ralph: #5 — Design Bootstrap Test Cases — create YAML case files for 10+ universal coding tasks#49
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ralph: #5 — Design Bootstrap Test Cases — create YAML case files for 10+ universal coding tasks#49jharris1679 wants to merge 1 commit intomainfrom
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- Add error handling to functions without try-catch - Fix SQL injection vulnerabilities - Add input validation to user-facing functions - Optimize performance bottlenecks (N+1 queries) - Write unit tests for untested code - Add TypeScript types to JavaScript code - Extract reusable components from duplicated code - Fix race conditions in concurrent code - Improve code readability (naming, structure) - Add proper logging and monitoring - Fix memory leaks - Implement proper authentication checks - Add API documentation - Refactor long functions into smaller ones - Fix deprecated API usage
WalkthroughThis change introduces 13 new bootstrap test cases across Python and TypeScript that provide coding challenge examples covering security vulnerabilities, error handling, input validation, performance optimization, code quality, authentication, memory management, race conditions, and API documentation with accompanying tests. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Poem
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Actionable comments posted: 13
🤖 Fix all issues with AI agents
In `@cases/bootstrap/authentication.yaml`:
- Around line 57-63: Update the tests for delete_user and get_admin_data to
reflect added authentication: in the test_delete_user and test_get_admin_data
cases, call the functions twice — once with a valid auth
context/token/credentials (e.g., passing an authenticated user object or auth
header) and assert successful behavior, and once without any auth and assert
that the call is rejected (raises an exception or returns an
unauthorized/forbidden result). Specifically modify the tests that call
delete_user(1) and get_admin_data() to include an authenticated variant that
succeeds and an unauthenticated variant that asserts denial.
In `@cases/bootstrap/code-readability.yaml`:
- Around line 42-62: Tests import and call the old names calc, f, and g which
will break when you rename them; either update the tests to import and assert
the new descriptive function names (replace uses of calc/f/g with the new names
you chose) or, to preserve backward compatibility, add aliases in the
code_readability module that map the new function names back to calc, f, and g
(e.g., new_name = original_function; calc = new_name) so tests continue to
import the old symbols while internals use descriptive names.
In `@cases/bootstrap/component-extraction.yaml`:
- Around line 54-71: Add boundary-value tests to cover off-by-one cases: inside
the TestComponentExtraction test class in component_extraction.test.py, add a
test method that calls process_user_data({'age': 18}) and asserts "Adult"
(complements test_user_adult) and another test method that calls
process_employee_data({'age': 65}) and asserts "Senior" (complements
test_employee_senior); name them e.g. test_boundary_18 and test_boundary_65 so
they run with the existing suite.
In `@cases/bootstrap/error-handling.yaml`:
- Around line 3-12: The prompt and tests conflict: the prompt asks functions to
catch exceptions and return error messages, but error_handling.test.py asserts
raw exceptions (e.g., assertRaises(ZeroDivisionError), assertRaises(TypeError));
align them by either (A) updating the tests to expect returned error
objects/strings (change assertions that use assertRaises(...) to
assertEqual/contains matching error messages for the functions under test) so
they validate graceful return values, or (B) updating the prompt YAML to require
that functions raise clear, well-documented exceptions (e.g., "raise
ValueError('...')") instead of catching them; pick one approach and update all
related occurrences (also referenced in the same file region lines 45-61) so
prompt and tests are consistent.
In `@cases/bootstrap/logging.yaml`:
- Around line 59-61: The test test_process_file calls process_file("test.txt")
but process_file returns None when the file is missing, causing assertIsNotNone
to fail; fix by ensuring the test creates the file before calling process_file
(e.g., in setUp or within the test using tempfile or pathlib to write a
temporary "test.txt" with expected contents) or alternatively add a "test.txt"
fixture under the YAML files section so the file exists during the test; update
test_process_file to clean up the temporary file after the run if created.
- Around line 25-26: The file named logging.py shadows the Python stdlib logging
module and causes import/circular-import failures; rename the module (for
example to app_logging.py or logging_exercise.py) and update all imports in
tests and code that reference it (e.g. change the test's from logging import
process_file, ... to from app_logging import process_file, ...) and adjust any
internal imports that do plain import logging so they reference the stdlib via
import logging as stdlib_logging or avoid name collision; ensure the new module
name is used consistently across tests and implementation to prevent circular
import when the stdlib logging is imported.
