Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/grasscaograss/AwesomeWeldoneSkillsnpx agentmods add skills/grasscaograss/awesomeweldoneskills/archive-sessionWrote 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/archive-session)<a href="https://agentmods.dev/skills/grasscaograss/awesomeweldoneskills/archive-session"><img src="https://agentmods.dev/badge/skills/grasscaograss/awesomeweldoneskills/archive-session/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/archive-session"><img src="https://agentmods.dev/badge/skills/grasscaograss/awesomeweldoneskills/archive-session.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 | $0.00069 | $0.01750 |
| Opus 5 | $0.00034 | $0.00875 |
| Sonnet 5 | $0.00014 | $0.00350 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
archive-session 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 9d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Archive Session — 会话归档
Quick start
用户完成一轮设计+实现后，Agent 收集变更、生成 record、提取可复用知识、按领域路由写入，更新索引。
领域路由表
知识文件写入前，必须根据主题判断归入哪个领域文件夹。下表按匹配优先级排列（优先匹配更具体的领域）：
| 领域文件夹 | 含义 | 典型关键词 |
|---|---|---|
dual-arm/ |
双臂系统 | DualArm、双臂协同、Leader/Follower、起收弧时序、双机过渡段 |
weld-template/ |
焊接模板 | WeldTemplate、模板匹配、包角方向、操作日志 |
weld-seam/ |
焊缝规划 | WeldSeam、规划过滤、ILIdx 分组、后处理、几何类型、PoseTValue |
coarse-positioning/ |
粗定位 | CoarseVision、粗定位、龙门补偿矩阵、排单初始化 |
scanning/ |
精定位与扫描 | ScanTarget、PrecisePositioning、VCM、推扫、拍照点、FineLoc |
capacity/ |
产能统计 | 产能统计、清枪计数、操作日志事件 |
weld-tracking/ |
焊接跟踪 | WeldTracking、TrackingMode、焊缝跟踪传感器 |
coordinate/ |
坐标与矩阵 | Coordinate、MapMatrix、标定、PhantomType、IRobotCoordinateService |
workflow/ |
状态机与工作流 | StateMachine、FSM、PoseRole、TransitionPlan、工件持久化、排单 |
frontend/ |
前端界面 | Blazor、React、Three.js、面板、渲染、UI 交互 |
device-robot/ |
设备与机器人 | Robot、Fanuc、FTP、TCP、设备配置 |
tools/ |
工具与其他 | CLI、几何参数、空气墙、文件浏览器、不属于以上任何领域的通用工具 |
路由规则：一个知识条目只归入一个领域。如果跨领域，拆成多个知识文件。无法判断时归入 tools/。
Workflow
1. 收集变更
并行执行以下操作：
git log --oneline自上次 record 日期（或用户指定起点）git diff archive/CONTEXT.md查看术语变更- 检查
docs/adr/是否有新增或修改的 ADR - 从 git diff 总结关键文件变更
2. 确定 slug
询问用户："这个会话用什么 slug 概括？"（kebab-case，例如 transition-plan-refactor）。
用户没有想法时，根据收集到的变更自行拟定，让用户确认或修改。
3. 生成 Record
创建 archive/records/YYYY-MM-DD-<slug>.md。
Record 模板（保持不变）：
# <Title>
> **TL;DR**: <one-line summary> `keyword1` `keyword2` — <what changed>
## Background
<为什么做这件事。未来读者需要的上下文。>
## Decisions
<做了什么决定。关键取舍。反直觉的选择。>
## Results
<实际改了什么。文件、模块、行为。>
## Legacy
<遗留项、后续工作、有意推迟的事情。>
参考 archive/records/ 中的已有记录作为风格参考。
4. 检测可复用知识 & 领域路由
分析本次会话是否产出了可复用的技术知识（模式、规则、配置、架构约束——未来开发会需要的东西）。
判断标准：
- 如果只是 bug 修复或一次性调整，不需要知识文件
- 如果涉及架构模式、设计规则、接口约定、算法细节，则提取
对每条可复用知识：
- 领域路由：根据上面「领域路由表」判断应归入哪个领域文件夹
- slug 定名：用 kebab-case，简短但能区分同领域内其他文件
- 判断新建还是更新：
- 检查目标领域文件夹下是否已有同名或高度相关的知识文件
- 已有 → 建议更新（扩展内容）
- 没有 → 新建
多知识产出：一个会话可能涉及多个领域。对每个领域独立判断，产出多个知识文件。例如一次重构同时涉及 coordinate/ 和 workflow/，应产出两个知识文件。
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.
- 9d ago First seen · 176 lines · 69 tokens per session scan A dc1d0a744313
archive-session is a skill published in the GitHub repository grasscaograss/AwesomeWeldoneSkills (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 69 tokens to every session and 1,750 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.
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