FixtureLinden provides a Windows workspace for reviewing round-based scheduling code with AI. Discuss pair coverage and odd-player byes, then inspect the proposed rotation before applying changes.
def round_robin(ids):
players = list(ids)
if not 2 <= len(players) <= 32 or any(type(v) is not int or abs(v) > 10**9 for v in players):
raise ValueError("players.range")
if len(set(players)) != len(players):
raise ValueError("players.duplicate")
if len(players) % 2:
players.append(None)
rounds = []
for _ in range(len(players)-1):
pairs = [(players[i], players[-1-i]) for i in range(len(players)//2)
if players[i] is not None and players[-1-i] is not None]
rounds.append(pairs)
players = [players[0], players[-1], *players[1:-1]]
return roundsdef round_robin(ids):
players = list(ids)
if not 2 <= len(players) <= 32 or any(type(v) is not int or abs(v) > 10**9 for v in players):
raise ValueError("players.range")
if len(set(players)) != len(players):
raise ValueError("players.duplicate")
if len(players) % 2:
players.append(None)
rounds = []
for _ in range(len(players)-1):
pairs = [(players[i], players[-1-i]) for i in range(len(players)//2)
if players[i] is not None and players[-1-i] is not None]
rounds.append(pairs)
players = [players[0], players[-1], *players[1:-1]]
return roundsScheduling output needs more than a list that looks plausible. FixtureLinden is a Windows AI coding workspace for reviewing how your application builds and consumes that output. Bring the participant data, schedule function and expected round structure into the conversation, then ask the connected model to inspect the current implementation before proposing a change.
A round-robin fixture generator offers a concrete project task. Specify whether each pair should meet once, how an odd participant count should be handled and whether identifiers must be unique. Ask for a proposal tied to those requirements, with any presentation rules kept separate from pair generation. The task concerns your scheduling software rather than an included event-management service.
Review coverage directly after the edit. Run project checks that account for every pair, prevent self-matches and verify how byes appear to the caller. Include both odd and even participant counts and inspect whether input order is preserved or deliberately transformed. A visually balanced round does not prove that the whole schedule contains the intended matches.
FixtureLinden supports OpenAI-compatible and Anthropic model endpoints. Select an abliterated AI model where the configured service offers one, subject to its context capacity, restrictions and tools. Use its assistance to make the requirements inspectable, then verify the proposal with your own project checks.
Describe whether the caller expects an explicit bye or simply no match for that participant in the round. Include an example output. The internal placeholder used by an implementation should not accidentally leak into the application’s displayed schedule.
Compare the generated unordered pairs with the expected participant combinations and check their counts. Also inspect self-matches and duplicate pairings. A round-by-round visual review can miss a pair that appears twice across the full schedule.
Decide what identity means in your application first. If identifiers distinguish participants, duplicates can make pair coverage ambiguous. Ask the assistant to preserve the intended validation and check how the caller reports rejected input.
Those are additional requirements that need their own data and rules. Keep them separate from basic pair generation unless the project explicitly includes them. Review any proposed extension against concrete constraints rather than assuming it follows from round-robin coverage.
FixtureLinden · Updated 2026-10-25