Literate Programming Skill Skill

Literate Programming Skill

CRITICAL: This skill MUST be activated BEFORE making any changes to .nw files!

You are an expert in literate programming using the noweb system.

Reference Files

This skill includes detailed references in references/:

| File | Content | Search patterns | |------|---------|-----------------| | noweb-commands.md | Tangling, weaving, flags, troubleshooting | notangle, noweave, -R, -L | | testing-patterns.md | Test organization, placement, dependency testing | test functions, pytest, after implementation | | git-workflow.md | Version control, .gitignore, pre-commit | git, commit, generated files | | multi-directory-projects.md | Large project organization, makefiles | src/, doc/, tests/, MODULES | | project-initialization.md | New project setup, templates, checklist | new project, initialize, pyproject.toml | | preamble.tex | Standard LaTeX preamble for documentation | \usepackage, memoir |

When to Use This Skill

Correct Workflow

User asks to modify a .nw file
YOU ACTIVATE THIS SKILL IMMEDIATELY
You plan the changes with literate programming principles
You make the changes following the principles
You regenerate code with make/notangle

Anti-pattern (NEVER do this)

User asks to modify a .nw file
You directly edit the .nw file ← WRONG
Later review finds literate quality problems
You have to redo everything

Remember

.nw files are NOT regular source code files
They combine documentation and code for human readers
Literate quality is AS IMPORTANT as code correctness
Bad literate quality = failed task, even if code works

Planning Changes

When making changes to a .nw file:

Read the existing file to understand structure and narrative
Plan with literate programming in mind:
- What is the "why" behind this change?
- Why does this approach work, not just why was it chosen?
- How does this fit into the existing narrative?
- What new chunks are needed? What are their meaningful names?
- Where in the pedagogical order should this be explained?
Design documentation BEFORE writing code:
- Write prose explaining the problem and solution
- Use subsections to structure complex explanations
Decompose code into well-named chunks:
- Each chunk = one coherent concept
- Names describe purpose, not syntax (like pseudocode)
Write the code chunks
Regenerate and test

Key principle: If you find yourself writing code comments to explain logic, that explanation belongs in the documentation chunks instead.

Reviewing Literate Programs

When reviewing, evaluate:

Narrative flow: Coherent story? Pedagogical order?
Variation theory: Contrasts used? "Whole, parts, whole" structure?
Chunk quality: Meaningful names? Focused on single concepts?
Explanation quality: Explains "why" not just "what"? The explanation should also say why the chosen approach works. Red flags: prose that begins "We [verb] the [noun]" matching a function name; prose that describes parameter types visible in the signature; prose that restates conditionals without explaining why they matter; prose that says an approach is better without explaining the mechanism, invariant, or constraint that makes it work.
Test organization: Tests after implementation, not before?
Proper noweb syntax: [[code]] notation in prose? Identifiers in chunk titles escaped with [[...]]? Valid chunk references?

Core Philosophy

Literate programming (Knuth) has two goals:

Explain to human beings what we want a computer to do
Present concepts in order best for human understanding (psychological order, not compiler order)

Variation Theory

Apply variation-theory skill when structuring explanations:

Contrast: Show what something IS vs what it is NOT
Separation: Start with whole (module outline), then parts (chunks)
Generalization: Show pattern across different contexts
Fusion: Integrate parts back into coherent whole

CRITICAL: Show concrete examples FIRST, then state general principles. Readers cannot discern a pattern without first experiencing variation.

Noweb File Format

Documentation Chunks

Begin with @ followed by space or newline
Contain explanatory text (LaTeX, Markdown, etc.)
Copied verbatim by noweave

Code Chunks

Begin with <<chunk name>>= on a line by itself (column 1)
End when another chunk begins or at end of file
Reference other chunks using <<chunk name>>
Multiple chunks with same name are concatenated

Syntax Rules

Quote code in documentation using [[code]] (escapes LaTeX special chars). Never manually escape characters (e.g. \_) inside [[...]] — noweb handles all escaping automatically. Writing [[\_]] double-escapes.
Escape: @<< for literal <<, @@ in column 1 for literal @

Writing Guidelines

Start with the human story - problem, approach, design decisions
Introduce concepts in pedagogical order - not compiler order
Use meaningful chunk names - 2-5 word summary of purpose (like pseudocode)
Escape all identifiers in chunk names — any identifier (variable, function, parameter, attribute) that appears in a chunk title must be wrapped in [[...]]. This tells noweave to render it as code (monospace) and prevents LaTeX errors from underscores.

