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 skills add grasscaograss/AwesomeWeldoneSkills --skill weldone-animationgit 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/weldone-animation)<a href="https://agentmods.dev/skills/grasscaograss/awesomeweldoneskills/weldone-animation"><img src="https://agentmods.dev/badge/skills/grasscaograss/awesomeweldoneskills/weldone-animation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/grasscaograss/awesomeweldoneskills/weldone-animation"><img src="https://agentmods.dev/badge/skills/grasscaograss/awesomeweldoneskills/weldone-animation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00189 | $0.02411 |
| Opus 5 | $0.00095 | $0.01205 |
| Sonnet 5 | $0.00038 | $0.00482 |
| Haiku 4.5 | $0.00019 | $0.00241 |
Grade A, and why
weldone-animation 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 10d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gen-ls:PF + 粗定位 → 规划 → LS 验证
命令定位
gen-ls(src/Weldone.Cli/Commands/GenLsCommand.cs)是路径仿真模块(Pages/PathSimulation/)的 CLI 等价物:输入 PF 文件 + 粗定位位姿,跑完整生产级规划流水线并产出 Fanuc LS 脚本。不 Mock 设备、不走 FSM 执行,纯规划链路。
规划流水线(每个 craft 逐条执行):
- 外部轴规划 —
PlanManager.ScanWeldExternalPlan(data, craftId, isDual) - 焊接路径规划 —
WeldRobotAppService.WeldPathPlan/WeldPathPlanDualArm - 过渡规划 —
PlanManager.WeldTransitionPlan/WeldTransitionDualArmPlanForCraft - 逐道次生成 LS —
WeldRobotAppService.GenerateLSFile/DualArmGenerateLSFile
任一步失败会打 失败/跳过 日志,不中断后续 craft。
调用方式
justfile 没有为 gen-ls 配别名,直接用 dotnet 跑 CLI 项目(工作目录 src/Weldone):
dotnet run --project src/Weldone.Cli/Weldone.Cli.csproj -- gen-ls --pf "<PF路径>" --coarse "<粗定位>" [选项]
注意:
ExecuteInteractiveAsync(交互式菜单入口)被显式禁用,注释为"需要 ArmTransStateDict 支持,CLI 流程暂未适配"。只能通过命令行参数调用,不要走just interactive菜单。
参数
| 参数 | 缩写 | 必填 | 说明 |
|---|---|---|---|
--pf |
-p |
是 | PF 文件路径(.pf JSON,即 ProcessTransfer 序列化) |
--coarse |
-c |
否 | 粗定位矩阵,缺省=单位矩阵。三种格式见下 |
--user-frame |
否 | 用户坐标系矩阵(同 --coarse 格式),缺省=单位矩阵 |
|
--dual |
否 | 双机模式(默认单臂) | |
--post-mode |
否 | 双机后处理模式 Async/Sync/Hybrid,默认 Async |
|
--verify |
-v |
否 | 生成完成后扫描 --dir 下 LS 文件,解码 GP3 外部轴点位 |
--dir |
-d |
否 | --verify 的扫描/输出目录,默认 Documents\weldone |
--coarse 三种格式(ParseMatrix 解析,按顺序尝试)
- JSON 文件路径:16 元素 float 数组,行主序
M11..M44[1,0,0,0, 0,1,0,0, 0,0,1,0, 100,200,300,1] - CoarsePositionningContextDto JSON 文件:生产环境
ProductionData目录下的ModelBase_*.json。内部从GeneralCoarseResults(JSON 字符串) →workpiece_results[0].model_base_result.transform_4x4提取m11..m44。最贴近现场粗定位产物的格式。 - inline 16 个逗号分隔浮点数:行主序
--coarse "1,0,0,0,0,1,0,0,0,0,1,0,100,200,300,1"
矩阵含义:粗定位是完整 4×4 位姿矩阵(含姿态),不是单纯 XYZ 位置。规划内部会乘
UserFrame的逆(coarseMatrix * userFrameInvert)得到工件在用户坐标系下的位姿。
典型用法
1. 单臂 + 现场粗定位文件验证规划(最常见)
dotnet run --project src/Weldone.Cli/Weldone.Cli.csproj -- gen-ls `
--pf "D:\data\task.pf" `
--coarse "D:\data\ModelBase_xxx.json"
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.
- 10d ago First seen · 137 lines · 189 tokens per session scan A 39b1c3113927
weldone-animation is a skill published in the GitHub repository grasscaograss/AwesomeWeldoneSkills (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 189 tokens to every session and 2,411 once invoked, about $0.0009 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.
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