analysis-pipeline
Reverse engineering - multi-source product intelligence analysis with provenance tracking. Master methodology for all analysis agents.
Layer 1 skill for extracting behavioral intelligence from test suites. Framework detection, test code reading strategy, test execution strategy, behavioral claim extraction with Given/When/Then mapping, e2e vs unit value classification. Loaded by the analyzer agent during Layer
$ npx -y skills add prime-radiant-inc/greenfield --skill test-suite-analysis --agent claude-codeHow it fires
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/test-suite-analysisContext preview
The summary Claude sees to decide when to auto-load this skill.
Layer 1 skill for extracting behavioral intelligence from test suites. Framework detection, test code reading strategy, test execution strategy, behavioral claim extraction with Given/When/Then mapping, e2e vs unit value classification. Loaded by the analyzer agent during Layer
name: test-suite-analysis description: Layer 1 skill for extracting behavioral intelligence from test suites. Framework detection, test code reading strategy, test execution strategy, behavioral claim extraction with Given/When/Then mapping, e2e vs unit value classification. Loaded by the analyzer agent during Layer 1.
Extract behavioral intelligence from test suites. Tests are executable specifications -- they encode what the system MUST do in a form that can be verified. A passing test is a confirmed behavioral contract.
Test suite analysis activates when:
This mode runs independently of all other intelligence sources. All output is **RAW** (test code references internal implementation details).
Tests are the only source type that is simultaneously:
A single end-to-end test is worth more than a page of documentation because the test is verified by CI on every commit.
Identify the test framework(s) in use before analyzing test code. Different frameworks use different assertion styles, test organization, and execution models.
| Framework | Language | Detection Signals | |-----------|----------|-------------------| | Jest | JavaScript/TypeScript | `jest.config.*`, `describe(` / `it(` / `expect(` in `__tests__/` or `*.test.*`, `@jest/globals` imports | | Playwright | JavaScript/TypeScript | `playwright.config.*`, `@playwright/test` imports, `page.goto(` / `page.click(` | | Cypress | JavaScript/TypeScript | `cypress.config.*`, `cypress/` directory, `cy.visit(` / `cy.get(` | | pytest | Python | `conftest.py`, `pytest.ini` / `pyproject.toml` with `[tool.pytest]`, files named `test_*.py` / `*_test.py`, `assert` statements | | Go testing | Go | `*_test.go` files, `testing.T` / `testing.B` parameters, `go test` in CI config | | RSpec | Ruby | `.rspec`, `spec/` directory, `spec_helper.rb`, `describe` / `it` / `expect` blocks | | JUnit | Java/Kotlin | `@Test` annotations, `src/test/` directory, `assertEquals` / `assertThat` calls | | XCTest | Swift/Objective-C | `XCTestCase` subclasses, `func test*()` methods, `XCTAssert*` calls | | Catch2 | C++ | `#include <catch2/catch.hpp>`, `TEST_CASE(` / `SECTION(` / `REQUIRE(` macros |
# Check for test configuration files ls -la jest.config.* playwright.config.* cypress.config.* .rspec pytest.ini 2>/dev/null # Check pyproject.toml for pytest config grep -l '\[tool\.pytest' pyproject.toml 2>/dev/null # Find test directories find . -maxdepth 3 -type d \( -name "__tests__" -o -name "test" -o -name "tests" -o -name "spec" -o -name "cypress" \) 2>/dev/null # Find test files by naming convention find . -maxdepth 4 -type f \( -name "*.test.*" -o -name "*.spec.*" -o -name "test_*" -o -name "*_test.*" \) 2>/dev/null | head -50 # Count test files per pattern echo "Jest/Mocha-style:" && find . -name "*.test.*" -o -name "*.spec.*" 2>/dev/null | wc -l echo "Python-style:" && find . -name "test_*.py" -o -name "*_test.py" 2>/dev/null | wc -l echo "Go-style:" && find . -name "*_test.go" 2>/dev/null | wc -l echo "JUnit-style:" && find . -path "*/src/test/*" -name "*.java" 2>/dev/null | wc -l
Write detection results to `workspace/raw/test-evidence/test-inventory.md`.
Read test files directly and extract behavioral claims. This strategy always works -- it requires no working environment, no dependencies, and no execution.
# Build complete inventory of test files with metadata find . -type f \( -name "*.test.*" -o -name "*.spec.*" -o -name "test_*" -o -name "*_test.*" -o -name "*_test.go" \) 2>/dev/null | while read f; do lines=$(wc -l < "$f") echo "$lines $f" done | sort -rn
For each test file, extract the assertions -- these are the behavioral contracts:
# Jest/Mocha assertions grep -n "expect\|assert\|should\|toBe\|toEqual\|toContain\|toThrow\|toHaveBeenCalled" "$TEST_FILE" # pytest assertions grep -n "assert \|assert_\|assertEqual\|assertRaises\|pytest.raises" "$TEST_FILE" # Go test assertions grep -n "t\.Error\|t\.Fatal\|t\.Log\|assert\.\|require\." "$TEST_FILE" # RSpec assertions grep -n "expect\|should\|is_expected\|eq(\|include(\|raise_error" "$TEST_FILE"
Transform test code into behavioral claims using Given/When/Then structure:
For each test case (`it(`, `test(`, `func Test*`, `def test_*`), extract:
## Test: "should reject expired tokens" **Given:** A token with expiry date in the past **When:** The token is validated via `checkPermissions()` **Then:** - Returns false - Sets error to "TOKEN_EXPIRED" - Does not call the downstream service **Source:** `auth.test.ts:45-62` **Confidence:** confirmed (test assertion is an explicit behavioral contract)
Not all tests carry equal behavioral intelligence value:
| Test Type | Detection Signals | Behavioral Value | |-----------|-------------------|-----------------| | End-to-end (e2e) | Browser automation, HTTP requests to running server, multi-service interaction | **High** -- tests the system as a user experiences it | | Integra
Reverse engineer clean behavioral specs from any codebase. Greenfield reads source code, documentation, SDKs, runtime behavior, and binaries, then produces behavioral specifications, test vectors, acceptance criteria, and a full provenance trail.
Repo: prime-radiant-inc/greenfield
Reverse engineering - multi-source product intelligence analysis with provenance tracking. Master methodology for all analysis agents.
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