BAD — bare identifier, underscore breaks LaTeX:
```
<<restrict to top-level when not show_tree>>=
if not show_tree:
    ...
@
```
GOOD — identifier escaped with [[...]]:
```
<<restrict to top-level when not [[show_tree]]>>=
if not show_tree:
    ...
@
```
Other examples: <<add graders to [[graders]] list>>, <<initialise [[default_username]]>>.

IMPORTANT: Do not escape underscores or other special characters inside [[...]]. The brackets already tell noweb to escape everything. [[__init__.py]] is correct; [[\_\_init\_\_.py]] is wrong and will double-escape.
Decompose by concept, not syntax
Explain the "why" - don't just describe what the code does. Prose that merely restates the code in English teaches nothing. Good prose explains why a design choice was made and why this approach works: what alternative was rejected, what property makes the chosen approach effective, what would break without it, or what constraint drives the implementation.

Self-test: If your prose could be mechanically generated from the function signature, it's "what" not "why." Ask yourself: What design decision does this paragraph justify? What alternative did we reject and why? Why does the chosen approach work here? If the paragraph doesn't answer those questions, rewrite it.

BAD — prose restates code in English:
```
\subsection{Counting $n$-grams}

We count overlapping $n$-grams.
If $n$ is larger than the input, the result is empty.

<<functions>>=
def ngram_counts(text, *, n):
    ...
@
```
GOOD — prose explains why this design choice:
```
\subsection{Counting $n$-grams}

We use overlapping $n$-grams because they capture all positional
contexts---in \enquote{THE}, overlapping bigrams yield TH and HE,
whereas non-overlapping would only yield TH.  This matches the
standard definition used in cryptanalysis.

<<functions>>=
def ngram_counts(text, *, n):
    ...
@
```
Red flags that prose is "what" not "why":
- Begins "We [verb] the [noun]" where the verb matches a function name
- Describes parameter types or return values already in the signature
- Restates conditional logic ("If X, we do Y") without explaining why X matters

Keep chunks focused — one function per <<functions>>= chunk with prose before it. Each function (or small group of tightly related functions) gets its own <<functions>>= chunk preceded by explanatory prose. Never put multiple unrelated functions in a single chunk.

BAD — four functions crammed into one chunk with minimal prose:

\subsection{Helper Functions}

We provide several utility functions.

<<functions>>=
def normalize_text(text): ...

def letters_only(text): ...

def key_shifts(key): ...

def index_of_coincidence(text): ...
@

GOOD — each function with its own subsection and prose:

\subsection{Text Normalization}

Before analysis, we strip non-alphabetic characters and
convert to lowercase so that frequency counts are meaningful.

<<functions>>=
def normalize_text(text): ...
@

\subsection{Index of Coincidence}

The index of coincidence measures how likely two randomly
chosen letters from a text are identical ...

<<functions>>=
def index_of_coincidence(text): ...
@

Decompose long functions into named sub-chunks — If a function has more than ~25 lines and contains two or more distinct algorithmic phases, decompose it into named sub-chunks. Each sub-chunk name should read like a step in an algorithm description. The prose before each sub-chunk explains why that phase works the way it does and what property of the data or algorithm makes the approach succeed. This is the classic Knuth technique.

BAD — 80-line function with one line of prose:

We generate plaintext by concatenating sentences.

<<functions>>=
def generate_plaintext(size, *, sources, seed=None):
    """..."""
    if size <= 0:
        raise ValueError(...)
    paragraphs = extract_paragraphs(sources, ...)
    ...  # 75 more lines
    return normalize(prefix, options)
@

GOOD — function body decomposed into named sub-chunks with prose:

<<functions>>=
def generate_plaintext(size, *, sources, seed=None):
    """..."""
    <<prepare filtered paragraphs>>
    <<pick random starting point>>
    <<collect sentences until target length>>
    <<select closest sentence boundary>>
@

We extract paragraphs from the corpus, removing headings and ToC
entries.  Paragraphs lacking sentence-ending punctuation are
discarded---they are typically list items or table rows.

<<prepare filtered paragraphs>>=
if size <= 0:
    raise ValueError("size must be positive")
...
@

To avoid always starting at the beginning of the corpus, we
rotate to a random paragraph.

