Use when the user requests integration testing, feature validation, or test plan execution
中文简介正在准备中。当前显示原文说明。
Restate content as a flat list of atomic facts in the style of av/facts fact sheets. Use when the user says "just facts", "facts only", "as facts", "give me the facts", or replies with a bare "facts" / "facts?" to a previous message — then the scope is that message's content.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Restate content as facts, in the style of av/facts fact sheets (github.com/av/facts). The output is a flat list of atomic claims: short enough to read in full, complete enough that nothing in scope is lost.
One fact per line, - prefixed. Group with # headings only when there are 2+ distinct domains; skip headings for short outputs. No intro, no outro, no transitions, no "in summary". The fact list IS the message.
# billing
- MRR is $48.2k, up 6% MoM [stripe, 2026-08-25]
- 3 enterprise deals in contract stage
- Brain Restore Centers closed at $500/mo
# risks
- churn concentrated in workspaces without custom skills
- VIK-2058 unassigned until 07:20Z, now with Ivan
- vendor claims fix ships Friday (unverified). Never upgrade a guess to a fact.[source, YYYY-MM-DD] for volatile data.- Viktor recommends X because Y).- not covered in source: X.还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Use when the user requests integration testing, feature validation, or test plan execution
中文简介正在准备中。当前显示原文说明。
Use when the user wants to systematically fix AI code slop — duplicated logic, over-engineering, silent error swallowing, convention drift, cargo-cult patterns, and other LLM-introduced architectural decay — over a specified duration
中文简介正在准备中。当前显示原文说明。
Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.
中文简介正在准备中。当前显示原文说明。
Runs autonomous keep/discard experiments on a codebase to optimize a single metric for a fixed duration, in the style of karpathy/autoresearch. Use when the user says "autoresearch" (optionally with a focus, e.g. "autoresearch the optimizer"), asks to run experiments on a repo overnight, to hill-climb or optimize a metric autonomously, or points at a repo with a karpathy-style program.md.
中文简介正在准备中。当前显示原文说明。
Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.
中文简介正在准备中。当前显示原文说明。
Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.
中文简介正在准备中。当前显示原文说明。