fix(openai): guard truncation loop from emptying the user slot on token-dense standups

[detail-app]

Detail bug report: View on Detail

Bug

• textToCompletionPrompt (src/openai/format.ts) truncates standup text so the user slot fits MODEL_MAX_TOKENS. Its loop computes the keep-count as words.length - Math.ceil((tokens - MODEL_MAX_TOKENS) / 2).
• For token-dense, space-sparse input (e.g. compact JSON.stringify output or a base64 blob — thousands of tokens but only a handful of space-delimited words), this keep-count drops to 0 or below -words.length, so slice(0, end) returns [] and the user message collapses to "".
• Introduced by 7f5e346 (which fixed a prior infinite loop but overshot). Only the fine-tuning training path is affected — it has no length guard before truncation. The live-scoring path short-circuits text.length > 1000 before this code runs, so it's unaffected.
• Effect: a human-rated standup over budget at high token density yields a training example pairing an empty standup with a yes/no label, which trainModel.ts writes to the JSONL with no local validation.

Fix

• Clamp the keep-count so the user slot is never emptied: floor at 1, cap at words.length.
• Break the loop when an iteration makes no progress (next >= truncatedTextTokenLength), so a single over-budget word that can't be reduced further terminates instead of infinite-looping — preserving the termination property 7f5e346 introduced.

Testing

• Unit tests (src/openai/format.test.ts, offline custom harness) — all pass:
  • Added a compact-JSON regression test asserting the input is in the collapse region, the user slot stays non-empty, and the result fits the budget.
  • Added a single-over-budget-word test asserting the loop terminates within a time budget and the user slot stays non-empty.
  • Pre-existing tests (oversized prose, under-limit passthrough, prompt structure, classification prompts, training-example labels) still pass.
• Typecheck (tsc --skipLibCheck --noEmit) passes.
• Lint: only a pre-existing @typescript-eslint/parser vs TypeScript 5.2 incompatibility surfaces (a 0:0 parser error affecting every .ts file, including unchanged ones); no new violations from this change.
• Reproduction check (unversioned, run locally): the bug report's compact-JSON standup previously produced an empty user message; after the fix it yields non-empty content within the token budget.
• Near-end-to-end training check (unversioned, run locally): exercised trainModel's full JSONL-writing path via the same typedi injection seam the existing aiScoreStandups test uses; the generated .jsonl has no line with an empty user content when the corpus includes a collapse-triggering standup.
• Scoring regression: the existing offline aiScoreStandups test still passes — no regression in the path that calls getProjectStandupScore.
• Live OpenAI upload: attempted against api.openai.com but it returned 401 invalid_api_key — no real API key is available in this environment, so the accept/reject behavior of a real fine-tune upload could not be verified. The local defect (empty user content reaching the upload) is eliminated by the fix and confirmed by the near-end-to-end JSONL check above.

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[augmentcode]

🤖 Augment PR Summary

Summary: This PR hardens OpenAI standup prompt truncation for token-dense, space-sparse input.

Changes:

• Clamps the word keep-count to retain at least one word and no more than the input contains.
• Stops the truncation loop when an iteration does not reduce the token count.
• Adds offline regression coverage for compact JSON and a single oversized whitespace-free word.
• Retains existing tests for normal truncation, prompt structure, and training labels.

Technical note: The affected formatting is used when serializing fine-tuning examples and when creating classification prompts.

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[augmentcode]

For a single whitespace-free value whose token count exceeds MODEL_MAX_TOKENS, next equals the original count, so this break returns that still-oversized value. getTrainingExample serializes it directly into the fine-tuning JSONL, so this reintroduces an invalid over-budget training example (and similarly lets scoring requests exceed the function's documented limit).

Severity: medium

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[detail-app]

Checked src/openai/format.ts:82-91 and the PR's own testSingleOverBudgetWordTerminatesAndStaysNonEmpty (src/openai/format.test.ts:154-167), which deliberately asserts an unsplittable over-budget single word is preserved verbatim and stays over MODEL_MAX_TOKENS (the comment states "the whole word is preserved since it cannot be split, even though it remains over the model's token budget").

I attempted to fix this by hard-truncating to the token budget once word-splitting can no longer reduce the count (sub-word cut via decode(encode(text).slice(0, MODEL_MAX_TOKENS))). That brings the over-budget single word under MODEL_MAX_TOKENS, but it directly fails the PR's assertEqual(userContent, text, '... preserved verbatim ...') contract, so landing it would rewrite that test and change the PR's stated purpose. For a single whitespace-free over-budget token, "non-empty" and "under budget" are mutually exclusive under word-based truncation; getting under budget requires sub-word splitting, which the PR deliberately rejects.

Leaving the code unchanged pending a human decision: (a) keep the current behavior — preserve non-empty verbatim and accept that an unsplittable over-budget input exceeds MODEL_MAX_TOKENS in getTrainingExample/scoring, or (b) change the PR's purpose to hard-truncate unsplittable over-budget input to the token budget (which rewrites testSingleOverBudgetWordTerminatesAndStaysNonEmpty).