<<pick random starting point>>=
rng = random.Random(seed)
...
@

Decompose classes by method — Introduce the class shell in one chunk with a placeholder like <<repo methods>>, then define each method in its own \section or \subsection with prose + method chunk + tests. This is the class-level analogue of guideline 7 (one function per chunk) and guideline 8 (decompose long functions). The class shell gives the reader the whole picture; the method sections fill in each part with full explanations and verification.

BAD — entire class in one chunk:

\section{The Repository}

<<classes>>=
class Repo:
    def __init__(self, path):
        ...

    def save(self, data):
        ...

    def load(self, key):
        ...
@

\section{Tests}
<<test functions>>=
def test_save(): ...
def test_load(): ...
@

GOOD — class shell + one section per method, tests distributed:

\section{The Repository}

The class needs two operations: saving and loading.  We introduce
the class shell here and fill in each method in its own section.

<<classes>>=
class Repo:
    def __init__(self, path):
        self.path = pathlib.Path(path)

    <<repo methods>>
@

\subsection{Saving data}

We serialise to JSON because students can inspect the files
manually, unlike a binary format.

<<repo methods>>=
def save(self, data):
    """Persist ``data`` to disk as JSON."""
    ...
@

Let's verify that saving round-trips correctly:

<<test functions>>=
def test_save_creates_file(tmp_path):
    repo = Repo(tmp_path)
    repo.save({"key": "value"})
    assert (tmp_path / "data.json").exists()
@

\subsection{Loading data}

We return [[None]] for missing keys rather than raising, because
a missing key is a normal condition during first run.

<<repo methods>>=
def load(self, key):
    """Load data for ``key``, or ``None`` if absent."""
    ...
@

<<test functions>>=
def test_load_missing_returns_none(tmp_path):
    repo = Repo(tmp_path)
    assert repo.load("absent") is None
@

The chunk name <<repo methods>> is a bucket chunk scoped to the class: noweb concatenates all definitions, so each <<repo methods>>= adds another method to the class body. Choose a name that reflects the class (e.g. <<iolog methods>>, <<stream capture methods>>).

Apply the same pattern to test classes. If <<test functions>>= introduces class Test...:, put only the class shell there and delegate the body to a class-specific bucket chunk such as <<feature a test methods>>. Do not concatenate indented test methods directly into <<test functions>>=; nested scopes are clearer and less error-prone when they use their own bucket chunk.

Use bucket chunks — distribute <<constants>>= near their relevant code - Define each constant in the section where it is conceptually relevant. Never group all constants into a single \subsection{Constants}.

IMPORTANT: When a constant exists only to support one helper or one small cluster of related helpers, place its <<constants>>= bucket immediately adjacent to the bucket that contains those helpers. Do not hide configuration keys in one distant global block if the reader only needs them to understand one local section.

Preferred pattern — prose, then helper bucket, then local constants bucket for that helper family:

We read the debug flag from configuration rather than the process
environment so detached hooks and interactive commands see the same
value.

<<helper functions>>=
def track_debug_enabled():
    return config.get(TRACK_DEBUG_CONFIG)
@

<<constants>>=
TRACK_DEBUG_CONFIG = "track.debug"
@

This keeps the constant close enough to the narrative that readers meet it when they need it, without forcing them to search a giant global constants block.

BAD — all constants dumped in one subsection:

\subsection{Constants}

<<constants>>=
DATA_DIR = ...        % used in loading section
GUTENBERG_START = ... % used in extraction section
SENTENCE_RE = ...     % used in sentence splitting section
KEEP_PUNCT = ...      % used in normalization section
@

GOOD — each constant near the code that uses it:

\subsection{Loading Texts}

<<constants>>=
DATA_DIR = Path(__file__).parent / "data"
@

<<functions>>=
def load_text(path): ...
@

\subsection{Extracting Body Text}

<<constants>>=
GUTENBERG_START = "*** START OF"
GUTENBERG_END = "*** END OF"
@

<<functions>>=
def extract_body(text): ...
@

Define constants for magic numbers - never hardcode values
Co-locate dependencies with features - feature's imports in feature's section
Never leave prose inside an open code chunk — When inserting local <<constants>>= buckets or explanatory paragraphs, first close the current code chunk with @. Documentation between two function sections must be outside code mode; otherwise noweb tangles the prose into Python and produces syntax errors.

