ClawHub

Gaming Backlog Guide

v1.0.0

Match the user’s current mood, available time, platform habits, and energy level to the right kind of game experience, then suggest a low-friction way to sta...

⭐ 0· 71·0 current·0 all-time
byhaidong@harrylabsj

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OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for harrylabsj/gaming-backlog-guide.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Gaming Backlog Guide" (harrylabsj/gaming-backlog-guide) from ClawHub.
Skill page: https://clawhub.ai/harrylabsj/gaming-backlog-guide
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

Bare skill slug

openclaw skills install gaming-backlog-guide

ClawHub CLI

Package manager switcher

npx clawhub@latest install gaming-backlog-guide
Security Scan
Capability signals
CryptoCan make purchases
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
VirusTotalVirusTotal
Benign
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OpenClawOpenClaw
Benign
high confidence
✓
Purpose & Capability
Name/description (help choose games given mood/time/energy) matches the code and SKILL.md: the handler scores profiles and builds textual recommendations. No unexpected credentials, binaries, or unrelated platform integrations are requested.
✓
Instruction Scope
SKILL.md is dialogue-focused and explicitly says it does not fetch live store or pricing data. The handler reads only the skill's local SKILL.md and user input, parses and scores it, and formats textual recommendations; there are no instructions to read system-wide config, shell history, or transmit data externally in the visible code.
✓
Install Mechanism
No install spec is present (instruction-only install), and included code files are pure Python with no install step. This is the lowest-risk model for skills with local logic.
✓
Credentials
The skill requests no environment variables, no credentials, and no config paths. The code only opens the local SKILL.md and uses standard libraries (json, os, re), which is proportional to its purpose.
✓
Persistence & Privilege
always:false and default autonomous invocation are appropriate for a dialog skill. The code does not request persistent presence, nor does it modify other skills or system-wide settings in the visible portions.
Assessment
This skill appears coherent and limited to local text processing and recommendation generation. Before installing or granting autonomous invocation, you may want to: 1) quickly scan the full handler.py for any networking (requests, urllib, sockets), subprocess/os.exec calls, or file system writes outside the skill folder; 2) run the provided tests in a sandboxed environment to confirm behavior; and 3) avoid enabling always:true or giving unrelated credentials (not required here). If you see network calls or unexpected file access in the rest of the file, reconsider or sandbox usage.

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

latestvk979sh4yyjam1gv1q54c1j0dws84xv85
71downloads
0stars
1versions
Updated 1w ago
v1.0.0
MIT-0
haidong@harrylabsj
latest
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Gaming Backlog Guide

Chinese name: 游戏荒推荐指南

Purpose

Help the user find the right kind of game experience for the current moment instead of doom-scrolling the backlog or starting something that does not fit the available time and energy. This skill is descriptive only. It does not fetch live release news, store prices, or review scores.

Use this skill when

  • The user wants to play something but cannot choose.
  • The user feels low energy, bored, or stuck in backlog guilt.
  • The user wants a game direction that fits tonight, the weekend, or a tight schedule.
  • The user wants a better leisure match instead of a giant title dump.

Inputs to collect

  • Current mood
  • Available play time
  • Platform habits
  • Budget or backlog pressure
  • Recently played genres
  • Desired experience or feeling

Workflow

  1. Read mood, energy, time, platform, and budget signals.
  2. Infer the kind of experience the user actually needs right now.
  3. Recommend 2 to 3 game directions, not a random pile of titles.
  4. Separate a play-now option, a weekend option, and one type to avoid for now.
  5. End with one easiest-start suggestion.

Output Format

  • Current need profile
  • Best-fit directions
  • Start here
  • Avoid for now
  • Tonight’s easiest start

Quality bar

  • The recommendation must match time and energy honestly.
  • The output should emphasize experience fit, not title spam.
  • Include at least one option the user can start with low friction.
  • Stay honest about not having live launch or pricing data.

Edge cases and limits

  • If the user wants brand-new release news, explain that this skill does not provide real-time launch data.
  • If budget is tight, prefer backlog, free, or low-cost directions.
  • This skill does not replace reviews, sale tracking, or purchasing guidance.

Compatibility notes

  • Works for solo leisure planning, family play, and reward-based downtime.
  • Can pair conceptually with gaming-session-scheduler.
  • Fully dialogue-based, no store integration required.

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