Loading...
Loading...
Found 67 Skills
Manage RFC-style specifications with validation, and dynamic generation of history, index, and namings files. Use when validating RFC documents, checking taxonomy compliance, or generating specification indices and terminology references.
Transforms technical documents into rigorous learning journeys with collegiate-level mastery requirements. Uses Bloom's taxonomy progression, 80%+ mastery thresholds, and multi-level verification before advancing. Treats learning as a high school to college graduation progression. Use when user wants deep understanding, not surface familiarity.
Generates structured literature survey reports from collected papers using a multi-stage pipeline: outline generation (query-type adaptive) → draft survey → section-by-section expansion → summary section refinement → final assembly. Produces survey-grade output with taxonomy-based method analysis, LaTeX formalizations, comparative tables, and dense citations. Use when: user wants a literature review, research survey, field overview, or systematic synthesis of multiple papers. Do NOT use for finding/searching papers (use paper-navigator), generating research ideas (use research-ideation), or writing a paper's Related Work section (use paper-writing).
Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
Use when organizing content for digital products, designing navigation systems, restructuring information hierarchies, improving findability, creating taxonomies or metadata schemas, or when users mention information architecture, IA, sitemap, navigation design, content structure, card sorting, tree testing, taxonomy, findability, or need help making information discoverable and usable.
Plan content architecture, editorial calendars, taxonomy, and content audits. Activate for content-heavy projects or when organizing information across pages/sections.
Apply Bloom's revised taxonomy to classify learning objectives and design assessments across six cognitive levels. Use this skill when the user needs to write learning objectives at specific cognitive levels, align assessment with instructional goals, or evaluate curriculum for cognitive complexity distribution — even if they say 'how to write learning objectives', 'what level of thinking does this require', or 'higher-order thinking skills'.
Pre-publish assistant for new blog posts. Use when the user wants to classify a new post with categories and tags, generate SEO metadata (title, description, focus keyphrase), or get intelligent suggestions with rationale. Works with draft content (file path, URL, or text) and suggests from existing taxonomy to maintain balanced distribution.
This skill generates interactive multiple-choice quizzes for each chapter of an intelligent textbook, with questions aligned to specific concepts from the learning graph and distributed across Bloom's Taxonomy cognitive levels to assess student understanding effectively. Use this skill after chapter content has been written and the learning graph exists.
Convert a taxonomy (`outline/taxonomy.yml`) into a bullet-only outline (`outline/outline.yml`) with sections/subsections. **Trigger**: outline builder, bullet outline, outline.yml, 大纲生成, bullets-only. **Use when**: structure 阶段（NO PROSE），已有 taxonomy，需要生成可映射/可写作的章节与小节骨架（每小节≥3 bullets）。 **Skip if**: 已经有批准过且可映射的 outline（避免无意义 churn）。 **Network**: none. **Guardrail**: bullets-only；移除 TODO/模板语句；每小节至少 3 个可检查 bullets。
Generate measurable learning outcomes aligned with Bloom's taxonomy and CEFR proficiency levels for educational content. Use when educators need to define what students will achieve, create learning objectives for curriculum planning, or ensure objectives are specific and testable rather than vague.
Write and audit Python code comments using antirez's 9-type taxonomy. Two modes - write (add/improve comments in code) and audit (classify and assess existing comments with structured report). Use when users request comment improvements, docstring additions, comment quality reviews, or documentation audits. Applies systematic comment classification with Python-specific mapping (docstrings, inline comments, type hints).