trade-pipeline-run: Reusable for AI Agents | OpenAgentSkill

Overview

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

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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:

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

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。）

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:

trade-pipeline --input <path> --order <order_no> --buyer <buyer_id>

If trade-pipeline command not found, fallback to:

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

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

Execute:

trade-pipeline --price-update <quotation_path> --model <model_path>

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.

View source ↗

File metadata

name: trade-pipeline-run
description: Execute the trade document pipeline (RFQ → Quotation → PI → CI → PL) or price write-back
triggers:
  - 处理询价单
  - 生成报价单
  - 做报价
  - run pipeline
  - generate quotation
  - 帮我出单
  - 价格填好了
  - 回写价格

View original text

---
name: trade-pipeline-run
description: Execute the trade document pipeline (RFQ → Quotation → PI → CI → PL) or price write-back
triggers:
  - 处理询价单
  - 生成报价单
  - 做报价
  - run pipeline
  - generate quotation
  - 帮我出单
  - 价格填好了
  - 回写价格
---

# 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.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 25 GitHub stars
Stars/forks activity: 25 stars, 5 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Open full audit

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Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: Dangooy/trade-pipeline-skill
License: MIT
Version: Unknown
Last GitHub push: Sep 4, 2026
Registry updated: Sep 13, 2026
Instruction path: .claude/skills/trade-pipeline-run/SKILL.md @ c6a6df9d5345

Version reported in registry metadata; check source releases before relying on it.

Quality

55/100

Promising

Trust

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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      "Audit: 71/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "dangooy-trade-pipeline-run (trade-pipeline-run)",
      "install_command": "npx skills add Dangooy/trade-pipeline-skill --skill trade-pipeline-run",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "dangooy-trade-pipeline-run",
      "task": "Use trade-pipeline-run in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/dangooy-trade-pipeline-run",
    "api": "https://www.openagentskill.com/api/agent/skills/dangooy-trade-pipeline-run",
    "audit": "https://www.openagentskill.com/skills/dangooy-trade-pipeline-run/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=dangooy-trade-pipeline-run&task=Use%20trade-pipeline-run%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20trade-pipeline-run%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20trade-pipeline-run%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/dangooy-trade-pipeline-run/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/dangooy-trade-pipeline-run"
  }
}

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Creator: Dangooy
Source: Dangooy/trade-pipeline-skill
Indexed by: OpenAgentSkill community index

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/dangooy-trade-pipeline-run?metric=listed&label=Listed)](https://www.openagentskill.com/skills/dangooy-trade-pipeline-run?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/dangooy-trade-pipeline-run?metric=trust&label=Trust)](https://www.openagentskill.com/skills/dangooy-trade-pipeline-run?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/dangooy-trade-pipeline-run?metric=audit&label=Audit)](https://www.openagentskill.com/skills/dangooy-trade-pipeline-run/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/dangooy-trade-pipeline-run?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/dangooy-trade-pipeline-run?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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Overview

Trade Pipeline Run — Execute the Document Pipeline

Trigger

Prerequisites

Flow 1: Pipeline Execution

Step 1: Collect Input

Step 2: Execute

Step 3: Report Results

Flow 2: Price Write-Back

Trigger

Steps

Notes

Review the source

Start with one small task

Source & usage notes

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