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Install in OpenClaw
/install gaming-backlog-guide
Description
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...
README (SKILL.md)
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
- Read mood, energy, time, platform, and budget signals.
- Infer the kind of experience the user actually needs right now.
- Recommend 2 to 3 game directions, not a random pile of titles.
- Separate a play-now option, a weekend option, and one type to avoid for now.
- 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.
Usage Guidance
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.
Capability Analysis
Type: OpenClaw Skill
Name: gaming-backlog-guide
Version: 1.0.0
The gaming-backlog-guide skill is a recommendation tool that suggests game types based on user mood and available time. The logic in handler.py is entirely self-contained, using keyword scoring and predefined profiles to generate text responses without any network access, file system modifications, or sensitive data retrieval.
Capability Tags
Capability Assessment
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install gaming-backlog-guide - After installation, invoke the skill by name or use
/gaming-backlog-guide - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Gaming Backlog Guide.
- Helps match your current mood, time, platform, and energy to the right kind of gaming experience.
- Suggests specific directions to play now, for the weekend, and one type to avoid for now.
- Provides a low-friction recommendation for the easiest way to get started.
- Focuses on fit and reducing choice paralysis—no live news, prices, or review data included.
- Designed for solo, family, or reward-based play planning with a plain, dialogue-based workflow.
Metadata
Frequently Asked Questions
What is Gaming Backlog Guide?
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... It is an AI Agent Skill for Claude Code / OpenClaw, with 81 downloads so far.
How do I install Gaming Backlog Guide?
Run "/install gaming-backlog-guide" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Gaming Backlog Guide free?
Yes, Gaming Backlog Guide is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Gaming Backlog Guide support?
Gaming Backlog Guide is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Gaming Backlog Guide?
It is built and maintained by haidong (@harrylabsj); the current version is v1.0.0.
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