Search

PythonReactSEOPDF
GitHub Skill

literate-writer

Guide coding agents to write code, tests, and documentation as a single coherent narrative in clear natural language prose, with identifiers that read as grammatically correct English.

zfifteen☆ 1 Repository stars0 Repository forks

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

View source ↗Guides

Skill instructions

Literate Writer

This skill trains coding agents to treat all code as a readable story rather than a bag of symbols. When active, it reshapes naming, tests, comments, and structure so a human can skim the codebase as if reading plain English prose.

When to use this skill

  • The user asks for “literate”, “narrative”, or “prose-like” code, or references the EDE Chop Shop style.
  • The task involves designing APIs, algorithms, or refactors where human readability is a primary concern.
  • The code will be read or maintained by researchers, students, or collaborators who benefit from self-explanatory naming and structure.

Core principles to apply

  1. Code as narrative

    • Treat every file as a chapter of a story about behavior and intent, not just mechanics.
    • Prefer straightforward control flow and explicit logic over clever tricks that obscure the narrative.
  2. Identifiers as sentences

    • Choose method names that read like natural language actions at the call site, such as findPositionOf, listThePrimesFor, sortInPlace.
    • Use descriptive variable names like remainingValue, leftBoundary, candidateDivisor that make loops and conditionals read as prose.
    • Name classes as clear nouns such as FactorFinder, MergeSorter, GraphTraverser; constants use UPPER_SNAKE_CASE like MAX_ITERATIONS, DEFAULT_THRESHOLD.
    • Avoid cryptic abbreviations except those that are universally recognized (e.g., URL, ID).
  3. Tests as the opening chapter

    • Begin by writing tests that describe behavior in natural language: method names like shouldFindAllPrimeFactorsOfCompositeNumber or shouldReturnEmptyListWhenSearchingEmptyGraph.
    • Let tests serve as both specification and narrative outline; implementation then “fills in” the story so all tests read as true statements.
  4. Documentation as connective tissue

    • Provide JavaDoc or equivalent docstrings for all methods, public and private, explaining purpose, parameters, return values, and side effects in clear prose.
    • Keep documentation concise but complete, ensuring a reader can reconstruct intent from the docs plus the identifiers without external context.
  5. Quality guardrails

    • Guard aggressively against mathematical errors, logical flaws, and unnecessary inefficiency while preserving readability.
    • Reject code that “reads like gibberish”; prefer slightly more verbose, explicit constructs when they improve clarity for future readers.

How an agent should behave with this skill

When this skill is active, the agent should:

  • Start by sketching tests or usage examples that read like natural language specifications, then design APIs and implementations to satisfy them.
  • Consistently favor names and structures that form readable sentences at call sites and within control flow blocks.
  • Add or refine documentation to maintain a smooth narrative through the codebase, aligning text and identifiers.
  • During review or refactoring tasks, point out places where the story is unclear and propose renamings or restructurings that restore narrative flow.

Examples

Read the usage examples in the source repository.

View source ↗

Source history

Indexed on
2026-01-20
Index updated
2026-01-30
Branch
main

Index and sync dates describe this directory. View the source repository for code history.

GitHub ↗

chat Comments (0)

chat_bubble_outline

No comments yet. Be the first to share your thoughts!