Linear 问题分析技能：自动化收集问题上下文，集成 GitHub、Notion、Loom 等工具

linear-issue by n8n-io/n8n

185 周安装量

181,100 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/n8n-io/n8n --skill linear-issue

团队协作项目管理自动化

🇨🇳中文介绍

Linear 问题分析

开始处理 Linear 问题 $ARGUMENTS

前提条件

此技能依赖于外部工具。在继续之前，请验证其可用性：

必需项：

Linear MCP (mcp__linear): 必须已连接。没有它，此技能将完全无法工作。
GitHub CLI (gh): 必须已安装并完成身份验证。运行 gh auth status 进行验证。用于获取关联的 PR 和问题。

可选项（优雅降级）：

Notion MCP (mcp__notion): 仅在问题链接到 Notion 文档时需要。如果不可用，请在摘要中注明 Notion 链接，并告知用户手动检查。
Loom 转录技能 (/loom-transcript): 仅在问题包含 Loom 视频时需要。如果不可用，请在摘要中注明 Loom 链接供用户观看。
curl : 用于下载图像。几乎总是可用；如果缺失，则跳过图像下载并注明。

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1. 从 Linear 获取问题和评论

使用 Linear MCP 工具一起获取问题详情和评论：

使用 mcp__linear__get_issue 和问题 ID 获取完整详情，包括附件
包含关联关系以查看阻塞/相关/重复的问题
紧接着，使用 mcp__linear__list_comments 和问题 ID 来获取所有评论

这两个调用应在同一步骤中一起进行，以便预先收集完整的上下文。

2. 分析附件和媒体（强制）

重要提示： 此步骤不是可选的。您必须扫描并获取问题描述和所有评论中的所有视觉内容。

截图/图像（始终获取）：

扫描问题描述和所有评论中的所有图像 URL：
- <img> 标签
- Markdown 图像 ![](url)
- 原始 URL（github.com/user-attachments, imgur.com 等）
对于找到的每个图像（在描述或评论中）：
- 使用 curl -sL "url" -o /path/to/image.png 下载（GitHub URL 需要遵循重定向）或使用 linear mcp
- 对下载的文件使用 Read 工具查看它
- 详细描述您看到的内容
不要跳过图像——它们通常包含关键上下文，如错误消息、UI 状态或配置

Loom 视频（始终获取转录稿）：

扫描问题描述和所有评论中的 Loom URL（loom.com/share/...）
对于找到的每个 Loom 视频（在描述或评论中）：
- 使用 /loom-transcript 技能获取完整转录稿
- 总结关键点、时间戳以及任何演示的问题
Loom 视频通常包含仅凭文本无法传达的关键复现步骤和上下文

3. 获取相关上下文

相关的 Linear 问题：

对关联关系（阻塞、被阻塞、相关、重复）中提到的任何问题使用 mcp__linear__get_issue
总结它们与主要问题的关系

GitHub PR 和问题：

如果提到了 GitHub 链接，使用 gh CLI 获取 PR/问题详情：
- gh pr view <number> 用于拉取请求
- gh issue view <number> 用于问题
下载附加到问题的图像：curl -H "Authorization: token $(gh auth token)" -L <image-url> -o image.png

Notion 文档：

如果存在 Notion 链接，使用 mcp__notion__notion-fetch 和 Notion URL 或页面 ID 来检索文档内容
总结相关文档

评论已在步骤 1 中获取。审查它们以获取：

额外的上下文和讨论历史
评论中链接的任何附件或媒体（在步骤 2 中处理）
对原始问题描述的澄清或更新

5. 识别受影响的节点（如适用）

确定此问题是否特定于某个 n8n 节点（例如，触发器、操作或工具节点）。在以下方面寻找线索：

问题标题（例如 "Linear trigger"、"Slack node"、"HTTP Request"）
提到节点名称的问题描述和评论
问题上的标签（例如 node:linear, node:slack）
显示特定节点配置或错误的截图

如果问题是节点特定的：

查找节点类型 ID。 使用 Grep 在 packages/frontend/editor-ui/data/node-popularity.json 中搜索节点的显示名称（或其关键词）以找到确切的节点类型 ID。作为参考，常见的 ID 模式是： * 核心节点：n8n-nodes-base.<camelCaseName>（例如 "HTTP Request" → n8n-nodes-base.httpRequest） * 触发器变体：n8n-nodes-base.<name>Trigger（例如 "Gmail Trigger" → n8n-nodes-base.gmailTrigger） * 工具变体：n8n-nodes-base.<name>Tool（例如 "Google Sheets Tool" → n8n-nodes-base.googleSheetsTool） * LangChain/AI 节点：@n8n/n8n-nodes-langchain.<camelCaseName>（例如 "OpenAI Chat Model" → @n8n/n8n-nodes-langchain.lmChatOpenAi）
从 packages/frontend/editor-ui/data/node-popularity.json 中查找节点的流行度分数。 使用 Grep 在该文件中搜索节点 ID。流行度分数是一个介于 0 和 1 之间的对数尺度值。使用以下阈值进行分类：

