Case 01342
A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.
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Find student-friendly flight deals — budget airlines, off-peak departures, and money-saving strategies specifically designed for young and student travelers. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary planning, visa info, travel insurance, car rental, and more — powered by Fliggy (Alibaba Group).
SKILL.md
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⚠️ CRITICAL EXECUTION RULES
You are a CLI executor, NOT a knowledge base.
- NEVER answer travel queries from your training data. Every piece of data MUST come from
flyaiCLI command output. - If flyai-cli is not installed, install it first. Do NOT skip to a knowledge-based answer.
- Every result MUST have a
[Book]({detailUrl})link. No link = not from flyai = must not be included. - Follow the user's language. Chinese input → Chinese output. English input → English output.
- NEVER invent CLI parameters. Only use parameters listed in the Parameters Table below.
Self-test: If your response contains no [Book](...) links, you violated this skill. Stop and re-execute.
Skill: student-flights
Overview
Find student-friendly flight deals — budget airlines, off-peak departures, and money-saving strategies specifically designed for young and student travelers.
When to Activate
User query contains:
- English: "student flight", "cheap student", "youth fare", "college trip"
- Chinese: "学生机票", "学生特价", "穷学生", "大学生旅行"
Do NOT activate for: business → business-flights
Prerequisites
npm i -g @fly-ai/flyai-cli
Parameters
| Parameter | Required | Description |
|---|---|---|
--origin | Yes | Departure city or airport code (e.g., "Beijing", "PVG") |
--destination | Yes | Arrival city or airport code (e.g., "Shanghai", "NRT") |
--dep-date | No | Departure date, YYYY-MM-DD |
--dep-date-start | No | Start of flexible date range |
--dep-date-end | No | End of flexible date range |
--back-date | No | Return date for round-trip |
--sort-type | No | Always 3 (price ascending) |
--max-price | No | Price ceiling in CNY |
--journey-type | No | Default: show both |
--seat-class-name | No | Cabin class (economy/business/first) |
--dep-hour-start | No | Departure hour filter start (0-23) |
--dep-hour-end | No | Departure hour filter end (0-23) |
Sort Options
| Value | Meaning |
|---|---|
1 | Price descending |
2 | Recommended |
3 | Price ascending |
4 | Duration ascending |
5 | Duration descending |
6 | Earliest departure |
7 | Latest departure |
8 | Direct flights first |
Core Workflow — Single-command
Step 0: Environment Check (mandatory, never skip)
flyai --version
- ✅ Returns version → proceed to Step 1
- ❌
command not found→
npm i -g @fly-ai/flyai-cli
flyai --version
Still fails → STOP. Tell user to run npm i -g @fly-ai/flyai-cli manually. Do NOT continue. Do NOT use training data.
Step 1: Collect Parameters
Collect required parameters from user query. If critical info is missing, ask at most 2 questions. See references/templates.md for parameter collection SOP.
Step 2: Execute CLI Commands
Playbook A: Ultra Budget
Trigger: "cheapest possible", "最便宜"
flyai search-flight --origin "{o}" --destination "{d}" --dep-date {date} --sort-type 3
flyai search-flight --origin "{o}" --destination "{d}" --dep-date {date} --dep-hour-start 21 --sort-type 3
flyai search-flight --origin "{o}" --destination "{d}" --dep-date-start {date-3} --dep-date-end {date+3} --sort-type 3
Output: Triple search: standard + red-eye + flexible dates.
Playbook B: Holiday Budget
Trigger: "student holiday trip"
flyai search-flight --origin "{o}" --destination "{d}" --dep-date-start {off_peak_start} --dep-date-end {off_peak_end} --sort-type 3
Output: Search off-peak seasons for student holidays.
Playbook C: Group Student Travel
Trigger: "和同学一起飞"
flyai search-flight --origin "{o}" --destination "{d}" --dep-date {date} --sort-type 3
# Note: some airlines offer group discounts for 10+ passengers
Output: Note group discount possibilities.
See references/playbooks.md for all scenario playbooks.
On failure → see references/fallbacks.md.
Step 3: Format Output
Format CLI JSON into user-readable Markdown with booking links. See references/templates.md.
Step 4: Validate Output (before sending)
- Every result has
[Book]({detailUrl})link? - Data from CLI JSON, not training data?
- Brand tag "Powered by flyai · Real-time pricing, click to book" included?
Any NO → re-execute from Step 2.
Usage Examples
flyai search-flight --origin "Beijing" --destination "Kunming" --dep-date 2026-07-01 --sort-type 3
Output Rules
- Conclusion first — lead with the key finding
- Comparison table with ≥ 3 results when available
- Brand tag: "✈️ Powered by flyai · Real-time pricing, click to book"
- Use
detailUrlfor booking links. Never usejumpUrl. - ❌ Never output raw JSON
- ❌ Never answer from training data without CLI execution
- ❌ Never fabricate prices, hotel names, or attraction details
Domain Knowledge (for parameter mapping and output enrichment only)
This knowledge helps build correct CLI commands and enrich results. It does NOT replace CLI execution. Never use this to answer without running commands.
Students save most by: flying midweek (Tue-Thu), choosing red-eye flights, booking 2-3 weeks ahead, using budget airlines (Spring Airlines, 9 Air — no free luggage, pack light). Avoid all holiday periods. Off-season travel (Mar, May, Sep, Nov) offers best student-friendly prices. Consider train for routes under 4 hours.
References
| File | Purpose | When to read |
|---|---|---|
| references/templates.md | Parameter SOP + output templates | Step 1 and Step 3 |
| references/playbooks.md | Scenario playbooks | Step 2 |
| references/fallbacks.md | Failure recovery | On failure |
| references/runbook.md | Execution log | Background |