AI Agent Skills

Use this to recommend an LLM observability / evaluation tool or stack for a specific situation. Trigger on "which observability tool should I use", "compare Langfuse vs…

Use this to capture user feedback on LLM outputs (thumbs up/down, edits, corrections, implicit signals) and feed it back into observability and evals. Trigger on "add th…

Use this to pick or switch the LLM behind a feature, based on evidence instead of hype or the newest release. Trigger on "which model should I use", "is GPT/Claude/Gemin…

Use this to diagnose WHY an LLM agent or chain produced a wrong, empty, slow, or expensive result, by reading its observability trace. Trigger on "my agent gave the wron…

Use this to detect when an LLM is making things up, so you can flag or block confident-but-wrong answers before users see them. Trigger on "detect hallucinations", "is t…

Use this to build or change an LLM feature the reliable way, by writing evals first and iterating against them, instead of tweaking prompts by vibes. Trigger on "how do…

Use this when adding tracing/observability to an LLM or AI-agent application - capturing prompts, tool calls, token usage, latency, and cost per step. Trigger whenever s…

Use this to measure whether an AI agent actually completed its task end to end, not just whether individual LLM calls looked fine. Trigger on "is my agent working", "mea…

Use this to measure and monitor the quality of a RAG (retrieval-augmented generation) pipeline - whether it retrieves the right context and answers faithfully. Trigger o…

Use this to improve a prompt systematically instead of hand-tweaking it by feel. Trigger on "optimize my prompt", "make this prompt better", "the prompt isn't working we…

Use this when adding LLM observability to an app that handles sensitive data (finance, healthcare, PII) and you must NOT ship raw prompts/PII to a third-party tracing ba…

Use this to cut the cost of an LLM app using observability data. Trigger on "my OpenAI/Anthropic bill is too high", "reduce token usage", "the app is expensive", "optimi…

深度阅读模式：用户给出文章链接且提示词里带「深度」二字（「深度读一下」「这篇走深度」「深度分析」）时触发本 skill、不走 x-post-scheduler 快速流水线。流程是互动式的：细读全文 → 输出总结 + 观点讨论底稿 → 与用户多轮讨论、校准立场 → 定稿后排成深度读后感长推或 3~7 条线程（经 Buffer），超长内容可…

热梗雷达 + 原创二创短推：从热榜或用户丢来的热帖提取「梗点」（绝不搬运原文），以程序员 / AI 从业者人设写原创短段子，原帖有图时提取梗点让 Codex 全新生成二创图（原帖无图则纯文字），人工确认后经 Buffer 发布到 X。触发场景：用户说「来条段子」「今天有什么梗」「这个帖子二创一下」「玩个梗」，或丢来微博/即刻/社交平台的…

API testing from OpenAPI/Swagger or case schemas: parameters, boundaries, auth, idempotency, concurrency, error responses, data consistency; runnable scripts; k6 handoff…

对已确认的 Bug 做根因定位、影响分析、回归建议时使用——复现 → 读代码定位根因 → 影响五面分析 → 回归建议，条目（根因/影响/Severity 依据/修复建议/回归建议五个扩展字段）落盘为 Bug 条目。不用于：仅收集 Bug 证据（automated-e2e-testing / api-testing）、疑似未定性缺陷（te…