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BD news-sentiment-scan

舆情监控与情绪分析技能,扫描港股、美股、A股等公司公告、新闻报道、券商研报与社交媒体(微博、雪球),去噪后进行情绪打分(-10至+10),输出情绪温度计与重大事件清单。支持多渠道信息采集、来源权重加权、事件类型量化与操作建议生成,适用于投资决策辅助、舆情预警与市场情绪跟踪。基于Python脚本执行,通过自然语言指令驱动Agent完成任务.

ClawHub Hermes author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 2 097 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

舆情监控与情绪分析技能…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationResearchFinanceAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 170 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "summary_zh"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "pricing_tier"

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 (news-sentiment-scan) differs from the folder (news-sentiment-2)
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 53 steps
  • 100Execution cost. Instruction body is 2097 tokens
  • 100Running it twice. No mutating operations
  • low 24 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

  • +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
  • +2Single-language instructions
  • +3Description length 170: enough signal without eating the budget
  • +4Structure: 40 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (4 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
The skill is mostly a Markdown guide for stock-news sentiment scanning, but it requests command execution and describes callback URLs, file writes, external APIs, and unrelated security-scan features without clear scope or implementation.
LLM: suspicious (high) · 20 Aug 2026