BD bettafish-opinion-analysis
BettaFish（微舆）多智能体舆情分析系统 - 基于 QueryAgent、MediaAgent、InsightAgent 三引擎并行架构，通过 ForumEngine 实现 Agent 间协作讨论，生成 Word/PDF + 高设计质量 HTML 双格式报告。 当用户需要以下分析时触发此 skill： - 分析某品牌/企业/产品的社交媒体声誉和口碑 - 追踪热点舆情事件（如某车企被抨击、某明星争议事件） - 挖掘特定社媒账号的内容和影响力数据 - 监测竞品舆情动态，进行多品牌对比 - 分析公众对某话题的情绪倾向和态度 - 生成舆情监测报告或危机预警分析 - 需要Word文档/PDF格式的正式报告 - 需要高设计质量的交互式HTML可视化报告 - 需要基于真实数据的深度舆情分析 此 skill 采用 QueryAgent + MediaAgent + InsightAgent 并行架构，通过 ForumEngine 实现 Agent 间讨论协作，执行 3 轮反思循环优化分析结果，最终输出: 1. **Word/PDF 文档** - 使用 docx/pdf subskill 生成，适合正式汇报、打印、存档 2. **高设计质量 HTML 报告** - 使用 frontend-design subskill，独特的编辑杂志风格，交互式可视化 **不使用任何数据库和模拟数据**，所有数据通过 WebSearch/WebFetch/Browser/Curl 实时获取。 即使遇到复杂的多步骤分析需求、需要整合多个数据源、或生成专业格式的舆情报告，也请务必使用此 skill。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 18. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 46/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Steps. 99 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2855 tokens
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
- medium 8 test cases, all positive: not one "should refuse" or "should ask first"
- low No test case covers injection arriving through data
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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -43 reference files, but SKILL.md never points to them: the model will not open them
- -33 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 699: enough signal without eating the budget
- +4Structure: 50 headings
- +3Step-by-step instructions: 99 items
- +4Has examples (22 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.