Use this skill when the user asks to "analyze my content", "learn my writing style", "research competitors", "find content angles", "improve my blog", "write like me", "embody my brand voice", or mentions content strategy, voice analysis, competitive research, or iterative content improvement.
中文简介正在准备中。当前显示原文说明。
Design and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.
中文简介正在准备中。当前显示原文说明。
Design and evaluate context compression strategies for long-running agent sessions. Use when agents exhaust memory, need to summarize conversation history, or when optimizing tokens-per-task rather than tokens-per-request.
中文简介正在准备中。当前显示原文说明。
End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.
中文简介正在准备中。当前显示原文说明。
Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.
中文简介正在准备中。当前显示原文说明。
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
中文简介正在准备中。当前显示原文说明。
How to test the peoplereadme CLI end-to-end, including the fidelity harness (eval/calibrate) with a synthetic persona fixture and live LLM calls.
中文简介正在准备中。当前显示原文说明。
Set up and end-to-end test the peoplereadme CLI (init, validate, schema changes). Use when verifying changes to src/peoplereadme/, schemas/persona.schema.json, or persona package scaffolding.
中文简介正在准备中。当前显示原文说明。
Generates a BACKPRESSURE.md file for any project. Analyzes codebase and PRD to produce deterministic self-verification checks that AI coding agents run on their own output.
中文简介正在准备中。当前显示原文说明。