| name | interview-collector |
| description | Collect and curate public AI Agent, LLM, RAG, MCP, and AI application interview experiences for agent-interview-hub. |
Interview Collector
Use this skill when the user asks to collect, summarize, deduplicate, or update AI Agent / LLM / RAG interview experiences.
Rules
- Use only public accessible sources unless the user logs in through their browser.
- Do not request, expose, store, or commit cookies, tokens, or credentials.
- Do not bypass paywalls, login walls, or anti-bot controls.
- Summarize sources; do not copy long external text.
- Preserve source attribution.
Workflow
- Search: 牛客、小红书、知乎、CSDN、博客园、掘金、GitHub、RSS.
- Read public pages with Jina Reader or platform tools.
- Extract structured fields.
- Deduplicate.
- Score quality.
- Write candidates to
data/interviews.json.
- Generate Markdown summaries and run the static site build.
Commands
python3 scripts/collect_interviews.py doctor
python3 scripts/collect_interviews.py search --query "AI Agent 大模型 面经 2026"
agent-reach doctor --json
mcporter call 'exa.web_search_exa(query: "AI Agent 大模型 面经 2026", numResults: 10)'
curl -s "https://r.jina.ai/https://example.com/page"
opencli xiaohongshu search "AI Agent 面经" -f yaml --window background
gh search repos "AI Agent interview" --limit 10 --json fullName,description,stargazersCount,url,updatedAt
python3 -m json.tool data/interviews.json
python3 scripts/build_site.py
Data model
{
"id": "stable-kebab-id",
"platform": "牛客",
"title": "面经标题",
"company": "字节跳动",
"role": "AI Agent开发",
"published_at": "2026-05-20",
"source_url": "https://...",
"source_note_id": null,
"source_lookup": null,
"score": 5,
"topics": ["Agent", "RAG", "MCP"],
"summary": "一到两句摘要。"
}
For Xiaohongshu, avoid unstable direct links in public Markdown. Use source_note_id and source_lookup.