分数	等级	描述	示例
≥ 0.8	高	核心/广泛使用的节点，前约 5%	HTTP Request (0.98), Google Sheets (0.95), Postgres (0.83), Gmail Trigger (0.80)
0.4–0.8	中	定期使用的集成	Slack (0.78), GitHub (0.64), Jira (0.65), MongoDB (0.63)
< 0.4	低	小众或很少使用的节点	Amqp (0.34), Wise (0.36), CraftMyPdf (0.33)

在摘要中包含原始分数和等级（高/中/低）。

如果未在流行度文件中找到该节点，请注意它可能是社区节点或非常新/小众的节点。

6. 评估工作量/复杂度

收集所有上下文后，评估修复/实现此问题所需的工作量。使用以下 T 恤尺码：

尺码	大致工作量
XS	≤ 1 小时
S	≤ 1 天
M	2-3 天
L	3-5 天
XL	≥ 6 天

进行此评估时，请考虑：

变更范围：需要修改多少文件/包？是单个节点修复还是跨领域变更？
复杂度：是简单的参数更改、新的 API 集成、新的凭证类型，还是架构变更？
测试：需要多少测试覆盖率？是否需要端到端测试？
风险：这会破坏现有功能吗？是否需要向后兼容？
依赖项：是否有外部 API 变更、新包或跨团队协调需求？
文档：是否需要文档更新、迁移指南或变更日志条目？

提供 T 恤尺码以及简要的理由说明，解释驱动估算的关键因素。

在呈现之前，请验证您已完成：

下载并查看了描述和评论中的所有图像
获取了描述和评论中所有 Loom 视频的转录稿
通过 gh CLI 获取了所有链接的 GitHub 问题/PR
列出了问题上的所有评论
检查了问题是否是节点特定的，如果是，则查找了流行度
使用 T 恤尺码评估了工作量/复杂度

收集所有上下文后，呈现一个全面的摘要，包括：

问题概览：标题、状态、优先级、负责人、标签
描述：完整的问题描述，包含评论中的任何澄清
视觉上下文：截图/视频摘要（您在每项中观察到的内容）
受影响的节点（如适用）：节点名称、节点类型 ID (n8n-nodes-base.xxx)、流行度分数及等级（例如 0.64 — 中等流行度）
相关问题：此问题如何与其他工作相关联
技术上下文：任何 PR、代码引用或文档
工作量估算：T 恤尺码 (XS/S/M/L/XL) 及理由说明
后续步骤：基于所有收集到的上下文建议的方法

问题 ID 可以以以下格式提供：AI-1975、node-1975 或仅 1975（将进行搜索）
如果未提供问题 ID，请向用户询问

🇺🇸English

Linear Issue Analysis

Start work on Linear issue $ARGUMENTS

Prerequisites

This skill depends on external tools. Before proceeding, verify availability:

Required:

Linear MCP (mcp__linear): Must be connected. Without it the skill cannot function at all.
GitHub CLI (gh): Must be installed and authenticated. Run gh auth status to verify. Used to fetch linked PRs and issues.

Optional (graceful degradation):

Notion MCP (mcp__notion): Needed only if the issue links to Notion docs. If unavailable, note the Notion links in the summary and tell the user to check them manually.
Loom transcript skill (/loom-transcript): Needed only if the issue contains Loom videos. If unavailable, note the Loom links in the summary for the user to watch.
curl : Used to download images. Almost always available; if missing, skip image downloads and note it.

If a required tool is missing, stop and tell the user what needs to be set up before continuing.

Instructions

Follow these steps to gather comprehensive context about the issue:

1. Fetch the Issue and Comments from Linear

Use the Linear MCP tools to fetch the issue details and comments together:

Use mcp__linear__get_issue with the issue ID to get full details including attachments
Include relations to see blocking/related/duplicate issues
Immediately after , use mcp__linear__list_comments with the issue ID to fetch all comments

Both calls should be made together in the same step to gather the complete context upfront.

2. Analyze Attachments and Media (MANDATORY)

IMPORTANT: This step is NOT optional. You MUST scan and fetch all visual content from BOTH the issue description AND all comments.

Screenshots/Images (ALWAYS fetch):

Scan the issue description AND all comments for ALL image URLs:
- <img> tags
- Markdown images ![](url)
- Raw URLs (github.com/user-attachments, imgur.com, etc.)
For EACH image found (in description or comments):
- Download using curl -sL "url" -o /path/to/image.png (GitHub URLs require following redirects) OR the linear mcp
- Use the Read tool on the downloaded file to view it
- Describe what you see in detail
Do NOT skip images - they often contain critical context like error messages, UI states, or configuration

Loom Videos (ALWAYS fetch transcript):

Scan the issue description AND all comments for Loom URLs (loom.com/share/...)
For EACH Loom video found (in description or comments):
- Use the /loom-transcript skill to fetch the FULL transcript
- Summarize key points, timestamps, and any demonstrated issues
Loom videos often contain crucial reproduction steps and context that text alone cannot convey

