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BC hectorlee-volume-price-screener

纯量价关系A股选股系统 V3.7.1。6种量价形态(D/E/C/B/A/R)+多因子+多周期+行业共振+关注度追踪，130分制评分(+多周期确认-5~+10)，全A股覆盖(含科创板/北交所TDX数据源)，7×日定时扫描(09:00~15:05)，硬过滤ST/退市/次新/一字板/连板/跌势反弹。触发词：放量拉升、缩量调整、量价选股、量价形态、突破延续、蓄力突破、回踩确认、高位平台、底部反转、板块共振、盘中监控。

ClawHub Agent Skills author: HectorLee v3.7.1 MIT-0 32 files body ≈ 1 452 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

纯量价关系A股选股系统…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentAI and agentstype 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
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 28. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1452 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)
  • +3Output format is not stated: the model decides each time
  • -45 reference files, but SKILL.md never points to them: the model will not open them
  • -32 of 19 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 206: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: clean
This is a disclosed A-share volume-price stock screening skill whose local file, network, and MCP use fits its stated finance-analysis purpose.
LLM: benign (high) · VirusTotal: · 24 Aug 2026