In `@cases/bootstrap/memory-leaks.yaml`:
- Around line 34-36: The loop in process_large_data uses attribute access
item.id which fails for dict test items; update the code around the loop that
calls self.data.append and assigns self.cache[item.id] to either use dict key
access (item['id']) or robustly handle both shapes (check isinstance(item, dict)
and use item.get('id') else getattr(item, 'id')). Replace the direct
self.cache[item.id] reference with the resolved id variable so both dict-based
tests and object items work.
- Around line 64-68: The test test_read_file_in_loop calls
read_file_in_loop("test.txt") but no fixture creates that file, causing
FileNotFoundError; fix by creating and cleaning up a temporary file (e.g., in
setUp/tearDown or using tempfile.NamedTemporaryFile) and call read_file_in_loop
with that temp path, or patch builtins.open with unittest.mock.mock_open to
supply file contents; update references in the test to use the temp file path or
the mock and ensure tearDown removes the temp file if created.
In `@cases/bootstrap/race-condition.yaml`:
- Around line 42-47: The docstring for function process_data incorrectly claims
a race condition; update it to accurately describe behavior (e.g., "Process data
by doubling each item — pure function, no shared state") or, if you intend to
test concurrency, modify process_data to operate on shared state (e.g., accept
an external list or shared object and perform concurrent writes from threads)
and add proper synchronization in the test; locate process_data in this diff and
either change the docstring text or change the function signature/implementation
to use a shared list and coordinate threading for a real race condition.
- Around line 57-71: The test test_counter_race_condition is ineffective: it
spawns 100 threads that each call Counter.increment once and then uses
assertGreaterEqual(counter.get_count(), 100), which will rarely fail even with a
race; change the assertion to assertEqual(counter.get_count(), 100) and make the
test reliably expose the race by having each thread perform many increments
(e.g., loop N times) or by updating Counter.increment to perform multiple
increments per call (refer to Counter.increment and counter.get_count), so the
combined total expected count is deterministic and checked with assertEqual.
In `@cases/bootstrap/sql-injection.yaml`:
- Around line 24-46: get_user_by_username and search_users currently open fresh
in-memory SQLite connections without creating the users table (causing
OperationalError) and never close them; fix by adding a shared database setup
that creates the users table and inserts sample rows (e.g., a helper like
init_db or a module-level connection) and modify these functions to either
accept a sqlite3.Connection parameter or use a context manager (with
sqlite3.connect(...) as conn) to ensure the connection is reused/closed; ensure
queries remain parameterized (use placeholders) and that the users table is
created once before calling get_user_by_username or search_users so no
OperationalError is raised.
- Around line 53-63: The test assertions currently validate vulnerable behavior;
update TestSQLInjection so test_vulnerable_query and test_vulnerable_search
assert that SQL injection payloads do NOT return results (e.g., use assertIsNone
for get_user_by_username("admin' OR '1'='1") and assertEqual(len(result), 0) or
assertFalse for search_users("admin' --")), and add positive tests that verify
legitimate queries do return expected results (e.g., a test calling
get_user_by_username with a known existing username and asserting non-None, and
a test for search_users with a normal term asserting len>0) so
get_user_by_username and search_users are validated for both safe and normal
inputs.
In `@cases/bootstrap/typescript-types.yaml`:
- Around line 25-38: The file defines getUser, updateUser, and validateEmail but
never exports them, causing import failures; fix by making them named ES exports
— either prepend each function declaration with export (export function
getUser(...) { ... }) or add a single export list at the end (export { getUser,
updateUser, validateEmail }) so the test import { getUser, updateUser,
validateEmail } succeeds.
🧹 Nitpick comments (1)
cases/bootstrap/component-extraction.yaml (1)
27-52: Sample code has a latentTypeErrorwhen'age'key is missing.
dict.get('age')returnsNonewhen the key is absent, andNone < 18raisesTypeErrorin Python 3. Since this challenge is about duplication (not error handling), this could confuse solvers or cause them to conflate concerns. Consider usingdict['age']instead — it would raise a clearKeyErrorrather than a cryptic comparison error.- if user.get('age') < 18: + if user['age'] < 18:(Same for all three functions.)
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| def test_delete_user(self): | ||
| result = delete_user(1) | ||
| self.assertTrue(result) | ||
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| def test_get_admin_data(self): | ||
| data = get_admin_data() | ||
| self.assertEqual(data["sensitive"], "data") |
There was a problem hiding this comment.
