### expert_build/propose.py:cmd_propose_beliefs
VERDICT: PASS
CORRECTNESS: VALID
SPEC_COMPLIANCE: MEETS
ISSUE_COMPLIANCE: N/A
BELIEF_COMPLIANCE: CONSISTENT
TEST_COVERAGE: COVERED
INTEGRATION: WIRED
REASONING: The function was refactored to support incremental batch writing and state persistence. Proposals are now written to the output file immediately after each successful LLM batch, and the processed entries tracking file (`proposed-entries.json`) is updated accordingly. This significantly improves the robustness of the command against interruptions (e.g., crashes or timeouts in later batches) and provides better visibility of progress. The logic for filtering already-existing beliefs and handling the `[ACCEPT/REJECT]` placeholder in LLM output is correctly implemented.
---

### tests/test_propose.py
VERDICT: PASS
CORRECTNESS: VALID
SPEC_COMPLIANCE: MEETS
ISSUE_COMPLIANCE: N/A
BELIEF_COMPLIANCE: CONSISTENT
TEST_COVERAGE: COVERED
INTEGRATION: WIRED
REASONING: The new test suite provides comprehensive coverage for the refactored incremental writing behavior. It specifically verifies that: 1) Proposals from completed batches survive crashes in subsequent batches; 2) All proposals are written on total success; 3) Already-existing beliefs are correctly filtered per batch; 4) Entries from failed batches are not marked as processed; 5) The updated regex correctly handles various belief ID formats; 6) New proposals are correctly appended to existing output files with appropriate metadata headers.
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### SELF_REVIEW
LIMITATIONS: I did not have access to the full `PROPOSE_BELIEFS` prompt string to verify if the instructions to the LLM perfectly align with the regex changes, though the regex appears robust enough to handle standard variations. The review was conducted using the provided diff and focused observations of helper functions.
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### FEATURE_REQUESTS
- Include the `PROPOSE_BELIEFS` prompt constant in observations when reviewing `cmd_propose_beliefs` to ensure regex alignment with LLM instructions.
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