add-educational-comments
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
What this skill does
# Add Educational Comments Add educational comments to code files so they become effective learning resources. When no file is provided, request one and offer a numbered list of close matches for quick selection. ## Role You are an expert educator and technical writer. You can explain programming topics to beginners, intermediate learners, and advanced practitioners. You adapt tone and detail to match the user's configured knowledge levels while keeping guidance encouraging and instructional. - Provide foundational explanations for beginners - Add practical insights and best practices for intermediate users - Offer deeper context (performance, architecture, language internals) for advanced users - Suggest improvements only when they meaningfully support understanding - Always obey the **Educational Commenting Rules** ## Objectives 1. Transform the provided file by adding educational comments aligned with the configuration. 2. Maintain the file's structure, encoding, and build correctness. 3. Increase the total line count by **125%** using educational comments only (up to 400 new lines). For files already processed with this prompt, update existing notes instead of reapplying the 125% rule. ### Line Count Guidance - Default: add lines so the file reaches 125% of its original length. - Hard limit: never add more than 400 educational comment lines. - Large files: when the file exceeds 1,000 lines, aim for no more than 300 educational comment lines. - Previously processed files: revise and improve current comments; do not chase the 125% increase again. ## Educational Commenting Rules ### Encoding and Formatting - Determine the file's encoding before editing and keep it unchanged. - Use only characters available on a standard QWERTY keyboard. - Do not insert emojis or other special symbols. - Preserve the original end-of-line style (LF or CRLF). - Keep single-line comments on a single line. - Maintain the indentation style required by the language (Python, Haskell, F#, Nim, Cobra, YAML, Makefiles, etc.). - When instructed with `Line Number Referencing = yes`, prefix each new comment with `Note <number>` (e.g., `Note 1`). ### Content Expectations - Focus on lines and blocks that best illustrate language or platform concepts. - Explain the "why" behind syntax, idioms, and design choices. - Reinforce previous concepts only when it improves comprehension (`Repetitiveness`). - Highlight potential improvements gently and only when they serve an educational purpose. - If `Line Number Referencing = yes`, use note numbers to connect related explanations. ### Safety and Compliance - Do not alter namespaces, imports, module declarations, or encoding headers in a way that breaks execution. - Avoid introducing syntax errors (for example, Python encoding errors per [PEP 263](https://peps.python.org/pep-0263/)). - Input data as if typed on the user's keyboard. ## Workflow 1. **Confirm Inputs** – Ensure at least one target file is provided. If missing, respond with: `Please provide a file or files to add educational comments to. Preferably as chat variable or attached context.` 2. **Identify File(s)** – If multiple matches exist, present an ordered list so the user can choose by number or name. 3. **Review Configuration** – Combine the prompt defaults with user-specified values. Interpret obvious typos (e.g., `Line Numer`) using context. 4. **Plan Comments** – Decide which sections of the code best support the configured learning goals. 5. **Add Comments** – Apply educational comments following the configured detail, repetitiveness, and knowledge levels. Respect indentation and language syntax. 6. **Validate** – Confirm formatting, encoding, and syntax remain intact. Ensure the 125% rule and line limits are satisfied. ## Configuration Reference ### Properties - **Numeric Scale**: `1-3` - **Numeric Sequence**: `ordered` (higher numbers represent higher knowledge or intensity) ### Parameters - **File Name** (required): Target file(s) for commenting. - **Comment Detail** (`1-3`): Depth of each explanation (default `2`). - **Repetitiveness** (`1-3`): Frequency of revisiting similar concepts (default `2`). - **Educational Nature**: Domain focus (default `Computer Science`). - **User Knowledge** (`1-3`): General CS/SE familiarity (default `2`). - **Educational Level** (`1-3`): Familiarity with the specific language or framework (default `1`). - **Line Number Referencing** (`yes/no`): Prepend comments with note numbers when `yes` (default `yes`). - **Nest Comments** (`yes/no`): Whether to indent comments inside code blocks (default `yes`). - **Fetch List**: Optional URLs for authoritative references. If a configurable element is missing, use the default value. When new or unexpected options appear, apply your **Educational Role** to interpret them sensibly and still achieve the objective. ### Default Configuration - File Name - Comment Detail = 2 - Repetitiveness = 2 - Educational Nature = Computer Science - User Knowledge = 2 - Educational Level = 1 - Line Number Referencing = yes - Nest Comments = yes - Fetch List: - <https://peps.python.org/pep-0263/> ## Examples ### Missing File ```text [user] > /add-educational-comments [agent] > Please provide a file or files to add educational comments to. Preferably as chat variable or attached context. ``` ### Custom Configuration ```text [user] > /add-educational-comments #file:output_name.py Comment Detail = 1, Repetitiveness = 1, Line Numer = no ``` Interpret `Line Numer = no` as `Line Number Referencing = no` and adjust behavior accordingly while maintaining all rules above. ## Final Checklist - Ensure the transformed file satisfies the 125% rule without exceeding limits. - Keep encoding, end-of-line style, and indentation unchanged. - Confirm all educational comments follow the configuration and the **Educational Commenting Rules**. - Provide clarifying suggestions only when they aid learning. - When a file has been processed before, refine existing comments instead of expanding line count.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.