data before counting
OpenSenseNova/SenseNova-Skills5.8KUpdated Oct 9, 2026

Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.

  • Summarize Excel records using custom categories
  • Compare category distributions across groups
  • Score and grade text using adjustable rules
OpenSenseNova/SenseNova-Skills5.8KUpdated Oct 9, 2026

Count categories in a chosen data column after removing missing values and placeholder labels. Calculate each category’s share and create high-resolution charts combining counts, percentages, pie charts, and horizontal bars for distribution reports.

  • Compare record counts across categories
  • Show category shares with pie and bar charts
  • Exclude placeholder labels before counting
OpenSenseNova/SenseNova-Skills5.8KUpdated Oct 9, 2026

Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.

  • Compare category distributions across groups
  • Clean category labels and fill merged-cell gaps
  • Export analysis results to Excel
OpenSenseNova/SenseNova-Skills5.8KUpdated Oct 9, 2026

Filter Excel data using candidate column names, RGB color values, text prefixes, and regular expressions, which match text patterns. Calculate totals and averages, then export results to Excel with highlighted rows and a download link.

  • Filtering rows by RGB values or identifier prefixes
  • Converting text to numbers and calculating totals or averages
  • Extracting matching commands with their identifiers and descriptions
OpenSenseNova/SenseNova-Skills5.8KUpdated Oct 9, 2026

Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.

  • Count records containing a specified risk level by work category
  • Calculate group shares among matching records
  • Summarize large datasets already converted to Parquet
OpenSenseNova/SenseNova-Skills5.8KUpdated Oct 9, 2026

See all skills by OpenSenseNova

Report a problem with this skill