Rune From Code

How to Use

When given an existing function (or class method), analyze it and generate a complete RUNE spec that describes its current behavior.

Input: source code of one or more functions (any language). Output: a .rune YAML spec or Markdown section for each function.

Process

Step 1: Extract SIGNATURE

Read the function declaration exactly as written. Use the source language's actual syntax:

Python:

def calculate_discount(price: float, percentage: int) -> float:

SIGNATURE: |
  def calculate_discount(price: float, percentage: int) -> float

Go:

func CalculateDiscount(price float64, percentage int) (float64, error) {

SIGNATURE: |
  func CalculateDiscount(price float64, percentage int) (float64, error)

TypeScript:

function calculateDiscount(price: number, percentage: number): number {

SIGNATURE: |
  function calculateDiscount(price: number, percentage: number): number

If the language doesn't have type annotations (plain JavaScript, Ruby, Lua), infer types from:

• Parameter names and usage within the function body
• Default values and return statements
• Add a comment noting the inference: # types inferred from usage

Step 2: Write INTENT

Summarize what the function does in 1-3 sentences based on:

• The function name
• The docstring/doc comment (if present)
• The overall behavior

Do NOT describe implementation details. Describe purpose.

# Bad (implementation details):
INTENT: |
  Uses regex pattern to match RFC 5322 emails and returns a tuple.

# Good (purpose):
INTENT: |
  Validates an email address against RFC 5322 standards.
  Returns a validity flag and a message explaining the result.

Step 3: Extract BEHAVIOR Rules

Analyze the function's control flow and convert each branch to a WHEN/THEN rule. The source language varies, but the BEHAVIOR output is always the same WHEN/THEN format:

From Python:

if not email:
    return (False, "Email cannot be empty")

From Go:

if email == "" {
    return false, fmt.Errorf("email cannot be empty")
}

From Rust:

if email.is_empty() {
    return Err("Email cannot be empty".to_string());
}

All extract to the same BEHAVIOR:

BEHAVIOR:
  - WHEN email is empty THEN return error "Email cannot be empty"

Rules for extraction:

• Each conditional branch that returns or raises/throws becomes a WHEN/THEN
• The final return or else branch becomes OTHERWISE
• Preserve the exact error messages from the code
• Preserve the order of checks (top to bottom)
• Collapse implementation details into business-level descriptions
• Map language-specific error mechanisms to neutral language:
  • Python raise ValueError / Go return err / Rust Err() / Java throw -> "raise error" or "return error"

Step 4: Extract CONSTRAINTS

Look for input validation patterns in any language:

• Type checks (type annotations, instanceof, is, type guards, pattern matching)
• Range checks (comparisons against bounds)
• Format checks (regex, string operations, parsing)
• Size checks (length, count, capacity)

CONSTRAINTS:
  - "price: must be non-negative number"
  - "percentage: integer between 0 and 100 inclusive"

Step 5: Identify EDGE_CASES

Look for:

• Boundary values handled in conditions (>=, <, ==)
• Special cases (empty input, null/nil/None/undefined, zero, max value)
• Comments mentioning edge cases
• Guard clauses at the top of the function

EDGE_CASES:
  - "empty string: returns error"
  - "percentage = 0: returns original price"
  - "percentage = 100: returns 0.0"

Step 6: Generate TESTS

Create test cases that cover:

1. Happy path — normal inputs that succeed (2-3 tests)
2. Boundary — values at the edges of conditions (2-3 tests)
3. Error cases — inputs that trigger each error branch (1 per branch)

TESTS:
  # Happy path
  - "calculate_discount(100.0, 20) == 80.0"
  - "calculate_discount(50.0, 10) == 45.0"

  # Boundary
  - "calculate_discount(100.0, 0) == 100.0"
  - "calculate_discount(100.0, 100) == 0.0"

  # Error cases
  - "calculate_discount(-10.0, 20) raises error"
  - "calculate_discount(100.0, -5) raises error"

Step 7: Add Metadata

DEPENDENCIES:
  - "library_name"  # only external imports/packages used by the function

COMPLEXITY:
  time: O(n)  # analyze loops and recursion
  space: O(1) # analyze allocations

Step 8: Assemble and Validate

Combine all sections into a complete spec. Then self-validate:

• Does every BEHAVIOR rule have at least one test?
• Does every EDGE_CASE have a test?
• Do CONSTRAINTS map to BEHAVIOR rules?
• Is INTENT free of implementation details?

Output Format

Ask the user which format they prefer. Default to the format used elsewhere in their project.

Set meta.language to the language detected from the source code.

YAML format:

---
meta:
  name: function_name
  language: <detected from source>
  version: 1.0
  tags: [inferred, tags]
---

RUNE: function_name

SIGNATURE: |
  ...
INTENT: |
  ...
BEHAVIOR:
  - ...
TESTS:
  - ...
# ... remaining fields

Markdown format:

### function_name

**SIGNATURE:** `...`

**INTENT:** ...

**BEHAVIOR:**
- WHEN ... THEN ...

**TESTS:**
- `...`

Rules

• Extract behavior from the code AS-IS — do not improve or fix the function
• If the code has bugs, note them but spec the current behavior
• Preserve exact error messages from the source code
• If types are not annotated, infer and mark with a comment
• Always generate at least 3 tests per function
• Use the source language's syntax for SIGNATURE, not pseudocode
• Works with any programming language — adapt extraction to the language's idioms
• Ask the user to verify the spec matches their intent — code might not reflect original requirements

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