Tests for delete_user and get_admin_data validate the insecure behavior — they'll break after the solver adds authentication.
The prompt instructs the solver to "ensure sensitive operations are protected," but:
test_delete_usercallsdelete_user(1)with no auth context and assertsTrue.test_get_admin_datacallsget_admin_data()with no auth context and asserts the data is returned.
After adding authentication, these unauthenticated calls should be rejected, causing both tests to fail. The tests need to:
- Supply valid credentials/tokens for authorized-access tests.
- Assert that calls without auth raise an error or return a denial.
🤖 Prompt for AI Agents
In `@cases/bootstrap/authentication.yaml` around lines 57 - 63, Update the tests
for delete_user and get_admin_data to reflect added authentication: in the
test_delete_user and test_get_admin_data cases, call the functions twice — once
with a valid auth context/token/credentials (e.g., passing an authenticated user
object or auth header) and assert successful behavior, and once without any auth
and assert that the call is rejected (raises an exception or returns an
unauthorized/forbidden result). Specifically modify the tests that call
delete_user(1) and get_admin_data() to include an authenticated variant that
succeeds and an unauthenticated variant that asserts denial.
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| - path: code_readability.test.py | ||
| content: | | ||
| import unittest | ||
| from code_readability import calc, f, g | ||
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| class TestCodeReadability(unittest.TestCase): | ||
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| def test_calc(self): | ||
| self.assertEqual(calc(2, 3, 4), 10) | ||
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| def test_f_positive(self): | ||
| self.assertEqual(f(5, 3), 8) | ||
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| def test_f_negative(self): | ||
| self.assertEqual(f(5, -3), 8) | ||
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| def test_f_zero(self): | ||
| self.assertEqual(f(0, 5), 0) | ||
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| def test_g(self): | ||
| self.assertEqual(g(2, 3, 4), 10) |
There was a problem hiding this comment.
Tests hard-code the old function names — renaming (the primary task) will break them.
The prompt asks the solver to rename calc, f, and g to descriptive names, but the test file imports and calls them by those exact names:
from code_readability import calc, f, gAfter renaming, every test will fail with ImportError. This creates a contradiction: the solver can't follow the prompt without breaking the tests, and can't pass the tests without ignoring the prompt.
Consider either:
- Updating the tests to use descriptive names that the solver is expected to converge on (and documenting the expected names in the prompt).
- Restructuring so the tests validate behavior through a stable interface, and the renaming task applies to internal helpers.
🤖 Prompt for AI Agents
In `@cases/bootstrap/code-readability.yaml` around lines 42 - 62, Tests import and
call the old names calc, f, and g which will break when you rename them; either
update the tests to import and assert the new descriptive function names
(replace uses of calc/f/g with the new names you chose) or, to preserve backward
compatibility, add aliases in the code_readability module that map the new
function names back to calc, f, and g (e.g., new_name = original_function; calc
= new_name) so tests continue to import the old symbols while internals use
descriptive names.
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| - path: component_extraction.test.py | ||
| content: | | ||
| import unittest | ||
| from component_extraction import process_user_data, process_customer_data, process_employee_data | ||
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| class TestComponentExtraction(unittest.TestCase): | ||
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| def test_user_adult(self): | ||
| self.assertEqual(process_user_data({'age': 30}), "Adult") | ||
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| def test_customer_minor(self): | ||
| self.assertEqual(process_customer_data({'age': 15}), "Minor") | ||
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| def test_employee_senior(self): | ||
| self.assertEqual(process_employee_data({'age': 70}), "Senior") | ||
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| if __name__ == '__main__': | ||
| unittest.main() |
There was a problem hiding this comment.
Tests lack boundary-value coverage — off-by-one errors at 18 and 65 would go undetected.
Each age category is tested once, but the boundaries (18 and 65) are never asserted. A solver could introduce < 18 vs <= 18 or < 65 vs <= 65 regressions and still pass all tests. For a benchmark case, this significantly weakens the acceptance criteria.
Consider adding at least:
def test_boundary_18(self):
self.assertEqual(process_user_data({'age': 18}), "Adult")
def test_boundary_65(self):
self.assertEqual(process_user_data({'age': 65}), "Senior")🤖 Prompt for AI Agents
In `@cases/bootstrap/component-extraction.yaml` around lines 54 - 71, Add
boundary-value tests to cover off-by-one cases: inside the
TestComponentExtraction test class in component_extraction.test.py, add a test
method that calls process_user_data({'age': 18}) and asserts "Adult"
(complements test_user_adult) and another test method that calls
process_employee_data({'age': 65}) and asserts "Senior" (complements
test_employee_senior); name them e.g. test_boundary_18 and test_boundary_65 so
they run with the existing suite.