BAD — prose accidentally tangled as Python:
```
<<helper functions>>=
def get_default_daily_limit():
    ...

This helper uses the daily-limit config key.

<<constants>>=
DEFAULT_DAILY_LIMIT_CONFIG = "track.daily_limit"
@
```
GOOD — close the code chunk before prose, then reopen a bucket:
```
<<helper functions>>=
def get_default_daily_limit():
    ...
@

This helper uses the daily-limit config key.

<<constants>>=
DEFAULT_DAILY_LIMIT_CONFIG = "track.daily_limit"
@
```
Prefer public functions - Default to making functions public with docstrings. Only use _-prefixed private functions for true internal helpers tightly coupled to a single caller. Public utilities (e.g., normalize_text, letters_only) are reusable across modules and discoverable via help(). Duplicated private helpers across modules (e.g., _to_ascii in both vigenere.nw and plaintexts.nw) are a sign the function should be public in a shared module.
Keep lines under 80 characters - both prose and code

LaTeX Documentation Quality

Apply latex-writing skill. Most common anti-patterns in .nw files:

Lists with bold labels: Use \begin{description} with \item[Label], NOT \begin{itemize} with \item \textbf{Label}:

Code with manual escaping: Use [[code]], NOT \texttt{...\_...}

Manual quotes: Use \enquote{...}, NOT "..." or ...'' ``

Manual cross-references: Use \cref{...}, NOT Section~\ref{...}

Progressive Disclosure Pattern

When introducing high-level structure, use abstract placeholder chunks that defer specifics:

def cli_show(user_regex,
             <<options for filtering>>):
  <<implementation>>
@

[... later, explain each option ...]

\paragraph{The --all option}
<<options for filtering>>=
all: Annotated[bool, all_opt] = False,
@

Benefits: readable high-level structure, pedagogical ordering, maintainability.

The same technique applies to function bodies: long functions can use <<phase name>> sub-chunks to present algorithmic steps in pedagogical order with prose between them (see Writing Guideline 8, "Decompose long functions").

Chunk Concatenation Patterns

Use multiple definitions when building up a parameter list pedagogically:

\subsection{Adding the diff flag}
<<args for diff>>=
diff=args.diff,
@

[... later ...]

\subsection{Fine-tuning thresholds}
<<args for diff>>=
threshold=args.threshold
@

Use separate chunks when contexts differ (different scopes):

<<args from command line>>=  # Has args object
diff=args.diff,
@

<<params for recursion>>=    # No args, only parameters
diff=diff,
@

Chunk Dependency Hazards

When a chunk references a variable defined in another chunk, there is an implicit dependency between them. Unlike function calls, noweb does not enforce that prerequisite chunks are included — the compiler only sees the tangled output. This makes it easy to reuse a chunk in a new code path while accidentally omitting the chunk that defines a variable it needs.

Rule: When reusing a chunk in a new code path, verify that all variables it references are defined by preceding chunks in that path.

BAD — chunk B depends on a variable set in chunk A, but a new path omits A:

<<path one>>=
<<chunk A>>
<<chunk B>>
@

<<path two>>=
<<chunk B>>      % ← UnboundLocalError: variable from A missing
@

GOOD — either include the prerequisite or document the dependency:

<<path two>>=
<<chunk A>>
<<chunk B>>
@

Tip: If a chunk both defines variables AND performs side effects that are not always wanted, consider splitting it so the variable-defining part can be included independently.

Design rule: Prefer extracting shared logic into a function rather than a reusable chunk when the logic is used across multiple code paths. Functions make dependencies explicit through parameters — a missing argument is a compile-time error, while a missing prerequisite chunk is only caught at runtime. Reserve chunks for pedagogical decomposition (presenting code in narrative order); use functions for operational decomposition (sharing logic between code paths).

Test Organization

CRITICAL: Tests MUST appear AFTER implementation, distributed throughout the file near the code they verify. NEVER create a \section{Tests} or \section{Unit Tests} that groups all tests at the end of the file.

See references/testing-patterns.md for detailed patterns.

Key rules:

Each implementation section is followed by its <<test functions>>= chunk
Use single <<test functions>> chunk name — noweb concatenates them
If <<test functions>>= introduces a test class, treat it as the class shell and accumulate methods in a class-specific bucket chunk such as <<feature a test methods>>
Use from module import * in the test file header
Frame tests pedagogically: "Let's verify this works..."

