Code to coach
Skill MarieLynneBlock/arcanum-artifex/skills/study-support/code-to-coach
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Deconstruct code into bite-sized, logical chunks from a coaching perspective. Remove abstraction, explain the "why" behind each concept, identify best practices, code smells, and cognitive load issues. Use structured frameworks to provide thorough, pedagogical analysis that teaches *how to think* about code, not just what it does.
SKILL.md
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Code-to-Coach Skill
Overview
This skill transforms code analysis into a teaching and coaching dialogue. Instead of explaining what code does, it:
- Deconstructs into logical units (functions, classes, patterns, business logic)
- Removes abstraction by connecting to concrete examples and intent
- Explains intent β the "why" behind each decision
- Identifies patterns β recognises idioms, best practices, and smells
- Highlights cognitive load β pinpoints confusing areas and suggests clearer structures
- Teaches thinking patterns β helps the learner develop intuition about code structure
When to Use This Skill
Use this skill when:
- A learner wants to understand how to think about a codebase, not just understand its current form
- You need to identify why code is difficult to maintain or extend
- You're reviewing code with an educational goal
- You want to recognise anti-patterns and suggest improvements with justification
- You need to break down complex logic into teachable chunks
- You're preparing code for code review, documentation, or mentoring
Workflow: The Five-Step Coaching Cycle
Step 1: Orient β Scope and Context
- Ask: What is this code's job? Who calls it? What problem does it solve?
- Look for: Business purpose, entry/exit points, dependencies
- Output: 1β2 sentences of clear context (remove jargon)
- Tool: Use context-discovery.md
Step 2: Deconstruct β Break Into Chunks
- Split the code into logical units using the deconstruction-model.md
- Identify: Functions, loops, conditionals, state mutations, side effects, business rules
- Name each chunk with a verb phrase (e.g., "Validate email format", "Fetch user from database")
- Remove jargon: Translate technical terms to intent (e.g., "memoization" β "cache results to avoid recalculation")
- Output: Annotated code or flowchart showing chunks and their relationships
Step 3: Analyse β Evaluate Quality Using the Rubric
- Apply the analysis-rubric.md to each chunk:
- Clarity & naming
- Single responsibility
- Error handling & assumptions
- Testability & dependencies
- Performance & resource use
- Cognitive load (nesting depth, variable scope, mutability)
- Identify code smells from code-smells.md
- Note best practices from best-practices.md
- Output: Strengths, concerns, and refactoring opportunities with rationale
Step 4: Teach β Explain the Why
- For each chunk, answer:
- Why was this pattern chosen?
- What problem does it solve?
- What trade-offs were made? (simplicity vs. performance, flexibility vs. maintainability, etc.)
- How would a learner think about this decision?
- Reference: Use cognitive-load.md to frame clarity issues
- Suggest: Better names, clearer logic flow, or simpler patterns with rationale, not just opinion
- Output: Coached explanation that builds intuition
Step 5: Advise β Next Steps
- Prioritise refactoring opportunities using refactoring-priorities.md
- Provide specific, actionable suggestions (with code examples if helpful)
- Link to best-practices.md or language-specific resources
- Output: Numbered, actionable items ranked by impact and effort
Templates & Guides
- Coaching Review Template β Structured format for delivering feedback
- Analysis Rubric β Criteria for evaluating code quality
- Deconstruction Model β How to break code into teachable units
- Teaching Script β Skeleton for explaining code to a learner
Reference Materials
- Code Smells β Anti-patterns and their symptoms
- Best Practices β Language-agnostic principles for maintainability
- Cognitive Load β How to spot and reduce mental friction
- Context Discovery β Questions to understand a codebase
Key Principles
- No jargon without context β Every technical term connects to intent and trade-offs
- Chunking first β Before analysis, divide into logical units a learner can hold in their head
- Why over what β Explain design decisions, not syntax
- Empathy β Assume the code was written with good intent; surface the constraints
- Actionable β Every suggestion includes rationale and effort estimate
- Teach thinking, not rules β Help learners develop intuition, not memorise dos and don'ts
Success Criteria
A successful coaching session:
- β Learner understands the purpose of each chunk
- β Learner can articulate why the code is structured this way
- β Learner identifies why certain areas are hard to understand
- β Learner has actionable suggestions for improvement, ranked by impact
- β Learner can apply the same thinking to unfamiliar code
Constraints & Limits
- Language scope: Apply coaching to any language; adjust examples and idioms accordingly
- Scale: Works best on functions or classes (up to 100β200 lines). For larger codebases, apply recursively to modules/services
- Assumptions: Assumes the code is the real implementation, not pseudocode or incomplete logic
- No guessing: If business context is missing, flag it and ask (don't assume intent)
Next Steps
- Choose a piece of code (function, class, or module)
- Orient yourself using context-discovery.md
- Deconstruct using the deconstruction-model.md
- Analyse against the analysis-rubric.md
- Teach using the teaching-script.md skeleton
- Advise using the coaching-review-template.md format
Gives 0 of the 12 instructions most learn study skills give in ~1.5k tokens
Counted across 546 of the 573 authors here whose files we hold, read 2026-08-07
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- set min_dist to 0.0 for clustering preprocessingin 16 of 546, across 6 files
- populate the mission file before teachingin 15 of 546, across 1 file
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Said here and by no other author read
- break code into logical chunks
- translate technical terms to intent
- apply the analysis rubric to each chunk
- explain why each pattern was chosen
- provide actionable refactoring suggestions with rationale
- assume code was written with good intent
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.