Trade review
Skill nexaforgelab/a-share-investment-skill-pack/skills/12_review_learning/trade_review
复盘交易执行质量、冲击成本、滑点、成交节奏和机会成本。From its SKILL.md
npx -y skills add nexaforgelab/a-share-investment-skill-pack --skill trade_reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
4.7 KB, ~1.6k tokens by cl100k_base, as published. Nobody here has run it
交易复盘/TCA Skill
Skill ID
a_share.trade_review.v1
功能定位
复盘交易执行质量、冲击成本、滑点、成交节奏和机会成本。
适用场景
交易后、调仓后、月度复盘。
必填输入
as_of_date: 分析截止日期,必须使用当时已公开可得数据。object_type:stock/industry/portfolio/market/theme/factor。object_id: 股票代码、行业名称、组合ID、主题名称或因子名称。investment_horizon:intraday/1w/1m/3m_6m/1y_3y。benchmark: 沪深300、中证500、中证1000、行业指数或自定义基准。data_bundle: 已加载的数据清单、来源、时间戳和质量评分。
可选输入
portfolio_context: 当前持仓、成本、权重、产品约束。risk_budget: 单票、行业、风格、流动性和回撤约束。previous_thesis: 历史投资假设、上次评级、上次目标价。manual_notes: 研究员补充信息或人工调研纪要。
依赖 Skill
rebalancing_adviceliquidity_risk
通用执行流程
- 校验输入参数,确认
as_of_date、对象、投资期限、基准、股票池和组合约束。 - 检查依赖数据是否齐全,并调用或引用数据质量审计结果。
- 按本 Skill 的专业框架进行分析,不跳步,不省略反证。
- 对所有关键判断打分:事实强度、影响程度、可验证性、时效性、组合相关性。
- 生成结构化 JSON、Markdown 报告和审计日志。
- 给出下游建议:进入哪个 workflow、是否需要人工复核、何时再次更新。
本 Skill 专业步骤
- 比较目标价格、实际成交、VWAP、TWAP、收盘价。
- 拆解冲击成本、滑点、未完成成交和机会成本。
- 识别交易指令、流动性和择时问题。
判断与打分框架
fact_strength: 事实强度,公告/财报/交易所数据最高,二手新闻和传闻最低。impact_score: 对盈利、估值、现金流、风险偏好、组合风险的影响程度。time_sensitivity: 信息有效期和是否需要盘中处理。verification_score: 是否可被其他数据源交叉验证。portfolio_relevance: 对当前组合、候选池、禁投池的影响程度。
标准输出
同时输出两种形态:
result.json:供下游 Agent / workflow 读取;report.md:供投研人员阅读。
result.json 至少包含:
{
"skill_id": "a_share.trade_review.v1",
"run_id": "test_run_0001",
"as_of_date": "2026-05-22",
"object_type": "portfolio",
"object_id": "demo_portfolio",
"conclusion": "待生成,禁止作为投资建议直接执行",
"confidence": 0.0,
"evidence": [],
"counter_evidence": [],
"risks": [],
"action_suggestion": "research_only",
"required_followups": [],
"human_review_required": true,
"data_quality_score": 0.0,
"audit_trail": []
}
报告结构
# 交易复盘/TCA报告
## 1. 一句话结论
## 2. 核心证据
## 3. 关键数据与图表建议
## 4. 正方观点
## 5. 反方观点
## 6. 风险与证伪条件
## 7. 对估值/盈利/组合/交易的影响
## 8. 后续跟踪指标
## 9. 人工复核事项
## 10. 审计日志
强制约束
- 不得把传闻、新闻标题、管理层口头表述当成确定事实;所有关键结论必须标注证据来源与时间戳。
- 不得使用未来函数:财务数据、公告、研报、价格、指数成分必须以
as_of_date当时已公开可得为准。 - 不得绕过人工复核直接给出实盘下单指令;涉及买入、卖出、加仓、减仓、禁投、强制平仓的结论必须输出
human_review_required: true。 - 数据缺失、数据冲突、来源不可靠时,必须降低置信度,并列出不可验证项。
- 对 ST、*ST、停牌、退市整理、重大违法、财务造假、流动性不足标的,必须优先触发风险检查。
- 报告中必须同时给出正方观点、反方观点、证伪条件和后续跟踪指标。
下游流转
- 若输出影响投资评级、仓位或禁投状态,必须传递给
investment_memo、portfolio_risk_monitor和compliance_check。 - 若发现数据异常,必须回流给
data_quality_audit或data_conflict_resolution。 - 若发现投资假设变化,必须回流给
thesis_review。
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most review quality skills give in ~1.6k tokens
Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07
- Ask questions one at a timein 81 of 1048, across 64 files
- Provide a recommended answer for each questionin 73 of 1048, across 50 files
- Explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
- Resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
- Interview the user relentlessly about the planin 38 of 1048, across 13 files
- Order findings by severityin 31 of 1048
- Resolve each branch of the decision treein 27 of 1048, across 5 files
- Run a grilling sessionin 26 of 1048, across 5 files
- Update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
- Propose precise canonical terms for vague languagein 25 of 1048, across 7 files
- Create documentation files lazilyin 24 of 1048, across 5 files
- Assign severity to every findingin 24 of 1048
Said here and by no other author read
- decompose impact costs and slippage
- identify execution and timing problems
- score key judgements
- generate result json and report markdown
- provide downstream workflow suggestions
- mark conclusions with evidence and timestamp
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.