Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/grasscaograss/awesomeweldoneskills/gitlab-issuenpx skills add grasscaograss/AwesomeWeldoneSkills --skill gitlab-issuegit clone --depth 1 https://github.com/grasscaograss/AwesomeWeldoneSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/grasscaograss/awesomeweldoneskills/gitlab-issue)<a href="https://agentmods.dev/skills/grasscaograss/awesomeweldoneskills/gitlab-issue"><img src="https://agentmods.dev/badge/skills/grasscaograss/awesomeweldoneskills/gitlab-issue.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.02052 |
| Opus 5 | $0.00032 | $0.01026 |
| Sonnet 5 | $0.00013 | $0.00410 |
| Haiku 4.5 | $0.00006 | $0.00205 |
Grade A, and why
gitlab-issue scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitLab Issue 协作创建
引导开发人员明确 Issue 内容，自动推断标签，通过 API 创建。
GitLab 配置
| 配置项 | 值 |
|---|---|
| URL | http://gitlab.roboticplus.com:2022 |
| Project ID | 305 |
| API Base | http://gitlab.roboticplus.com:2022/api/v4/projects/305 |
| Token | 环境变量 $env:GITLAB_TOKEN 或用户提供 |
Token 检查（步骤 0）
每次开始工作前，使用 pwsh 7 会话（默认 UTF-8）检查 $env:GITLAB_TOKEN 是否已设置：
if ($env:GITLAB_TOKEN) { 'Token exists' } else { 'Token not set' }
⚠️ 必须使用 pwsh 7。如果通过外层 shell 调用，使用
pwsh -NoProfile -Command并用单引号包住脚本，避免$env:被外层 shell 展开。
- 已设置（输出
Token exists）→ 直接进入步骤 1 - 未设置 → 引导用户创建并配置：
- 告诉用户：当前未配置 GitLab Token，需要创建一个才能操作 Issue
- 引导用户打开
http://gitlab.roboticplus.com:2022/-/profile/personal_access_tokens，创建一个 Personal Access Token，勾选api权限 - 让用户在终端执行以下命令设置环境变量（永久生效）：
# 在 pwsh 7 profile 中持久化 Add-Content -Path $PROFILE -Value "`n`$env:GITLAB_TOKEN = '<用户粘贴的token>'" $env:GITLAB_TOKEN = '<用户粘贴的token>' - 或者让用户在 Claude Code 中直接输入
! $env:GITLAB_TOKEN = '<token>'来临时设置 - 设置完成后继续步骤 1
注意：不要自己生成或猜测 Token 值，必须由用户提供。
工作流程
步骤 1：理解意图
从用户的描述中提取：
- 类型：根据语义自动判断
- 初步内容：问题/需求的核心描述
类型判断规则：
| 用户表达 | 类型 |
|---|---|
| 崩溃、报错、异常、不对、不生效、失败了、bug | 类型::缺陷 |
| 新功能、需要、能不能、想要、需求、支持 | 类型::功能 |
| 优化、改进、太慢、不好用、重构、改善 | 类型::优化 |
| 重构、整理、清理、迁移 | 类型::重构 |
步骤 2：引导补充内容
根据类型，引导用户补充关键信息（用自然对话方式，不要一次性抛出所有问题）。
类型::缺陷
引导获取（按顺序，已有信息跳过）：
- 问题现象：具体表现是什么？
- 复现步骤：怎么触发这个问题？
- 期望行为：应该是什么样子？
- 影响范围：哪些场景受影响？现场/研发？
- 环境信息：分支、工件版本（可选）
生成正文格式：
## 问题现象
{描述}
## 复现步骤
1. {步骤1}
2. {步骤2}
## 期望行为
{描述}
## 实际行为
{描述}
## 影响范围
{描述}
类型::功能
引导获取：
- 需求背景：为什么要做？谁提出？
- 功能描述：具体要实现什么？怎么使用？
- 验收标准：做到什么程度算完成？
生成正文格式：
## 需求背景
{描述}
## 功能描述
{描述}
## 验收标准
- [ ] {标准1}
- [ ] {标准2}
类型::优化 / 类型::重构
引导获取：
- 现状：当前有什么问题？
- 目标：改进后达到什么效果？
- 影响范围：涉及哪些模块？需要注意什么？
步骤 3：推断模块
从用户描述中匹配关键词，自动推断模块标签：
| 关键词 | 模块 |
|---|---|
| 焊缝、打断、合并、WSG、WeldSeamGroup | 模块::焊缝规划 |
| 模板、工艺、参数、焊道 | 模块::焊接模板 |
| 粗定位、点云、匹配、coarse | 模块::粗定位 |
| 精定位、视觉、拍照、fine、iLaser | 模块::精定位 |
| 扫描、校准、MapMatrix、calibration | 模块::扫描校准 |
| 机器人、外部轴、驱动、E1、E2 | 模块::机器人控制 |
| 双臂、包角、协同、wrapping | 模块::双臂协同 |
| 状态机、FSM、流程、工作流 | 模块::状态机 |
| 前端、Blazor、React、页面、UI、组件 | 模块::前端 |
| 插件、ZMJ、AGV、Rhino | 模块::插件 |
| CI、构建、迁移、发布、electron | 模块::基础设施 |
| 产能、统计、焊丝、消耗 | 模块::产能统计 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 203 lines · 65 tokens per session scan A dcbaf9af03cb
gitlab-issue is a skill published in the GitHub repository grasscaograss/AwesomeWeldoneSkills (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 65 tokens to every session and 2,052 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…