
Agent Reach
Run LinkedIn people, company, and job research plus GitHub repo and PR workflows from the agent using mcporter and gh CLI patterns.
Overview
Agent Reach is an agent skill most often used in Idea research (also Grow lifecycle and Operate iterate) that wires LinkedIn discovery and GitHub gh CLI commands for hiring and repository intelligence.
Install
npx skills add https://github.com/panniantong/agent-reach --skill agent-reachWhat is this skill?
- LinkedIn profile, people search, company profile, and job search via mcporter linkedin-scraper calls
- Documented login requirement for LinkedIn MCP sessions
- Jina Reader curl fallback when LinkedIn MCP is unavailable
- GitHub gh CLI recipes for search, repos, issues, PRs, Actions runs, and workflows
- Bilingual operational notes bridging Chinese section headers and universal CLI commands
Adoption & trust: 5.7k installs on skills.sh; 23.5k GitHub stars; 1/3 security scanners passed (skills.sh audits).
What problem does it solve?
You need repeatable agent-callable commands to research people, companies, jobs, and GitHub activity without manually copying browser workflows.
Who is it for?
Founders doing first hires, contractor scouting, or open-source due diligence who already use gh and can supply LinkedIn login for MCP.
Skip if: Builders who cannot accept LinkedIn ToS or credential risk, or who only need in-repo git without GitHub cloud APIs.
When should I use this skill?
User needs LinkedIn profile, people, company, or job data via agent tools, or GitHub search, repo, issue, PR, and Actions operations via gh CLI.
What do I get? / Deliverables
You can invoke documented mcporter LinkedIn calls or gh CLI sequences—or Jina fallback—and return structured hiring and repo intelligence to your agent thread.
- LinkedIn profile or search results
- GitHub repo, issue, or PR intelligence
- Fallback fetched page text via Jina Reader
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Idea/research is the canonical shelf because primary flows are discovering people, companies, roles, and open-source signals before you commit to hire or partner. Research subphase covers talent mapping, competitor hiring signals, and repository discovery rather than day-to-day CI operations.
Where it fits
Search LinkedIn for AI engineers and map companies posting similar roles before defining your first hire.
Pull competitor company profiles and cross-check their public GitHub org activity.
Verify whether an OSS dependency is actively maintained via gh issue and PR velocity.
Track open requisitions and candidate pipelines while scaling a two-person team.
Inspect failed GitHub Actions on a forked integration repo before adopting it.
How it compares
Skill-delivered CLI and MCP recipes, not a compliant official LinkedIn API integration product.
Common Questions / FAQ
Who is agent-reach for?
Solo founders and indie teams using coding agents to research candidates, companies, job markets, and GitHub projects from the terminal.
When should I use agent-reach?
During Idea research for talent and competitor hiring signals, in Validate when checking who maintains critical repos, and in Grow or Operate when tracking hires or upstream OSS health.
Is agent-reach safe to install?
It implies LinkedIn login and network scraping—read the Security Audits panel on this page and treat credentials and ToS compliance as your responsibility.
SKILL.md
READMESKILL.md - Agent Reach
# 职场招聘 LinkedIn。 ## LinkedIn ```bash # 获取个人资料 mcporter call 'linkedin-scraper.get_person_profile(linkedin_url: "https://linkedin.com/in/username")' # 搜索人才 mcporter call 'linkedin-scraper.search_people(keyword: "AI engineer", limit: 10)' # 获取公司资料 mcporter call 'linkedin-scraper.get_company_profile(linkedin_url: "https://linkedin.com/company/xxx")' # 搜索职位 mcporter call 'linkedin-scraper.search_jobs(keyword: "software engineer", limit: 10)' ``` > **需要登录**: LinkedIn scraper 需要有效的登录态。 ### Fallback 方案 如果 MCP 不可用,可以用 Jina Reader: ```bash curl -s "https://r.jina.ai/https://linkedin.com/in/username" ``` # 开发工具 GitHub CLI ## GitHub (gh CLI) GitHub 官方命令行工具,用于仓库、Issue、PR、Actions、Release 以及 API 访问。 ```bash # 认证 gh auth login gh auth status # 搜索 gh search repos "query" --sort stars --limit 10 gh search code "query" --language python # 仓库 gh repo view owner/repo gh repo clone owner/repo gh repo create my-repo --private gh repo fork owner/repo gh repo fork owner/repo --clone gh repo sync owner/repo # Issues gh issue list -R owner/repo --state open gh issue view 123 -R owner/repo gh issue create -R owner/repo --title "Title" --body "Body" # Pull Requests gh pr list -R owner/repo --state open gh pr view 123 -R owner/repo gh pr create -R owner/repo --title "Title" --body "Body" gh pr checks 123 --repo owner/repo # Actions / CI gh run list --repo owner/repo --limit 10 gh run view <run-id> --repo owner/repo gh run view <run-id> --repo owner/repo --log-failed gh workflow list --repo owner/repo # Releases gh release list -R owner/repo gh release create v1.0.0 # API gh api /user gh api repos/owner/repo # JSON 输出 gh issue list --repo owner/repo --json number,title --jq '.[] | "\(.number): \(.title)"' ``` ## 选择指南 | 工具 | 来源 | 用途 | |-----|------|------| | gh CLI | agent-reach | Git 操作 | | zread | my-mcp-tools | 读仓库内容 | | context7 | my-mcp-tools | 查技术文档 | # 搜索工具 Exa AI 搜索引擎。 ## Exa AI 搜索 高质量 AI 搜索引擎,擅长技术和代码搜索。 ```bash mcporter call 'exa.web_search_exa(query: "query", numResults: 5)' mcporter call 'exa.get_code_context_exa(query: "code question", tokensNum: 3000)' ``` ### 使用场景 | 场景 | 参数 | |-----|------| | 网页搜索 | `web_search_exa(query: "...", numResults: 5)` | | 代码搜索 | `get_code_context_exa(query: "...", tokensNum: 3000)` | ### 特点 - 擅长英文内容和技术文档 - 支持代码上下文搜索 - 结果质量高 ## 与其他搜索工具对比 | 工具 | 来源 | 适用场景 | |-----|------|---------| | Exa | agent-reach | 英文/技术/代码搜索 | | 智谱搜索 | my-mcp-tools | 中文搜索 | | GitHub 搜索 | agent-reach (dev.md) | 仓库/代码搜索 | # 社交媒体 & 社区 小红书、抖音、Twitter/X、微博、B站、V2EX、Reddit。 ## 小红书 / XiaoHongShu (xhs-cli) ### 稳定可用的命令 ```bash # 搜索笔记(推荐入口) xhs search "query" # 阅读笔记详情(必须用搜索结果中的 URL 或 ID,不能裸 note_id) xhs read NOTE_ID_OR_URL # 查看评论 xhs comments NOTE_ID_OR_URL # 浏览热门 xhs hot # 推荐 feed xhs feed ``` ### 已知不稳定的命令(v0.6.4) ```bash # 以下命令当前可能返回 API error,谨慎使用: xhs user USER_ID # 可能返回 {code: -1} xhs user-posts USER_ID # 可能返回 {code: -1} xhs favorites # 可能返回 API error ``` ### 重要注意事项 > **安装**: `pipx install xiaohongshu-cli`,然后 `xhs login`(自动从浏览器提取 Cookie)。 > > **xsec_token 限制**: 小红书强制 xsec_token 机制,**不能直接用裸 note_id 去读**。正确流程是:先 `xhs search` 或 `xhs feed` 获取结果,再用结果中的 URL/ID 去 `xhs read`。直接构造 note_id 会被拦截。 > > **频率控制**: 高频请求(批量搜索、深翻评论)会触发验证码,这是平台限制无法绕过。建议每次操作间隔 2-3 秒。 > > **POST 操作风险**: 发帖(post)、评论(comment)、点赞(like) 等写操作在 v0.6.x 可能因签名问题返回 406。如需使用,建议降级到 v0.3.5 (`pipx install xiaohongshu-cli==0.3.5`)。 ## 抖音 / Douyin ### 安装与配置 `douyin-mcp-server` 是 **stdio 模式**的 MCP server,需先安装再注册到 mcporter: ```bash # 1. 安装 pipx install douyin-mcp-server # 2. 查找安装路径 pipx runpip douyin-mcp-server show -f 2>/dev/null | grep "Location" \ || find ~/.local -name "douyin-mcp-server" 2>/dev/null | head -1 # 3. 注册到 mcporter(使用 stdio 模式,将路径替换为上一步的输出) mcporter config add douyin --command "/path/to/douyin-mcp-server" --scope home ``` > **注意**:`agent-reach install --channels douyin` 暂不支持抖音渠道(抖音在"可选渠道待解锁"列表)。 > HTTP 模式(`mcporter config add douyin http://localhost:18070/mcp`)**无法正常工作**,请使用上方 stdio 方式。 ### 用法 ```bash