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AF xiapi-screener

基于技术形态筛选A股股票池，支持VCP、RPS强势股、创新高、高股息等多种形态，并可按涨跌幅、动量、强度等维度排序。触发词：股票筛选、选股、VCP形态、RPS强势股、创新高、技术形态、形态选股、自下而上选股、涨幅最大、跌幅最大、最强股票。适用场景：技术形态选股、趋势跟踪、动量选股、价值选股、按指定维度排序筛选、多条件组合筛选。不适用场景：个股深度基本面分析、财报研究、估值建模。

ClawHub Agent Skills author: 三水清 v1.0.3 MIT-0 5 files body ≈ 2 588 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 40/100 · Will not run — References files that are not bundled: ../daxiapi/references/field-descriptions.md

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
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
F
40/100
Will not run
References files that are not bundled: ../daxiapi/references/field-descriptions.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 missing-ref reference to a missing file: ../daxiapi/references/field-descriptions.md

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: ../daxiapi/references/field-descriptions.md
  • 0Tools and files. 1 referenced file(s) missing: ../daxiapi/references/field-descriptions.md
  • 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
  • 100Steps. 83 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2588 tokens
  • 100Running it twice. No mutating operations
  • low 11 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
  • -216 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 190: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 83 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This skill is a disclosed stock-screening helper that uses a DaxiAPI token for market data, with manageable credential-handling cautions.
LLM: benign (high) · VirusTotal: · 29 May 2026