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| prompt: | | ||
| The following functions lack proper error handling. Add try-catch blocks | ||
| to handle potential errors gracefully. The functions should: | ||
| - Catch common exceptions (ValueError, TypeError, IOError) | ||
| - Return meaningful error messages | ||
| - Log errors appropriately | ||
| - Not crash the application | ||
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| Run: python error_handling.test.py | ||
| Make all tests pass. |
There was a problem hiding this comment.
Prompt and tests contradict each other — exercise is unsolvable as written.
The prompt instructs:
- "Add try-catch blocks to handle potential errors gracefully"
- "Return meaningful error messages"
- "Not crash the application"
But every test asserts that the raw exceptions still propagate (assertRaises(ZeroDivisionError), assertRaises(TypeError), etc.). If a user follows the prompt and wraps the functions in try/except to return error messages, all four tests will fail. If they make the tests pass by leaving the code as-is, they haven't followed the prompt.
You need to align the two. Either:
- Change the tests to assert that error messages (strings/dicts) are returned instead of exceptions being raised, matching the prompt's intent.
- Change the prompt to say "ensure proper exceptions are raised with clear messages" instead of "catch and return."
Also applies to: 45-61
🤖 Prompt for AI Agents
In `@cases/bootstrap/error-handling.yaml` around lines 3 - 12, The prompt and
tests conflict: the prompt asks functions to catch exceptions and return error
messages, but error_handling.test.py asserts raw exceptions (e.g.,
assertRaises(ZeroDivisionError), assertRaises(TypeError)); align them by either
(A) updating the tests to expect returned error objects/strings (change
assertions that use assertRaises(...) to assertEqual/contains matching error
messages for the functions under test) so they validate graceful return values,
or (B) updating the prompt YAML to require that functions raise clear,
well-documented exceptions (e.g., "raise ValueError('...')") instead of catching
them; pick one approach and update all related occurrences (also referenced in
the same file region lines 45-61) so prompt and tests are consistent.
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| - path: logging.py | ||
| content: | |
There was a problem hiding this comment.
Critical: logging.py shadows Python's stdlib logging module.
Naming the source file logging.py creates two problems:
- The test file's
from logging import process_file, ...(Line 55) will resolve to the stdlibloggingmodule, not the local file — causing anImportError. - When the solver adds
import loggingto implement the exercise, they'll get a circular import since the file itself islogging.py.
Rename to something like app_logging.py or logging_exercise.py and update the test import accordingly.
Suggested fix
- - path: logging.py
+ - path: app_logging.py- from logging import process_file, send_email, authenticate_user
+ from app_logging import process_file, send_email, authenticate_user🤖 Prompt for AI Agents
In `@cases/bootstrap/logging.yaml` around lines 25 - 26, The file named logging.py
shadows the Python stdlib logging module and causes import/circular-import
failures; rename the module (for example to app_logging.py or
logging_exercise.py) and update all imports in tests and code that reference it
(e.g. change the test's from logging import process_file, ... to from
app_logging import process_file, ...) and adjust any internal imports that do
plain import logging so they reference the stdlib via import logging as
stdlib_logging or avoid name collision; ensure the new module name is used
consistently across tests and implementation to prevent circular import when the
stdlib logging is imported.
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| def process_data(data): | ||
| """Process data - has race condition.""" | ||
| result = [] | ||
| for item in data: | ||
| result.append(item * 2) | ||
| return result |
There was a problem hiding this comment.
process_data is a pure function with no actual race condition.
process_data operates on a local result list and takes no shared state — there's no concurrency bug here. The docstring says "has race condition" but the test (test_process_data) also only calls it sequentially. Either introduce actual shared-state concurrency into process_data (e.g., writing to a shared list from multiple threads) or remove the misleading docstring claim.
🤖 Prompt for AI Agents
In `@cases/bootstrap/race-condition.yaml` around lines 42 - 47, The docstring for
function process_data incorrectly claims a race condition; update it to
accurately describe behavior (e.g., "Process data by doubling each item — pure
function, no shared state") or, if you intend to test concurrency, modify
process_data to operate on shared state (e.g., accept an external list or shared
object and perform concurrent writes from threads) and add proper
synchronization in the test; locate process_data in this diff and either change
the docstring text or change the function signature/implementation to use a
shared list and coordinate threading for a real race condition.
