hospitality-expert

Other Verified v1.0.0
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Other · hospitality · hotel · reservation
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
98 / 100 · audit passed
Author / version / license
@personamanagmentlayer · v1.0.0 · no license declared
Token usage
Heavy
Setup complexity
Guided setup
External API key
Not required
Operating systems
Unspecified (assume cross-platform)
Runtime requirements
Python
Permissions
  • Read-only
  • Write / modify
  • Shell exec
Network behavior
Local-only
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Output preview hospitality-expert.preview
---
name: hospitality-expert
description: Expert-level hotel management, reservation systems, guest services, revenue management, and hosp…
category: other
runtime: Python
---

# hospitality-expert output preview

## PART A: Task fit
- Use case: Expert-level hotel management, reservation systems, guest services, revenue management, and hospitality technology Expert guidance for hotel management, reservation systems, property management systems (PMS), guest services, revenue management, and hospitality technology solutions. from dataclasses import dataclass from datetime import datetime, timedelta….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Core Concepts / Hotel Management Systems / Technologies” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Expert-level hotel management, reservation systems, guest services, revenue management, and hospitality technology Expert guidance for hotel management, reservation systems, property management systems (PMS), guest services, revenue management, and hospitality technology solutions. from dataclasses import dataclass from datetime import datetime, timedelta…”.
- **02** When the source has headings, the agent prioritizes “Core Concepts / Hotel Management Systems / Technologies” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Expert-level hotel management, reservation systems, guest services, revenue management, and hospitality technology Expert guidan…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Core Concepts”, “Hotel Management Systems”, “Technologies”, “Standards and Protocols”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name hospitality-expert directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Core Concepts / Hotel Management Systems / Technologies” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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