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BD Privora · 实时监控 for AI Agents

Privora 实时监控——黄金实时监控 / 基金净值监控 / 多资产阈值告警，给一个已订阅资产字段配一条阈值规则（跌破/突破/等于），越线即飞书/微信/通用 Webhook 通知你；港股/美股/A股同样支持。建渠道→建规则→验证→上线，一份文档 11 步走完；Bearer Token 直连 /agent/skills/execute。

ClawHub Agent Skills author: GuangfuWu v1.0.4 MIT-0 5 files · 1 script body ≈ 4 476 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Privora 实时监控——黄金实时监控 / 基金净值监控 / 多资产阈值告警，给一个已订阅资产字段配一条阈值规则（跌破/突破/等于），越线即飞书/微信/通用 Webhook 通知你；港股/美股/A股同样支持。建渠道→建规则→验证→上线，一份文档 11 步走完；Bearer Token 直连…

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
D
44/100
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.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "title"
  • note frontmatter-key unknown frontmatter key "updatedAt"
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 44/100

  • 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
  • 30Running it twice. 21 mutating operations with no state check
  • 40Consistency. Frontmatter name (Privora · 实时监控 for AI Agents) differs from the folder (privora-alert)
  • 70Execution cost. Instruction body is 4476 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 34 steps
  • low 10 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
  • -228 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 169: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 34 items
  • +4Has examples (12 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
The skill mostly matches its Privora alerting purpose, but it ships a flexible authenticated API wrapper that can bypass the package’s narrow allowlist if used outside the documented flow.
LLM: suspicious (medium) · 31 Aug 2026