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Found 172 Skills
Test coverage verification for refactoring. Apply when verifying existing test coverage, identifying gaps, recommending pre-refactoring tests, and defining verification checkpoints.
Orchestrator for the complete talk preparation pipeline (REX or Concept mode). Runs all 6 stages in sequence with human-in-the-loop checkpoints.
Stage 4 — Strategic angles, titles, descriptions, peer feedback draft. Includes mandatory CHECKPOINT before script can start.
Initialize a new workspace by copying the standard artifact template (STATUS.md, CHECKPOINTS.md, UNITS.csv, DECISIONS.md + folders). **Trigger**: workspace init, initialize workspace, workspace template, 初始化 workspace. **Use when**: 启动任何 pipeline run（必须先有 workspace 工件与目录骨架）。 **Skip if**: workspace 已初始化且不希望覆盖既有文件（除非显式 `--overwrite`）。 **Network**: none. **Guardrail**: 不要修改 `.codex/skills/workspace-init/assets/` 模板；默认不覆盖已有文件。
CRITICAL: Use for agent-spec CLI tool workflow. Triggers on: agent-spec, contract, lifecycle, guard, verify, explain, stamp, checkpoint, spec verification, task contract, spec quality, lint spec, run log, "how to verify", "how to use agent-spec", "spec failed", "guard failed", contract review, contract acceptance, PR review, code review workflow, 合约, 验证, 生命周期, 守卫, 规格检查, 质量门禁, 合约审查, "验证失败", "怎么用 agent-spec", "spec 不通过", "工作流"
Expert GPU optimization for modern consumer GPUs (8-24GB VRAM). Use this skill when you need to optimize GPU training, speed up CUDA code, reduce OOM errors, tune XGBoost for GPU, migrate NumPy to CuPy, make a model faster, manage GPU memory, optimize VRAM usage, or benchmark PyTorch. Covers mixed precision, gradient checkpointing, XGBoost GPU acceleration, CuPy/cuDF migration, vectorization, torch.compile, and diagnostics. NVIDIA GPUs only. PyTorch, XGBoost, and RAPIDS frameworks.
Craft model-specific prompts optimized for the target checkpoint and identity method. Handles FLUX, SDXL, SD1.5, and Wan video models with proper syntax, quality tags, and negative prompts. Use when generating or refining prompts for ComfyUI workflows.
Use this skill when building real-time data pipelines, stream processing jobs, or change data capture systems. Triggers on tasks involving Apache Kafka (producers, consumers, topics, partitions, consumer groups, Connect, Streams), Apache Flink (DataStream API, windowing, checkpointing, stateful processing), event sourcing implementations, CDC with Debezium, stream processing patterns (windowing, watermarks, exactly-once semantics), and any pipeline that processes unbounded data in motion rather than data at rest.
This skill implements a specific task from a project's ROADMAP.md file. It should be used when the user wants to work on a roadmap action item by its ID (e.g., '1.1', '2.3'). Triggered by requests like '/do-task 1.1', '/do-task 2.3', or 'do task 3.1'. Works alongside the project-init skill (which creates the roadmap) and the checkpoint skill (which commits afterward).
Chain patterns for CC 2.1.71 pipelines — MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building multi-phase pipeline skills. Loaded via skills: field by pipeline skills (fix-issue, implement, brainstorm, verify). Not user-invocable.
Trigger: Called when a task is completed, enters phase acceptance, receives critical feedback, or repeated similar errors require systematic correction; common signals include review, audit, retrospective, quality check, error correction and retrospective. Trigger after delivery or at a review checkpoint when quality must be examined honestly and errors must be corrected without defensiveness. Use this skill for structured self-review, feedback processing, and continuous correction.
Use when executing implementation plans. Dispatches independent subagents for individual tasks with code review checkpoints between iterations for rapid, controlled development.