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Found 18 Skills
An intelligent assistant designed for long-form online novel creation, supporting full-process management from setting generation to main text writing, including intelligent quality control and state synchronization. (Yunshu Optimized)
Conduct a targeted code exploration of the repository, and document the process of "Ask Questions → Read Code → Draw Conclusions" as searchable evidence for direct reuse when similar questions arise next time. There are three types: question (investigate code around a specific question and provide conclusions), module-overview (sort out the structure, boundaries, entry points, and dependencies of a module), and spike (conduct lightweight technical exploration of multiple possible directions without making final decisions). Trigger scenarios: Users say "Let's explore first", "How is X implemented in this repository", "Quickly get familiar with this module", "Archive the exploration results". Refer to `codestable/reference/system-overview.md` for how to distinguish it from learning / tricks / decisions.
Use when you need to create new skills or update existing ones, integrate and package domain knowledge, workflows, scripts and tools into reusable Skills; the newly generated skills must be written in Chinese. Trigger words: create new skill, update skill, create new skill package, expand AI capabilities.
Conduct targeted code exploration on a repository, and document the process of "Asking Questions → Reading Code → Reaching Conclusions" as searchable evidence for direct reuse when similar questions arise next time. There are three types: question (investigate code around a specific problem and provide conclusions), module-overview (organize the structure, boundaries, entry points, and dependencies of a module), spike (conduct lightweight technical exploration of multiple possible directions without making final decisions). Trigger scenarios: When users say "Let's explore first", "How is X implemented in this repository", "Quickly get familiar with this module", "Archive the exploration results". For the distinction from learning / tricks / decisions, refer to the root skill `easysdd`.
Enter this sub-process when conducting code optimization — handle tasks where 'behavior remains unchanged, structure changes' (structure / performance / readability). Shift single-module internal optimization from 'AI random refactoring' to 'first scan to generate a checklist, confirm each item with the user, execute step-by-step according to the method library, and require manual approval for each step'. Trigger scenarios: Users mention phrases like 'optimize it / refactor / rewrite / split it / poor performance / code is too long' without any accompanying behavior changes. Do not handle new requirements (route to feature), bugs (route to issue), or cross-module architecture restructuring (route to architecture + decisions).
Standardize the article editing process to ensure clear modification scope, trackable progress, and documented changes. Use this skill when the user says "edit article", "revise article", "adjust content", or "modify this piece".
Generate project-level AGENTS.md guides that capture conventions, workflows, and required follow-up tasks. Use when a repository needs clear agent onboarding covering structure, tooling, testing, task flow, README expectations, and conventional commit summaries.
驗證規格檔案的完整性與一致性，確保所有必要的規格元素都已定義且符合標準格式。
When writing complex features or significant refactors or user ask explicitly, use an ExecPlan from design to implementation.
Phase 1 of the feature workflow — Draft a design document for the new feature, which serves as the sole input for subsequent implementation and acceptance. First gather evidence (read architecture docs, review relevant code, grep to prevent term conflicts, check archives), then write a complete first draft in one go (including YAML frontmatter + three-layer structure + test design), submit it to the user for overall review, and iterate until approval. After approval, extract {slug}-checklist.yaml from {slug}-design.md for use in the next two phases. Trigger scenarios: "Start designing the solution", "Write design doc", "Prepare to implement XX", with the prerequisite that you already know what to do, who it's for, and how to define success.
Shared kernel design workflow across all supported languages and DSLs. Provides language selection table, naming conventions, versioning rules, KernelPlan structure, composition patterns, clone workflow, implementation workflow, devlog template, and designer output contract. Use when: (1) choosing which language-specific kernel design skill to load, (2) the intended implementation language is not fixed yet, (3) you need naming or versioning guidance before selecting a DSL, (4) you are implementing any kernel regardless of DSL, (5) you are updating docs that refer to kernel design skills.
Turn approved storyboard logic, beat sheets, or prompt plans into provider-ready short-form video requests. Use this when the segment structure is already known and you need a model-agnostic request architecture that can later map cleanly into Seedance or other video generators.