BB game-sentiment
Automated game sentiment monitoring skill for mobile/PC games. Scans public feedback across multiple channels (Weibo, Bilibili, Zhihu, Tieba, NGA, TapTap, Reddit, X/Twitter, YouTube, Discord, Xiaohongshu, game media, etc.), classifies issues, assesses severity, assigns ownership, and generates actionable reports with P1 alerts. Use when: user asks to monitor game sentiment, create daily sentiment reports, analyze post-update player feedback, scan for negative sentiment spikes, do game industry sentiment analysis, track player complaints, monitor game reviews, watch for PR crises, check game community health, monitor game reputation (游戏口碑), game sentiment monitoring (游戏舆情监测), or says things like "帮我看看XX游戏的口碑", "跑一下舆情", "游戏评价怎么样", "玩家在骂什么", "游戏口碑怎么样", "做个游戏舆情监测", "monitor game feedback", "what are players saying about", "run sentiment scan". NOT for: generic brand PR writing, broad industry news without a specific game, one-off web searches without structured output, customer service scripting, or non-game product sentiment.
As a process B 79/100 · Nearly there — weak spots: running it twice, progress reporting
How to improve
- Shorten the description to 1024 characters.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1039 chars, limit 1024
Process rating: all ten parameters 79/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4266 tokens
- 100Tools and files. No external tools needed
- 100Steps. 107 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- low 11 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +3Description length 1038: 120–800 characters recommended
- -229 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +4Description says when NOT to use the skill
- +4Structure: 18 headings
- +3Step-by-step instructions: 107 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.