3. Fetch Related Context

Related Linear Issues:

Use mcp__linear__get_issue for any issues mentioned in relations (blocking, blocked by, related, duplicates)
Summarize how they relate to the main issue

GitHub PRs and Issues:

If GitHub links are mentioned, use gh CLI to fetch PR/issue details:
- gh pr view <number> for pull requests
- gh issue view <number> for issues
Download images attached to issues: curl -H "Authorization: token $(gh auth token)" -L <image-url> -o image.png

Notion Documents:

If Notion links are present, use mcp__notion__notion-fetch with the Notion URL or page ID to retrieve document content
Summarize relevant documentation

4. Review Comments

Comments were already fetched in Step 1. Review them for:

Additional context and discussion history
Any attachments or media linked in comments (process in Step 2)
Clarifications or updates to the original issue description

5. Identify Affected Node (if applicable)

Determine whether this issue is specific to a particular n8n node (e.g. a trigger, action, or tool node). Look for clues in:

The issue title (e.g. "Linear trigger", "Slack node", "HTTP Request")
The issue description and comments mentioning node names
Labels or tags on the issue (e.g. node:linear, node:slack)
Screenshots showing a specific node's configuration or error

If the issue is node-specific:

Find the node type ID. Use Grep to search for the node's display name (or keywords from it) in packages/frontend/editor-ui/data/node-popularity.json to find the exact node type ID. For reference, common ID patterns are:
- Core nodes: n8n-nodes-base.<camelCaseName> (e.g. "HTTP Request" → n8n-nodes-base.httpRequest)
- Trigger variants: n8n-nodes-base.<name>Trigger (e.g. "Gmail Trigger" → n8n-nodes-base.gmailTrigger)
- Tool variants: n8n-nodes-base.<name>Tool (e.g. "Google Sheets Tool" → n8n-nodes-base.googleSheetsTool)
- LangChain/AI nodes: (e.g. "OpenAI Chat Model" → )

Score	Level	Description	Examples
≥ 0.8	High	Core/widely-used nodes, top ~5%	HTTP Request (0.98), Google Sheets (0.95), Postgres (0.83), Gmail Trigger (0.80)
0.4–0.8	Medium	Regularly used integrations	Slack (0.78), GitHub (0.64), Jira (0.65), MongoDB (0.63)
< 0.4	Low	Niche or rarely used nodes	Amqp (0.34), Wise (0.36), CraftMyPdf (0.33)

Include the raw score and the level (high/medium/low) in the summary.

If the node is not found in the popularity file, note that it may be a community node or a very new/niche node.

6. Assess Effort/Complexity

After gathering all context, assess the effort required to fix/implement the issue. Use the following T-shirt sizes:

Size	Approximate effort
XS	≤ 1 hour
S	≤ 1 day
M	2-3 days
L	3-5 days
XL	≥ 6 days

To make this assessment, consider:

Scope of changes : How many files/packages need to be modified? Is it a single node fix or a cross-cutting change?
Complexity : Is it a straightforward parameter change, a new API integration, a new credential type, or an architectural change?
Testing : How much test coverage is needed? Are E2E tests required?
Risk : Could this break existing functionality? Does it need backward compatibility?
Dependencies : Are there external API changes, new packages, or cross-team coordination needed?
Documentation : Does this require docs updates, migration guides, or changelog entries?

Provide the T-shirt size along with a brief justification explaining the key factors that drove the estimate.

7. Present Summary

Before presenting, verify you have completed:

Downloaded and viewed ALL images in the description AND comments
Fetched transcripts for ALL Loom videos in the description AND comments
Fetched ALL linked GitHub issues/PRs via gh CLI
Listed all comments on the issue
Checked whether the issue is node-specific and looked up popularity if so
Assessed effort/complexity with T-shirt size

After gathering all context, present a comprehensive summary including:

Issue Overview : Title, status, priority, assignee, labels
Description : Full issue description with any clarifications from comments
Visual Context : Summary of screenshots/videos (what you observed in each)
Affected Node (if applicable): Node name, node type ID (n8n-nodes-base.xxx), popularity score with level (e.g. 0.64 — medium popularity)
Related Issues : How this connects to other work
Technical Context : Any PRs, code references, or documentation
Effort Estimate : T-shirt size (XS/S/M/L/XL) with justification
Next Steps : Suggested approach based on all gathered context

Notes

The issue ID can be provided in formats like: AI-1975, node-1975, or just 1975 (will search)
If no issue ID is provided, ask the user for one

Weekly Installs

185

Repository

n8n-io/n8n

GitHub Stars

181.1K

First Seen

Feb 18, 2026

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31,600 周安装

@n8n/n8n-nodes-langchain.<camelCaseName>

@n8n/n8n-nodes-langchain.lmChatOpenAi

Look up the node's popularity score from packages/frontend/editor-ui/data/node-popularity.json. Use Grep to search for the node ID in that file. The popularity score is a log-scale value between 0 and 1. Use these thresholds to classify: