create-skill
Guide for creating new Claude Code skills. Use when you need to create a new skill to package expertise or workflow into a reusable capability that Claude can automatically invoke.
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
formatting-build-output
Use /usr/local/bin/xcsift for all Swift and Xcode build commands. Pipe xcodebuild build, xcodebuild test, swift build, swift test through xcsift to get structured TOON output with compile errors, warnings, test failures, code coverage, slow tests, and build timing. Essential for parsing Xcode build output, diagnosing compile errors, analyzing test results, and measuring code coverage in any Swift or Xcode project.
add-new-entry
Workflow and tools for adding new entries from temp.md to the section files. Includes legend format, section reference, code tools, and common pitfalls. USE FOR: Adding new resources to the knowledge base. DO NOT USE FOR: Editing existing entries or restructuring sections.
update-llm-pool
Workflow for updating the LLM landscape paper pool (section/x_llm_papers.md) using fetch_llm_papers.py. Covers full re-fetch, resume from checkpoint, and adding new topics. USE FOR: Refreshing citation counts, expanding topic coverage. DO NOT USE FOR: Adding hand-curated entries to section files (use add-new-entry), updating RAG/Agent citation sections in best_practices.md (use update-cite-count).
plugin-maintenance
Guide for modifying the Chorus plugin, updating skill documentation, and releasing new plugin versions.
tutorial-module-writer
Write the tutorial content (`output/TUTORIAL.md`) from an approved module plan, including exercises and answer outlines. **Trigger**: write tutorial, tutorial modules, 教程写作, TUTORIAL.md. **Use when**: tutorial pipeline 的写作阶段（C3），且 `DECISIONS.md` 已记录 HUMAN 对 scope/running example 的批准（C2）。 **Skip if**: module plan 未完成/未批准（先跑 `module-planner`/`exercise-builder` 并通过 Approve C2）。 **Network**: none. **Guardrail**: 只写已批准范围；保持 running example 一致；每模块包含练习与答案要点。
module-planner
Plan tutorial modules from a concept graph, including module objectives and sequencing, saving as `outline/module_plan.yml`. **Trigger**: module plan, tutorial modules, course outline, 模块规划, module_plan.yml. **Use when**: tutorial pipeline 的结构阶段（C2），已有 `outline/concept_graph.yml`，需要把概念依赖转成可教学的模块序列。 **Skip if**: 还没有 concept graph（先跑 `concept-graph`）。 **Network**: none. **Guardrail**: 每模块明确 objectives + outputs（最好含 running example 步骤）；避免 prose 段落。
tutorial-spec
Define tutorial scope, target audience, prerequisites, learning objectives, and a running example; output a tutorial spec for downstream planning. **Trigger**: tutorial spec, scope, audience, prerequisites, learning objectives, running example, 教程规格. **Use when**: tutorial pipeline 的起点（C1），需要先锁定教学目标与边界，再进入 concept graph / module planning。 **Skip if**: 你不是在做教程产出（或已经有明确且不允许改动的 tutorial spec）。 **Network**: none. **Guardrail**: 结构化 spec 优先；避免提前写长教程 prose（prose 在 C3）。
concept-graph
Build a concept graph (nodes + prerequisite edges) from a tutorial spec, saving as `outline/concept_graph.yml`. **Trigger**: concept graph, prerequisite graph, dependency graph, 概念图, 先修关系. **Use when**: tutorial pipeline 的结构阶段（C2），需要把教程知识点拆成可排序的依赖图（在写教程 prose 前）。 **Skip if**: 还没有 tutorial spec（例如缺少 `output/TUTORIAL_SPEC.md`）。 **Network**: none. **Guardrail**: 只做结构；避免写长 prose 段落。
taxonomy-builder
Build a 2+ level taxonomy (`outline/taxonomy.yml`) from a core paper set and scope constraints, with short descriptions per node. **Trigger**: taxonomy, taxonomy builder, 分类, 主题树, taxonomy.yml. **Use when**: survey/snapshot 的结构阶段（NO PROSE），已有 `papers/core_set.csv`，需要生成可映射且读者友好的主题结构。 **Skip if**: 已经有批准过且可映射的 taxonomy（不要无意义重构）。 **Network**: none. **Guardrail**: 避免泛化占位桶；保持 2+ 层且每节点有具体描述。
evidence-selfloop
Evidence self-loop for surveys: read evidence bindings + evidence packs, then write an actionable upstream TODO plan (which stage/skill to fix) before writing more prose. Writes `output/EVIDENCE_SELFLOOP_TODO.md`. **Trigger**: evidence self-loop, evidence loop, evidence gaps, binding gaps, blocking_missing, 证据自循环, 证据缺口回路. **Use when**: C4 outputs exist (`outline/evidence_bindings.jsonl`, `outline/evidence_drafts.jsonl`) but writing looks hollow or C5 is BLOCKED due to thin evidence. **Skip if**: you are still pre-C3 (no notes/evidence bank yet), or you want to draft anyway and accept a lower evidence bar. **Network**: none. **Guardrail**: analysis-only; do not edit evidence/writing artifacts; do not invent facts/citations; only write the TODO report.
terminology-normalizer
Normalize terminology across a draft (canonical terms + synonym policy) without changing citations or meaning. **Trigger**: terminology, glossary, consistent terms, 术语统一, 统一叫法, 术语表. **Use when**: the draft has concept drift (same thing called 2–3 names) or global-review flags terminology inconsistency. **Skip if**: you are still changing the outline/taxonomy heavily (do that first). **Network**: none. **Guardrail**: do not add/remove citation keys; do not introduce new claims; avoid moving citations across subsections.
redundancy-pruner
Remove repeated boilerplate across sections (methodology disclaimers, generic transitions, repeated summaries) while preserving citations and meaning. **Trigger**: redundancy, repetition, boilerplate removal, 去重复, 去套话, 合并重复段落. **Use when**: the draft feels rigid because the same paragraph shape and disclaimer repeats across many subsections. **Skip if**: you are still drafting major missing sections (finish drafting first). **Network**: none. **Guardrail**: do not add/remove citation keys; do not move citations across subsections; do not delete subsection-specific content.
template-reference-first
Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`. **Skip if**: the task is a one-off workflow that will not be reused as a skill. **Network**: none. **Guardrail**: keep domain knowledge and writing exemplars out of `run.py`; make reference loading explicit; do not ship reader-facing placeholder text.
ce-compound-refresh
Refresh stale or drifting learnings and pattern docs in docs/solutions/ by reviewing, updating, consolidating, replacing, or deleting them against the current codebase. Use after refactors, migrations, dependency upgrades, or when a retrieved learning feels outdated or wrong. Also use when reviewing docs/solutions/ for accuracy, when a recently solved problem contradicts an existing learning, when pattern docs no longer reflect current code, or when multiple docs seem to cover the same topic and might benefit from consolidation.
ce-compound
Document a recently solved problem to compound your team's knowledge
section-bindings
Bind papers to chapter-level sections first, writing `outline/section_bindings.jsonl` and `outline/section_binding_report.md`. **Trigger**: section bindings, chapter bindings, section-first binding, 章节绑定, 章级绑定. **Use when**: survey structure should measure chapter saturation before stable H3 decomposition. **Skip if**: chapter skeleton is missing or the bindings are already refined. **Network**: none. **Guardrail**: NO PROSE; do not invent papers; produce auditable PASS/BLOCKED/REROUTE signals.