# Trade Pipeline Run

> Execute the trade document pipeline (RFQ → Quotation → PI → CI → PL) or price write-back

- Skill: `dangooy/trade-pipeline-run` (Agent Skill)
- Install (CLI): `npx skillmds@latest add dangooy/trade-pipeline-run`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dangooy/trade-pipeline-run/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: dangooy (https://skillmd.com/u/dangooy)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dangooy/trade-pipeline-run

---


# Trade Pipeline Run — Execute the Document Pipeline

## Trigger

When the user says: 处理询价单, 生成报价单, 做报价, 做PI, 做CI, 做PL, 跑管线, run pipeline, generate quotation, process inquiry, 处理这个Excel, 帮我出单

## Prerequisites

Before running, verify in order:

1. **Current directory**: must contain `trade_pipeline/` directory. If not, `cd` to the repo root.

2. **Installation check**: run `trade-pipeline --help`. If that command is not found, try `python -m trade_pipeline --help`, then `python3 -m trade_pipeline --help` (Mac / Linux have no bare `python`). If all fail, prompt:
   ```
   管线未安装。请在项目根目录（含 pyproject.toml）先建虚拟环境再安装：

     Mac / Linux:
       python3 -m venv .venv && source .venv/bin/activate
     Windows PowerShell:
       py -m venv .venv; .\.venv\Scripts\Activate.ps1

     pip install -e .

   （不能跳过虚拟环境：Homebrew 和多数 Linux 发行版的系统 Python 受 PEP 668 保护，
   直接 pip install 会报 externally-managed-environment。）
   ```

3. **Config check**: Read `trade_pipeline/config/config.yaml`.
   - If seller `name_en` contains "ACME EXPORT" or buyer email contains ".example.com" → inform user:
     "当前使用示例配置，可以跑 demo 体验。真实业务请先说"初始化配置"运行 init skill。"
   - Continue regardless (demo mode is fine).

## Flow 1: Pipeline Execution

### Step 1: Collect Input

Use AskUserQuestion to ask:

**Question 1** — 询价单路径:
- If user already provided a file path in their message, use it directly
- Otherwise ask: "询价单 Excel 文件路径？"
- Verify the file exists with Read tool

**Question 2** — 订单号:
- Ask: "订单号？（用于命名输出文件）"
- Options can include suggestions like DEMO, or user types custom

**Question 3** — 选择客户:
- Read `trade_pipeline/config/config.yaml`, extract buyer keys and name_en
- Use AskUserQuestion with options:
  - Each configured buyer: `buyer_id — name_en`
  - `_new — 新客户（buyer 信息填 TBD）`
  - "Other" for manual input
- Maximum 4 options (AskUserQuestion limit), prioritize most recently used

### Step 2: Execute

Run via Bash:
```bash
trade-pipeline --input <path> --order <order_no> --buyer <buyer_id>
```

If `trade-pipeline` command not found, fallback to:
```bash
python -m trade_pipeline --input <path> --order <order_no> --buyer <buyer_id>
```

Do NOT add `--interactive` flag (default to review.json mode for safety).

### Step 3: Report Results

If successful (exit code 0):
- List all 6 generated files with paths
- Show key stats: item count, buyer, seller, currency
- **If the output contains "⚠ 本次为降级结果"** (LLM parse fell back to rules mode
  due to a malformed response, not a network/auth error): surface this warning
  to the user verbatim, don't bury it — the parsed data may be less accurate
  than a normal `--use-llm` run, so ask the user to double-check item details.
- Prompt next step: "报价单已生成，单价列（黄色高亮）待填写。填好后说'价格填好了'触发回写。"

If buyer match failed (output contains "review.json"):
- Show the review.json path
- Explain: "buyer 匹配失败，已生成 review.json。"
- `review.json`'s `candidate_values` only contains raw buyer_id strings (e.g.
  `global_fasteners`) — **not company names**. Read `trade_pipeline/config/config.yaml`
  yourself and look up each candidate id's `name_en` (and address, if helpful)
  before showing anything to the user. Never show a bare buyer_id list.
- **Must display the full candidate list with real company names** using
  AskUserQuestion, and have the user explicitly pick the correct one
  (or say "都不是，是新客户"). Only after the user's explicit choice, edit
  `review.json` to set `resolved_value` to that buyer_id, then rerun with `--confirm`.
- Do NOT infer or guess a buyer_id from context (order history, similar past
  orders, etc.) and write it into `resolved_value` without the user naming it.

## Flow 2: Price Write-Back

### Trigger
价格填好了, 回写价格, 重新生成PI/CI, price update, 单价已填

### Steps

1. Locate the most recent quotation and model files:
   - Look in `output/<most_recent_order>/` for `*_quotation.xlsx` and `*_model.json`
   - Or ask user to confirm paths

2. Execute:
   ```bash
   trade-pipeline --price-update <quotation_path> --model <model_path>
   ```

3. Report:
   - How many prices were updated
   - Whether PI/CI were regenerated
   - If errors occurred: show errors, advise to fix and retry
   - **If PI/CI were regenerated, always end with this reminder — do not skip it**:
     "PI/CI 是正式对外单证，发送给客户前请人工核对买家抬头、金额、税号是否正确。"
     Automated checks (precheck) only catch structurally invalid data (missing
     weight, bad format, unknown seller) — they cannot detect a correctly-formatted
     document sent to the wrong buyer or with a wrong amount that's still valid-looking.

## Notes

- This skill handles the runtime execution. For first-time configuration, use the `trade-pipeline-init` skill.
- All generated files go to `output/<order_no>/`.
- The pipeline generates 6 files: rfq.json, model.json, quotation.xlsx, pi.xlsx, ci.xlsx, pl.xlsx.
- **PI/CI are legally consequential documents.** Whenever they're generated or
  regenerated, remind the user to manually verify buyer name/address, amounts,
  and tax IDs before sending — the pipeline's automated checks validate structure,
  not business correctness.
- Price column in quotation is yellow-highlighted — this is the manual input point.
- After price write-back, PI and CI are regenerated with actual amounts.