Browse reusable Agent Skills, each with a clear purpose and practical guidance.
根据代码、Ansible、Helm chart、验证脚本和现有设计文档,更新 `docs/00_设计文档/` 下受影响的 Markdown 文档。Use when a design change, deployment flow change, middleware change, runtime dependency change, or documentation correction should land in one or more design docs.
编排 `docs/00_设计文档/` 下的复杂设计文档更新、拆分、补图和回收。Use when requests mention 设计文档、架构图、部署图、时序图、组件边界、依赖矩阵、统一更新多份设计文档, or when a design change impacts more than one file under `docs/00_设计文档/`.
生成结构化演示文稿:产出大纲、每页标题与要点、演讲者备注,并导出为 Markdown 幻灯片(
分析故事情节点，识别关键情节与转折点。适用于深度分析情节结构、评估情节发展有效性
分析故事文本，提炼主要情节点并分析戏剧功能。适用于分析小说、剧本大纲、故事梗概等文本，识别关键转折点和情感节点
Integrate multiple plot point analysis results into comprehensive reports, generating high-quality analysis through deduplication, classification, sorting, and summarization. Suitable for integrating multiple analysis sources, generating unified reports
Build a clean per-dataset summary table/CSV for a datagen (trajectory-generation) campaign — one row per task source with Status (COMPLETED / FAILED / RUNNING / NOT STARTED), N Trials Completed, Mean Turns/Trace, Mean Tok/Trace, Mean Reward, and the HF trace-repo link. Use when asked to "summarize the campaign", "which datasets did we complete + their rewards/trials", "build a completion table/CSV", or to reconcile a prose tracker into auditable per-dataset metrics. Computes metrics by STREAMING each uploaded HF trace dataset (disk-bounded) and reusing the canonical OT-Agent analysis tools (scripts/analysis/utils.py: extract_conversation_text / count_turns / extract_reward) + the Qwen3-8B tokenizer; HF-ground-truths Status by probing each repo. Reference impl: scripts/analysis/build_campaign_summary_csv.py. Related skills: analyze-dataset-token-length (token method), analyze-job-history-iris (harbor Mean / trials from logs).
Analyze the token length of an OT-Agent conversation-format (ShareGPT-style) dataset — the per-trace distribution (median/p90/max) and/or counts under a token threshold + a metadata predicate (e.g. "task_complete AND < 32768 tokens"). Use when asked how long traces are, how many fit a context window (32k/131k), or to filter a trace dataset by length + a field. Uses the OT-Agent analysis tools + the Qwen3-8B tokenizer. Runs LOCALLY on the Mac (no GPU); full-dataset tokenization of ~10k multi-turn traces takes a few minutes → run it in the background.
How to reference code from sample repos in Agent Framework docs pages using :::code directives, snippet tags, zone pivots, and highlight attributes.