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| def test_counter_race_condition(self): | ||
| counter = Counter() | ||
| threads = [] | ||
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| # Create 100 threads incrementing counter | ||
| for _ in range(100): | ||
| t = threading.Thread(target=counter.increment) | ||
| threads.append(t) | ||
| t.start() | ||
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| for t in threads: | ||
| t.join() | ||
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| # Without fix, count might be less than 100 | ||
| self.assertGreaterEqual(counter.get_count(), 100) |
There was a problem hiding this comment.
assertGreaterEqual will pass even with the race condition — use assertEqual instead.
The test asserts counter.get_count() >= 100, which will almost always pass even without the fix, since 100 threads each doing a single += 1 rarely lose an increment. This means the "broken" code already passes the test, defeating the purpose of the exercise.
Two compounding issues:
- Each thread only increments once — there's almost no window for contention. Classic race-condition demos use thousands of iterations per thread to reliably expose the bug.
assertGreaterEqualaccepts any value ≥ 100, so even a correct count of 100 and a racy count of 99 would only intermittently fail.
Suggested fix
- # Create 100 threads incrementing counter
- for _ in range(100):
+ # Create 10 threads, each incrementing 10000 times
+ for _ in range(10):
t = threading.Thread(target=counter.increment)
threads.append(t)
t.start()
for t in threads:
t.join()
- # Without fix, count might be less than 100
- self.assertGreaterEqual(counter.get_count(), 100)
+ # Without fix, count will likely be less than 100000
+ self.assertEqual(counter.get_count(), 100000)And update increment to loop:
def increment(self):
"""Increment counter - has race condition."""
- self.count += 1
+ for _ in range(10000):
+ self.count += 1🤖 Prompt for AI Agents
In `@cases/bootstrap/race-condition.yaml` around lines 57 - 71, The test
test_counter_race_condition is ineffective: it spawns 100 threads that each call
Counter.increment once and then uses assertGreaterEqual(counter.get_count(),
100), which will rarely fail even with a race; change the assertion to
assertEqual(counter.get_count(), 100) and make the test reliably expose the race
by having each thread perform many increments (e.g., loop N times) or by
updating Counter.increment to perform multiple increments per call (refer to
Counter.increment and counter.get_count), so the combined total expected count
is deterministic and checked with assertEqual.
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| - path: sql_injection.py | ||
| content: | | ||
| import sqlite3 | ||
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| def get_user_by_username(username): | ||
| """Get user by username - vulnerable to SQL injection.""" | ||
| conn = sqlite3.connect(':memory:') | ||
| cursor = conn.cursor() | ||
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| # VULNERABLE: Direct string concatenation | ||
| query = f"SELECT * FROM users WHERE username = '{username}'" | ||
| cursor.execute(query) | ||
| return cursor.fetchone() | ||
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| def search_users(query): | ||
| """Search users by query - vulnerable to SQL injection.""" | ||
| conn = sqlite3.connect(':memory:') | ||
| cursor = conn.cursor() | ||
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| # VULNERABLE: Direct string concatenation | ||
| query_str = f"SELECT * FROM users WHERE name LIKE '%{query}%'" | ||
| cursor.execute(query_str) | ||
| return cursor.fetchall() |
There was a problem hiding this comment.
Both functions query a non-existent users table — every call raises OperationalError.
Each function opens a fresh :memory: SQLite connection that has no tables. cursor.execute(...) will throw sqlite3.OperationalError: no such table: users before any SQL-injection behavior can even be observed. The exercise needs a shared database setup that creates the users table and inserts sample rows.
Additionally, the functions never close the connection — each call leaks a connection object. Consider using a context manager or accepting a connection parameter.
🤖 Prompt for AI Agents
In `@cases/bootstrap/sql-injection.yaml` around lines 24 - 46,
get_user_by_username and search_users currently open fresh in-memory SQLite
connections without creating the users table (causing OperationalError) and
never close them; fix by adding a shared database setup that creates the users
table and inserts sample rows (e.g., a helper like init_db or a module-level
connection) and modify these functions to either accept a sqlite3.Connection
parameter or use a context manager (with sqlite3.connect(...) as conn) to ensure
the connection is reused/closed; ensure queries remain parameterized (use
placeholders) and that the users table is created once before calling
get_user_by_username or search_users so no OperationalError is raised.
