Use flyai-cli to call Fliggy MCP services for travel search and booking scenarios.
Works with
All commands output single-line JSON to stdout; errors and hints go to stderr for easy piping with jq or Python.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionflyaiExecute the skills CLI command in your project's root directory to begin installation:
Fetches flyai from alibaba-flyai/flyai-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate flyai. Access via /flyai in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Use flyai-cli to call Fliggy MCP services for travel search and booking scenarios.
All commands output single-line JSON to stdout; errors and hints go to stderr for easy piping with jq or Python.
npm i -g @fly-ai/flyai-cliflyai keyword-search --query "what to do in Sanya" and confirm JSON output.flyai --help.references/ for exact required parameters. Do NOT guess or reuse formats from other commands.The tool can make trial without any API keys. For enhanced results, configure optional APIs:
flyai config set FLYAI_API_KEY "your-key"
date +%Y-%m-%d when precise date context is required.keyword-search): one natural-language query across hotels, flights, attraction tickets, performances, sports events, and cultural activities.
ai-search): Semantic search for hotels, flights, etc. Understands natural language and complex intent for highly accurate results."search-flight): structured flight results for deep comparison.search-hotel): structured hotel results for deep comparison.search-poi): structured attraction results for deep comparison.search-train): structuring train ticket results for deep comparison.search-marriott-hotel): structuring Marriott Group's hotel results for deep comparison.search-marriott-package): structuring Marriott Group's hotel package product results for deep comparison.Detailed command docs live in references/ (one file per subcommand):
| Command | Doc |
|---|---|
keyword-search |
references/keyword-search.md |
ai-search |
references/ai-search.md |
search-hotel |
references/search-hotel.md |
search-flight |
references/search-flight.md |
search-train |
references/search-train.md |
search-poi |
references/search-poi.md |
search-marriott-hotel |
references/search-marriott-hotel.md |
search-marriott-package |
references/search-marriott-package.md |
markdown, with rich text+image presentation. If data contains jumpUrl, you must show a booking link; if data contains picUrl or mainPic, you must show an image; and the image must appear before the booking link., where picUrl comes from returned data.
For
search-hotel, output, wheremainPiccomes from returned data
[Click to book]({jumpUrl}), where jumpUrl comes from returned data.
For
search-flight, output[Click to book]({jumpUrl})Forsearch-hotel, output[Click to book]({detailUrl}), wheredetailUrlcomes from returned data Forsearch-poi, output[Click to book]({jumpUrl})
#, ##, ###), concise bullets, chronological ordering for itinerary items, and emphasis for key facts (date, location, price, constraints).markdown tables for option comparison.[Click to book]({url})keyword-search -> jumpUrlsearch-flight -> jumpUrlsearch-hotel -> detailUrlsearch-poi -> jumpUrlsearch-hotel -> mainPicpicUrl#, ##, ###) and concise bullets.Use this template when returning final results:
.[Click to book]({url}).Always follow the display rules for final user-facing output.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Solid pick for teams standardizing on skills: flyai is focused, and the summary matches what you get after install.
Registry listing for flyai matched our evaluation — installs cleanly and behaves as described in the markdown.
flyai has been reliable in day-to-day use. Documentation quality is above average for community skills.
flyai reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in flyai — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend flyai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
flyai reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend flyai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend flyai for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
flyai reduced setup friction for our internal harness; good balance of opinion and flexibility.
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