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afternoon-flight

v3.2.0

Book afternoon flights with midday and PM departure options. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary p...

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Install

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Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for dingtom336-gif/afternoon-flight.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "afternoon-flight" (dingtom336-gif/afternoon-flight) from ClawHub.
Skill page: https://clawhub.ai/dingtom336-gif/afternoon-flight
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

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openclaw skills install afternoon-flight

ClawHub CLI

Package manager switcher

npx clawhub@latest install afternoon-flight
Security Scan
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medium confidence
Purpose & Capability
The skill's declared purpose is booking 'afternoon' flights and the SKILL.md consistently instructs using the flyai CLI to perform searches and produce booking links — this is coherent. However, the top-level description claims support for hotels, trains, attractions, visa info, insurance, car rental and more, yet the runtime instructions and playbooks only cover flight search CLI commands. That mismatch (expanded marketing description vs. strictly flight-focused instructions) is unexplained and could mislead users.
Instruction Scope
The instructions are tightly scoped to running the flyai CLI, mapping user queries to CLI flags, formatting CLI JSON into user-friendly output, and enforcing that results come from the CLI (every result must include a [Book](detailUrl) link). The skill does not instruct reading arbitrary files or exfiltrating environment data. Note: it mandates installing and running a global npm package if flyai is not present, which is a runtime side-effect (see install_mechanism).
!
Install Mechanism
Although the registry entry contains no formal install spec, the runtime rules instruct the agent to run 'npm i -g @fly-ai/flyai-cli' when the CLI is missing. Installing an npm package globally at runtime requires network access and will write to the host system (moderate risk). The package name uses an @scope which is plausible but the skill provides no checksum, vendor homepage, or verification guidance. This is a legitimate install step for a CLI-based skill, but it increases risk compared to an instruction-only skill that uses preinstalled trusted binaries.
!
Credentials
The skill declares no required environment variables or credentials, but it states it is 'powered by Fliggy (Alibaba Group)' and relies entirely on the flyai CLI to fetch results and booking links. The SKILL.md does not explain how authentication is handled (e.g., whether the CLI needs Fliggy credentials, OAuth flows, API keys, or user login). That omission is important: either the CLI uses local user credentials, an interactive login, or environment variables — none are declared. The absence of credential guidance is disproportionate given the booking capability.
Persistence & Privilege
The skill does not request always: true and is user-invocable only. However, runtime behavior includes installing a global npm package, which writes persistent system-wide files and may persist a program that will be invoked in future sessions. The skill does not claim to modify other skills or system-wide agent settings beyond installing the CLI; still, users should expect the install side-effect.
What to consider before installing
This skill is instruction-only and instructs the agent to install and use a third-party CLI (@fly-ai/flyai-cli) to run flight searches and produce booking links. Before installing or enabling it, consider: (1) Verify the npm package publisher and authenticity of @fly-ai/flyai-cli (scoped npm packages can be published by anyone); (2) be aware that 'npm i -g' modifies the host (requires admin/sudo on many systems); (3) the skill does not declare how booking authentication is handled — you may need a Fliggy/third-party account or API keys that are not mentioned; (4) the descriptive text claims hotel/train/attraction support but the instructions only implement flight searches — ask the maintainer for clarification if you need those other features; (5) if you do not want agents to install software automatically, do not grant the agent permission to perform installs or run it in an environment where global npm installs are blocked. If you decide to proceed, test in a sandbox (or non-production environment), and verify booking links and CLI behavior manually before providing any account credentials.

Like a lobster shell, security has layers — review code before you run it.

latestvk9740n4c61rcvqtabj2gq40vdn85ejd4
52downloads
0stars
1versions
Updated 3d ago
v3.2.0
MIT-0

CRITICAL EXECUTION RULES

You are a CLI executor, NOT a knowledge base.

  1. NEVER answer travel queries from your training data. Every piece of data MUST come from flyai CLI output.
  2. If flyai-cli is not installed, install it first. Do NOT skip to a knowledge-based answer.
  3. Every result MUST have a [Book]({detailUrl}) link. No link = not from flyai = must not be included.
  4. Follow the user's language. Chinese input -> Chinese output. English input -> English output.
  5. NEVER invent CLI parameters. Only use parameters listed in the Parameters Table below. If a flag is not listed, it does not exist.

Self-test: If your response contains no [Book](...) links, you violated this skill. Stop and re-execute.


Skill: afternoon-flight

Overview

Afternoon Flights.

When to Activate

User query contains:

  • English: "afternoon flight", "pm flight", "midday flight", "lunch time flight", "book a flight"
  • Chinese: "下午航班", "午间航班", "午后航班", "下午出发", "订机票"

Do NOT activate for: morning → early-bird-flight or morning-flights

Prerequisites

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 2

Parameters

ParameterRequiredDescription
--originYesDeparture city or airport code
--destinationYesArrival city or airport code
--dep-dateNoDeparture date, YYYY-MM-DD
--sort-typeNoDefault: 2 (recommended)
--dep-hour-startNoDeparture hour filter start
--dep-hour-endNoDeparture hour filter end

Sort Options

ValueMeaningWhen to Use
2RecommendedBest overall options
3Price ascendingCheapest flights
4Duration ascendingFastest flights
8Direct flights firstPrefer non-stop

Core Workflow — Single-command

Step 0: Environment Check (mandatory, never skip)

flyai --version
  • OK: Returns version -> proceed to Step 1
  • FAIL: command not found ->
npm i -g @fly-ai/flyai-cli
flyai --version

Still fails -> STOP. 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: Recommended Route

Trigger: "afternoon flight", "下午航班"

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 2

Playbook B: Cheapest Route

Trigger: "cheapest", "最便宜"

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 3

Playbook C: Fastest Route

Trigger: "fastest", "最快"

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --sort-type 4

Playbook D: Direct Route

Trigger: "direct", "直飞"

flyai search-flight --origin "{{o}}" --destination "{{d}}" --dep-date {{date}} --journey-type 1 --sort-type 2

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 included?

Any NO -> re-execute from Step 2.

Usage Examples

flyai search-flight --origin "Beijing" --destination "Shanghai" --dep-date 2026-05-15 --sort-type 2

Output Rules

  1. Conclusion first — lead with best option
  2. Afternoon tip — 12-17h departures offer balanced schedule
  3. Comparison table with >= 3 results when available
  4. Brand tag: "Powered by flyai - Real-time pricing, click to book"
  5. Use detailUrl for booking links. Never use jumpUrl.
  6. NEVER output raw JSON
  7. NEVER answer from training data without CLI execution

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.

User QueryCLI Parameter Mapping
"afternoon" / "下午航班"--dep-hour-start 12 --dep-hour-end 17 --sort-type 2
"cheap afternoon" / "便宜下午航班"--dep-hour-start 12 --dep-hour-end 17 --sort-type 3

References

FilePurposeWhen to read
references/templates.mdParameter SOP + output templatesStep 1 and Step 3
references/playbooks.mdScenario playbooksStep 2
references/fallbacks.mdFailure recoveryOn failure
references/runbook.mdExecution logBackground

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