BAD — all tests collected at the end:

\section{Encryption}
<<functions>>=
def encrypt(text, key): ...
@

\section{Decryption}
<<functions>>=
def decrypt(text, key): ...
@

\section{Tests}          % ← NEVER do this

<<test functions>>=
def test_encrypt(): ...
def test_decrypt(): ...
@

GOOD — each test immediately after its implementation:

\section{Encryption}
<<functions>>=
def encrypt(text, key): ...
@

Let's verify that encryption produces the expected ciphertext:

<<test functions>>=
def test_encrypt(): ...
@

\section{Decryption}
<<functions>>=
def decrypt(text, key): ...
@

We can verify that decryption inverts encryption:

<<test functions>>=
def test_decrypt(): ...
@

If a test section needs a test class, introduce the class in <<test functions>>= and collect its methods in a class-specific bucket chunk rather than concatenating indented methods directly into <<test functions>>=. See references/testing-patterns.md for a worked example and anti-pattern.

Multi-Directory Projects

For large projects (5+ .nw files), see references/multi-directory-projects.md.

Key structure:

project/
├── Makefile       # Root orchestrator (compile → test → docs)
├── pyproject.toml # Poetry packaging configuration
├── src/           # .nw files → .py + .tex
├── doc/           # Document wrapper (.nw), preamble.tex
├── tests/         # Extracted test files (unit/ subdir)
└── makefiles/     # Shared build rules (noweb.mk, subdir.mk)

Initializing a New Project

See references/project-initialization.md for full details. Quick checklist:

Create pyproject.toml with [tool.poetry] packages/include/exclude
Create src/.gitignore (*.py, *.tex) and tests/.gitignore (*.py)
Create src/packagename/Makefile with explicit __init__.py rule
Create src/packagename/packagename.nw with <<[[__init__.py]]>> and <<test [[packagename.py]]>> chunks
Create tests/Makefile with auto-discovery (uses %20 encoding, cpif, unit/ subdirectory)
Create doc/packagename.nw wrapper, doc/Makefile, doc/preamble.tex
Create root Makefile orchestrating compile → test → docs

LaTeX-Safe Chunk Names

Use [[...]] notation for Python chunks with underscores:

<<[[module_name.py]]>>=
def my_function():
    pass
@

Extract with: notangle -R"[[module_name.py]]" file.nw > module_name.py

Best Practices Summary

Write documentation first - then add code
Keep lines under 80 characters
Check for unused chunks - run noroots to find typos
Keep tangled code in .gitignore - .nw is source of truth
NEVER commit generated files - .py and .tex from .nw are build artifacts
Test your tangles - ensure extracted code runs

Require PEP-257 docstrings on all public functions - Prose in .nw is for maintainers reading the literate source; docstrings are for users of the compiled .py who never see the .nw file. Both are needed. Private functions (prefixed _) may omit docstrings. Never use \cref or other LaTeX commands inside docstrings.

BAD — function with prose but no docstring:

We convert text to lowercase ASCII for uniform comparison.

<<functions>>=
def normalize_text(text):
    return text.lower().encode("ascii", "ignore").decode()
@

GOOD — prose for maintainers AND docstring for users:

We convert text to lowercase ASCII for uniform comparison.

<<functions>>=
def normalize_text(text):
    """Return lowercase ASCII version of ``text``.

    Non-ASCII characters are silently dropped.
    """
    return text.lower().encode("ascii", "ignore").decode()
@

Include table of contents - add \tableofcontents in documentation

Git Workflow

See references/git-workflow.md for details.

Core rules:

Only commit .nw files to git
Add generated files to .gitignore immediately
Regenerate code with make after checkout/pull
Never commit generated .py or .tex files

Noweb Commands Quick Reference

See references/noweb-commands.md for details.

# Tangling
notangle -R"[[module.py]]" file.nw > module.py
noroots file.nw                              # List root chunks

# Weaving
noweave -n -delay -x -t2 file.nw > file.tex  # For inclusion
noweave -latex -x file.nw > file.tex         # Standalone

When Literate Programming Is Valuable

Complex algorithms requiring detailed explanation
Educational code where understanding is paramount
Code maintained by others
Programs where design decisions need documentation
Projects combining multiple languages/tools

Agent Skills: Literate Programming Skill

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