Announced December 8, 2025 by Anthropic engineer @catherinewu
MCP servers load full tool definitions into the system prompt at session start. Power users with multiple MCPs (supabase, tabz, shadcn, docker-mcp, etc.) can burn 40-50k tokens before typing anything.
The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.
The correct way of creating a private frok by duplicating the repo is documented here.
For this assignment the commands are:
git clone --bare git@github.com:usi-systems/easytrace.git
| 0000000: 5249 4646 0ce0 0d00 5741 5645 666d 7420 RIFF....WAVEfmt | |
| 0000010: 3400 0000 feff 0200 44ac 0000 953e 0000 4.......D....>.. | |
| 0000020: e802 0000 2200 0008 0300 0000 bfaa 23e9 ....".........#. | |
| 0000030: 58cb 7144 a119 fffa 01e4 ce62 0100 285c X.qD.......b..(\ | |
| 0000040: 0000 0000 0000 0000 6661 6374 0800 0000 ........fact.... | |
| 0000050: 0025 2600 9009 0000 736d 706c 3c00 0000 .%&.....smpl<... | |
| 0000060: 0000 0000 0000 0000 9458 0000 3c00 0000 .........X..<... | |
| 0000070: 0000 0000 0000 0000 0000 0000 0100 0000 ................ | |
| 0000080: 1800 0000 0000 0000 0000 0000 9009 0000 ................ |
| name | web-asset-reload |
|---|---|
| description | Use this skill when working on the ESP-IDF firmware (or any ESP-IDF web UI) and questions arise about how already-open browser pages detect front-end (HTML/JS/CSS) changes and decide whether to reload after an OTA or reflash. It covers both deployment models (assets embedded in the firmware binary vs published on a flash filesystem), the build-time or runtime asset content hash, the assets field in /api/state and /api/stream, the bootAssets comparison in the web UI, why reload keys on the asset hash rather than version/build, how to add or change a served asset, and the test-1 / test-2 validation procedure. |
本 skill 说明 ESP-IDF 设备如何让其已打开的网页在前端资源（HTML/JS/CSS/ICO）
| Product Year Version Product Keys | |
| Visual Studio 2026 18.x | |
| Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK | |
| Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V | |
| https://x.com/massgravel/status/1988306014371008542 | |
| Visual Studio 2022 2021 17.x Professional: | |
| TD244-P4NB7-YQ6XK-Y8MMM-YWV2J | |
| Enterprise: |
"3 step" + a link to a paper/article/report is standing shorthand for: read the full source (not just abstract or preview), then write one continuous summary in three layers of increasing length. Layer 1 is one paragraph: the whole piece at maximum compression, a complete stopping point on its own. Layer 2 is two paragraphs that continue from Layer 1 without restating it, adding the next tier of detail (mechanism, key evidence, how the result was produced). Layer 3 is three paragraphs that continue from Layer 2 (methodology specifics, numbers, context, limitations, implications). Every prefix (1, 1+2, 1+2+3) must read as a self-contained summary at its depth. Mark the three layers visibly so the reader can stop at a boundary: a short bold label on its own line before each layer ("Layer 1", "Layer 2", "Layer 3"). No other headers or formatting inside the layers. Nothing is repeated across layers; each only adds. Compression is the point; analysis serves it. Write in plain, direct sentences for a smart reader o
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
本文是对开源项目 Flue —— The Agent Harness Framework（TypeScript）的一次源码级技术分析， 基于对仓库某一 commit 的阅读整理。涉及 Cloudflare 平台行为与计费的部分属平台知识， 具体数字请以 Cloudflare 官网当期定价为准。部分细节（如 202 后台保活）在文中如实标注为「待核」。
Flue 自我定位是 "The Agent Harness Framework"：一个 runtime-agnostic 的 TypeScript 框架，
用来构建 headless / 可编程 的 agent（"像 Claude Code，但 100% 无头、可编程，没有 TUI、没有人类操作员假设"）。
把 agent/workflow 源码编译成可部署的服务端产物（Node 单文件 .mjs，或 Cloudflare Workers + Durable Objects）。
大部分 agent「逻辑」活在 Markdown 里（skills、AGENTS.md、context）。
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title></title> | |
| <meta charset="utf-8" /> | |
| <script src="Scripts/jquery-1.9.1.min.js"></script> | |
| <link href="Content/bootstrap.min.css" rel="stylesheet" /> | |
| <script src="Scripts/isRockFx.js"></script> | |
| <script> | |
| $(function () { |
