BD social-sentiment-monitor
社交媒体舆情监控助手 - 实时监控Twitter、Reddit等平台的加密货币讨论、情绪分析和热点追踪。 当用户需要以下功能时触发此skill： (1) 监控特定代币或项目的社交媒体讨论热度 (2) 分析社区情绪变化（看涨/看跌/恐慌/FOMO） (3) 追踪KOL和大V的发言动向 (4) 发现 viral 内容和热点话题 (5) 监测负面舆情和FUD传播 (6) 生成舆情报告和趋势分析
As a process D 49/100 · 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.
For the model run — optional
- 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: 12. 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 49/100
- 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
- 40Consistency. Frontmatter name (social-sentiment-monitor) differs from the folder (shenmeng-social-sentiment-monitor)
- 100Tools and files. No external tools needed
- 100Steps. 47 steps
- 100Execution cost. Instruction body is 1220 tokens
- 100Running it twice. No mutating operations
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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 195: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 47 items
- +4Has examples (9 code blocks)
- +3All 7 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.
External checks
ClawHub: suspicious
This paid crypto sentiment skill is not clearly malicious, but it should be reviewed because it advertises live monitoring while generating simulated data and includes an under-scoped billing path.
LLM: suspicious (high) · VirusTotal: · 29 May 2026