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开发系统最极致高效的Agents.md，没有之一： # AGENTS.md ## Core Principles - Choose the simplest implementation that fully satisfies the current requirements. Avoid unnecessary abstraction, configuration, indirection, or speculative extensibility. - Make the smallest necessary change that fixes the root cause. Do not refactor unrelated modules or change strategy semantics unless explicitly requested. - Grow the system in layers. Start from the smallest working end-to-end version and add new capabilities incrementally. Never replace a working system with unfinished complexity. - Reuse existing project components before creating new ones. Prefer extending proven modules over introducing parallel implementations. - Prefer well-maintained libraries when they reduce overall complexity or improve reliability. Do not reimplement common functionality without a clear benefit. - Keep components modular with clearly defined responsibilities. Avoid unnecessary coupling between strategy logic, execution, accounting, replay, and infrastructure. - Design for long-term maintainability once a feature or strategy has been validated. Do not over-engineer speculative ideas before evidence exists. --- ## Strategy Development - Validate hypotheses with historical replay before introducing forward-only logic whenever historical validation is possible. - Every trading strategy must progress through Replay → Shadow → Canary → Live. Do not skip validation stages. - Base design decisions on measurable evidence rather than intuition. Optimize only after demonstrating that an edge exists. - Treat every strategy as an independent contract. Do not silently alter frozen behavior without explicit authorization. --- ## Existing Systems - Do not break running Shadow or Live systems for unrelated work. - Preserve compatibility only when required by active production or validation workflows. Otherwise, remove obsolete code instead of accumulating compatibility layers. - Reuse existing infrastructure whenever possible, including replay engines, accounting, execution, wallet management, order book handling, logging, monitoring, and daemon frameworks. --- ## Engineering Standards - Prefer deterministic behavior over hidden automation. - Fail loudly when assumptions are violated. Do not silently ignore errors or fall back to unexpected behavior. - Keep configuration minimal. Introduce new configuration only when behavior genuinely needs to vary. - Remove dead code instead of leaving unused paths behind. - Write code that is easy to inspect, replay, test, and reason about. - Keep implementation consistent with existing project architecture unless an architectural change is explicitly requested. --- ## Scope Discipline - Implement only the requested scope. - Do not introduce unrelated optimizations, redesigns, migrations, or feature expansions. - Non-blocking findings outside the requested scope may be noted separately but must not be merged into the current task. - Consider a task complete once its agreed acceptance criteria are satisfied. Treat subsequent improvements as separate work items.

mattpocock/skills v1.2 is out! We're now the 19th most-starred repo of all time. 13.5m downloads on skills​.sh. Thanks for your support! Here's what's new: - Docs: the community's biggest ask. Every skill documented, with explanations of the main flows + troubleshooting - Claude Plugin: install via Claude's official marketplace - Codex Support: full Codex support via agents/openai.yaml files Updated Skills: - /grilling now asks you questions in rounds, not one-by-one - /prototype now uses HTML instead of a TUI for building logic prototypes - easier to share and far richer - /writing-for-agents renamed from /writing-great-skills, use it for ANYTHING your agents read (AGENTS.md, system prompts, docs) New Skills: - /wizard: tired of provisioning infra? Get your agent to build you a TUI to walk you through it - /to-questionnaire: hit a grilling question you can't answer? Turn the session into a doc you can walk through on a call with a colleague - /wait-what: no idea what the model said? Refocus it in your domain language and simplify with ASD-STE100 Full changelog + docs below. Video soon!

introducing anydoc now your agents get 100x faster local parsing for pdf, docx, pptx & 10 more formats - sub-5ms md conversion - 500 docx files in 1.7s - top quality across all 13 formats - rust-based - open source already powering @firecrawl /parse

太它妈方便了，多年的程序员都不想写自动化了 微软开源了个有意思的东西，叫 Skill Recorder。 你只要录一遍自己怎么完成一个任务——它会会采集鼠标操作、窗口切换、网页、剪贴板、终端命令和语音讲解——录完之后通过 GitHub Copilot CLI 直接把这套流程重建成一个 SKILL.md，定好自动化。 任务意图 → 通用步骤 → SKILL.md → 定时自动化 说白了它不是传统 RPA 那套靠录屏重放像素坐标，而是把你的网页点击转成更稳定的 API、CLI 或 Agent 原生工具。 举个例子，你演示一次处理 GitHub Issue，它可以基于这条录制生成一个能够批量处理同类 Issue 的 Agent Skill。 目前原生支持 Microsoft Scout 和 Microsoft 365 Copilot Cowork，暂时还不直接支持 Claude Code、Codex 和 OpenCode，但生成的 SKILL.md 可以手动适配。 它代表的是 Agent 工作流的一个方向：不是教 AI 怎么做，而是直接做给 AI 看。 想试的自己扒下来跑一遍，GitHub 872 星还很新，项目还在快速迭代。

今天要看一个 bug 录屏， 挺长的不想看 看到一个 skills 让 agent 去看视频流程感觉不错 用 yt-dlp 下载视频 获取 Transcript，平台自带字幕/ MLX-Whisper 或者whisper.cpp 兜底 如果需要视觉，用 ffmpeg 按场景抽帧，把帧 + 对应时间段的 transcript 一起发给 Agent，来生成汇总的 生成 moments.md 和 summary.md 最后可以按需要写入自己的知识库里面。 我这个场景它一下就很清楚的描述了 bug 的详细信息，我再给其他 agent 去修就好了。 我觉得用 agent 看视频，挺好的，尤其是适合这种沟通类或者学习类的场景