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list-enrichment

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Table Enrichment

表格Enrichment

Add research-powered enrichment columns to Extruct company tables.
为Extruct企业表格添加由研究驱动的 enrichment 列。

Extruct API Operations

Extruct API 操作

This skill delegates all Extruct API calls to the 
extruct-api
skill.
For all Extruct API operations, read and follow the instructions in 
skills/extruct-api/SKILL.md
.
All table reads, column creation, enrichment runs, polling, and data fetching are handled by the extruct-api skill. This skill focuses on what to enrich and how to design columns — the extruct-api skill handles the API execution.
本Skill将所有Extruct API调用委托给
extruct-api
Skill。
对于所有Extruct API操作，请阅读并遵循
skills/extruct-api/SKILL.md
中的说明。
所有表格读取、列创建、enrichment运行、轮询和数据获取均由extruct-api Skill处理。本Skill专注于要增强哪些内容以及如何设计列——extruct-api Skill负责API执行。

Workflow

工作流

1. Confirm the table

1. 确认表格

Get the table ID from the user (URL or ID). Use the extruct-api skill to fetch table metadata. Show the user: table name, row count, existing columns.
向用户获取表格ID（URL或ID）。使用extruct-api Skill获取表格元数据。向用户展示：表格名称、行数、现有列。

2. Get column configs

2. 获取列配置

Two paths:
Path A: From enrichment-design — User has 
column_configs
ready. Confirm and proceed.
Path B: Design on the fly — Confirm with the user:
  1. What data point? — what to research (e.g. "funding stage", "primary vertical", "tech stack")
  2. Output format — pick the right format:
FormatWhen to useExtra params
text
Free-form research output—
number
/ 
money
Numeric data (revenue, headcount)—
select
Single choice from known categories
labels: [...]
multiselect
Multiple tags from known categories
labels: [...]
json
Structured multi-field data
output_schema: {...}
grade
1-5 score—
label
Single tag from list
labels: [...]
date
Date values—
url
/ 
email
/ 
phone
Contact info—
  1. Agent type — default 
    research_pro
    . Use 
    llm
    when no web research needed (classification from existing profile data).
有两种路径：
路径A：来自enrichment-design — 用户已准备好
column_configs
。确认后继续。
路径B：即时设计 — 与用户确认以下内容：
  1. 要添加的数据点？ — 需要研究的内容（如“融资阶段”、“核心行业领域”、“技术栈”）
  2. 输出格式 — 选择合适的格式：
格式适用场景额外参数
text
自由格式的研究输出—
number
/ 
money
数值型数据（收入、员工人数）—
select
从已知分类中单选
labels: [...]
multiselect
从已知分类中多选标签
labels: [...]
json
结构化多字段数据
output_schema: {...}
grade
1-5分评分—
label
从列表中选择单个标签
labels: [...]
date
日期值—
url
/ 
email
/ 
phone
联系信息—
  1. Agent类型 — 默认使用
    research_pro
    。当无需网页研究时（从现有资料数据进行分类），使用
    llm
    。

3. Write the prompt

3. 编写提示词

Craft a clear prompt using 
{input}
for the row's domain value. Prompt guidelines:
  • Be specific about what to find
  • Specify the exact output format in the prompt (e.g. "Return ONLY a number in millions USD")
  • Include fallback instruction (e.g. "If not found, return N/A")
  • For 
    select
    /
    multiselect
    , the labels constrain the output — the prompt should guide which label to pick
使用
{input}
指代行的域名值，编写清晰的提示词。提示词指南：
  • 明确说明需要查找的内容
  • 在提示词中指定确切的输出格式（例如：“仅返回以百万美元为单位的数字”）
  • 包含回退说明（例如：“若未找到，返回N/A”）
  • 对于
    select
    /
    multiselect
    ，标签会限制输出——提示词应指导选择哪个标签

4. Create the column(s)

4. 创建列

Delegate column creation to the extruct-api skill with the 
column_configs
array.
将列创建任务委托给extruct-api Skill，传入
column_configs
数组。

5. Trigger enrichment (only the new columns)

5. 触发增强（仅针对新列）

Delegate the enrichment run to the extruct-api skill. Always scope the run to the newly created column(s) only. Avoid broad or implicit run payloads when you only intend to enrich specific columns.
将增强运行任务委托给extruct-api Skill。始终将运行范围限定为仅新创建的列。当仅打算增强特定列时，避免使用宽泛或隐含的运行负载。

6. Monitor progress

6. 监控进度

Delegate progress monitoring to the extruct-api skill. Use it to poll table data and check cell statuses.
Show the user:
  • Current % complete (done cells / total cells)
  • Number of failed cells (if any)
  • Estimated time remaining (based on rate so far)
Stop polling when all cells are done or failed.
将进度监控任务委托给extruct-api Skill。使用该Skill轮询表格数据并检查单元格状态。
向用户展示：
  • 当前完成百分比（已完成单元格数/总单元格数）
  • 失败单元格数量（如有）
  • 预计剩余时间（基于当前速率）
当所有单元格完成或失败时停止轮询。

7. Quality spot-check

7. 质量抽查

After enrichment completes (or after 50%+ is done), fetch a sample of 5-10 enriched rows and display for review.
Present to user as a table. Ask:
  • "Does the data quality look right?"
  • "Any columns returning garbage or N/A too often?"
  • "Should we adjust any prompts and re-run?"
If quality issues are found:
  1. Delete the problematic column
  2. Adjust the prompt
  3. Re-create and re-run
增强完成后（或完成50%以上时），提取5-10条已增强的行样本并展示供审核。
以表格形式呈现给用户。询问：
  • “数据质量是否符合预期？”
  • “是否有列频繁返回无效内容或N/A？”
  • “是否需要调整提示词并重新运行？”
若发现质量问题：
  1. 删除有问题的列
  2. 调整提示词
  3. 重新创建列并重新运行