BF xiaoguo-qmt-assistant
小果全能大QMT量化交易助手 - 专业级QMT策略开发全能助手 本助手是专为「迅投QMT极速策略交易系统」打造的量化交易策略开发专家。由小果（微信：xg_quant）精心打造，深度封装了QMT平台的全部API接口、运行机制、数据结构和实战经验，能够根据用户的自然语言描述，自动生成从基础到高级、从回测到实盘的完整量化交易策略代码。 【核心功能矩阵】 ✅ 策略开发：根据需求描述自动生成QMT策略代码，支持股票/期货/期权/ETF/可转债/两融等多品种 ✅ 策略回测：生成完整的回测模型代码，包含历史数据获取、撮合规则、绩效分析 ✅ 实盘交易：生成实盘模型代码，支持逐K线生效和立即下单两种模式 ✅ 代码检查：自动检测QMT策略常见错误（ContextInfo回滚、全局变量、白名单、阻塞操作等） ✅ 策略模板：提供双均线、MACD、RSI、布林带、网格交易、多因子选股等多种内置模板 ✅ 函数查询：详细解释QMT全部API函数的用法、参数和示例 ✅ 问题诊断：针对QMT运行报错提供解决方案和修复建议 ✅ 数据管理：提供历史数据下载、财务数据获取、实时行情订阅的完整方案 版本：2.0.0 作者：小果 微信：xg_quant 更新日期：2026-08-31
小果全能大QMT量化交易助手 - 专业级QMT策略开发全能助手…
As a process F 26/100 · Will not run — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 80. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 43929 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 26/100
- 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
- 10Execution cost. Instruction body is 43929 tokens: crowds the task out of the window
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 25 mutating operations with no state check
- 40Consistency. Frontmatter name (xiaoguo-qmt-assistant) differs from the folder (xg-qmt)
- 50Steps. 2 steps
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- low The response is described with custom markup (8 tags): a typed call is more reliable
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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- -213 emoji in the instructions: noise for the model
- -410 reference files, but SKILL.md never points to them: the model will not open them
- -369 of 69 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 530: enough signal without eating the budget
- +4Structure: 65 headings
Quality base 70; lint remarks subtract, signals add up to 100. Result: 46.