Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Guide for AI agents running in the isolated agent-harness environment. Use when you need to discover your agent ID, find your ports, manage your stack with agent-cli.sh, run verification, or understand the multi-agent development setup.
Multi-agent workflows — Review Cycle, Discussion, and Research Review Round. Read the Briefing subagents rule before delegating to any subagent; read a mode in full only when the user asks for it by name.
Interact with Mission Control — AI agent orchestration dashboard. Use when registering agents, managing tasks, syncing skills, or querying agent/task status via MC APIs.
CCG Skills - Quality gates, documentation generator, and multi-agent orchestration. Auto-installed by CCG workflow system.
Create, refine, and optimize high-quality YAML prompts for AI assistants. Use when working with prompt templates, system prompts, agent prompts, or any prompt engineering tasks. Provides structure guidelines, template patterns, and quality standards for YAML-based prompts.
提议并执行 rubric 或 bucket 升级。两种模式：**完整 rubric bump**（最高风险动作，5 步强制 + 跨模型审核）和 **--bucket-only 轻量重校**（只换 bucket 边界，不动 rubric 公式）。**Phase 2 强制走 cheat-score-blind sub-agent 给校准池重打分**——不接受 self-scored fallback。触发词："升级 rubric"/"bump rubric"/"更新公式"/"我想加一个维度"/"调整权重"/"重校桶"/"recalibrate bucket"。
cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环，唯一区别是"发过视频的人"会在 init 时多一步：抓取已有视频建立历史 context（用于后续 cheat-seed 给更贴合的选题、更准的 baseline）。触发词："初始化"/"init"/"首次使用"/"我是新用户"/"setup cheat-on-content"。**必须在用户第一次会话执行；其他子 skill 在 .cheat-state.json 不存在时自动路由到此。**
INTERNAL sub-agent for blind 7-dim rubric scoring. **NOT a user-facing skill — do NOT invoke from main conversation.** Called via Task tool by cheat-score / cheat-predict / cheat-bump to get a context-isolated score on a script. Receives ONLY script_path + rubric_notes_path; refuses any other input. Outputs strict JSON: 9 dimensions × {score 0-5, confidence enum, one-line reason}. **Hard refuses to Read** .cheat-state.json, predictions/*, retro 段, or anything that could leak post-publish data. This is channel B in the 3-channel calibration model (A=main, B=blind sub, C=cross-model).
Interactive wizard that walks service teams through creating a package-specific skill for their Azure SDK package. Scans the package, detects customization patterns, scaffolds a SKILL.md with references, and validates with vally lint. The skill is placed inside the package's .github/skills/ directory so find-package-skill discovers it automatically. WHEN: create package skill; add service skill; bootstrap skill for package; new package skill; skill for my SDK package; write skill for search; write skill for cosmos.
Create and test a classify-and-route Azure AI Content Understanding pipeline for packets that contain multiple document types (e.g. invoice + bank statement + loan application in one PDF). Walks per-type schema authoring → outer classifier wiring → batch test → category-aware stdout summary using the typed ContentUnderstandingClient. Use when the user has mixed-document packets.