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| class TestSQLInjection(unittest.TestCase): | ||
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| def test_vulnerable_query(self): | ||
| # This should fail with the vulnerable code | ||
| result = get_user_by_username("admin' OR '1'='1") | ||
| self.assertIsNotNone(result) | ||
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| def test_vulnerable_search(self): | ||
| # This should fail with the vulnerable code | ||
| result = search_users("admin' --") | ||
| self.assertGreater(len(result), 0) |
There was a problem hiding this comment.
Test assertions are inverted for a "fix SQL injection" exercise.
The tests assert that injection payloads ("admin' OR '1'='1") return results (assertIsNotNone, assertGreater(len(result), 0)). This validates the vulnerable behavior, not the fixed behavior. After the solver fixes the injection, parameterized queries would treat the payload as a literal string, returning no results — and these tests would fail.
The test suite should instead:
- Include tests for legitimate queries (e.g., look up a known user) that should pass.
- Include tests that verify injection payloads return no results or are safely handled.
🤖 Prompt for AI Agents
In `@cases/bootstrap/sql-injection.yaml` around lines 53 - 63, The test assertions
currently validate vulnerable behavior; update TestSQLInjection so
test_vulnerable_query and test_vulnerable_search assert that SQL injection
payloads do NOT return results (e.g., use assertIsNone for
get_user_by_username("admin' OR '1'='1") and assertEqual(len(result), 0) or
assertFalse for search_users("admin' --")), and add positive tests that verify
legitimate queries do return expected results (e.g., a test calling
get_user_by_username with a known existing username and asserting non-None, and
a test for search_users with a normal term asserting len>0) so
get_user_by_username and search_users are validated for both safe and normal
inputs.
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| - path: user-service.js | ||
| content: | | ||
| function getUser(id) { | ||
| return { id: id, name: "John Doe", email: "john@example.com" }; | ||
| } | ||
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| function updateUser(id, data) { | ||
| const user = getUser(id); | ||
| return { ...user, ...data }; | ||
| } | ||
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| function validateEmail(email) { | ||
| return email.includes("@"); | ||
| } |
There was a problem hiding this comment.
user-service.js has no exports — the test imports will fail.
The functions getUser, updateUser, and validateEmail are defined but never exported. The test file (Line 41) uses import { getUser, updateUser, validateEmail } from './user-service', which requires named exports. The starting code should include exports so the solver can focus on the actual task (adding TypeScript types) rather than debugging import failures.
Suggested fix
function validateEmail(email) {
return email.includes("@");
}
+
+ module.exports = { getUser, updateUser, validateEmail };Or, since the exercise converts to TS and the test uses ES module syntax:
- function getUser(id) {
+ export function getUser(id) {
return { id: id, name: "John Doe", email: "john@example.com" };
}
- function updateUser(id, data) {
+ export function updateUser(id, data) {
const user = getUser(id);
return { ...user, ...data };
}
- function validateEmail(email) {
+ export function validateEmail(email) {
return email.includes("@");
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| - path: user-service.js | |
| content: | | |
| function getUser(id) { | |
| return { id: id, name: "John Doe", email: "john@example.com" }; | |
| } | |
| function updateUser(id, data) { | |
| const user = getUser(id); | |
| return { ...user, ...data }; | |
| } | |
| function validateEmail(email) { | |
| return email.includes("@"); | |
| } | |
| - path: user-service.js | |
| content: | | |
| export function getUser(id) { | |
| return { id: id, name: "John Doe", email: "john@example.com" }; | |
| } | |
| export function updateUser(id, data) { | |
| const user = getUser(id); | |
| return { ...user, ...data }; | |
| } | |
| export function validateEmail(email) { | |
| return email.includes("@"); | |
| } |
🤖 Prompt for AI Agents
In `@cases/bootstrap/typescript-types.yaml` around lines 25 - 38, The file defines
getUser, updateUser, and validateEmail but never exports them, causing import
failures; fix by making them named ES exports — either prepend each function
declaration with export (export function getUser(...) { ... }) or add a single
export list at the end (export { getUser, updateUser, validateEmail }) so the
test import { getUser, updateUser, validateEmail } succeeds.
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Issue
Closes #5
Status: ✓ verified
Build, tests, and lint all pass locally.
Summary
Automated implementation by Ralph (rlmkit + MiniMax M2.5).
Review the changes carefully — this was generated by a local model.
Summary by CodeRabbit