SKILLEMALL.ai
RU Sign in

BD 盯盘哨兵

实时盯盘工具，帮助上班族工作时间查询股票、贵金属价格走势并推送到指定频道

ClawHub Agent Skills author: fslong v1.6.0 MIT-0 5 files body ≈ 2 531 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
81/100
safety, quality, tests
Safety 60%
98
Quality 40%
55
Run on models
none yet
Process rating
D
39/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token SKILL.md:358
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    target_user: o9…@….wechat
    detector
  • low Secrets in code secret-high-entropy-token SKILL.md:368
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    --target-user "o9…@….wechat" \
    detector

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-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "icon"

Process rating: all ten parameters 39/100

  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 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
  • 40Consistency. Frontmatter name (盯盘哨兵) differs from the folder (finance-watch)
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 44 steps
  • 100Execution cost. Instruction body is 2531 tokens
  • 100Running it twice. No mutating operations
  • 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)
  • +3Description length 36: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 43 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (21 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.

External checks

ClawHub: suspicious
This finance-monitoring skill is mostly coherent, but it is preconfigured to send reports to a specific WeChat recipient that may not belong to the installer.
LLM: suspicious (high) · VirusTotal: · 29 May 2026