reformat_draft now uses concurrent.futures.ThreadPoolExecutor with a
default of 8 workers. OpenAI SDK calls are synchronous but network-
bound, so threads release the GIL during I/O and give real speedup.
ThreadPoolExecutor.map preserves input order regardless of completion
order, so output is deterministic.
Empirical: HML draft (134 entries) went from ~25 min serial to 1m55s
with concurrency=12. ~12x speedup; further increases hit OpenAI rate
limits.
CLI gains a --concurrency flag (default 8) for tuning per draft size /
rate limit headroom. concurrency=1 forces serial execution for
debugging. New test asserts that order is preserved when concurrent
calls finish out of input order (uses a sleep-by-index fake formatter).
Initial phase-1 baseline of the karpathy/autoresearch-style loop.
The formatter module is the inner-loop artifact; parser and linter
are infra. The linter carries a LINTER_VERSION hash (v0.2.0) that
will force a re-baseline on any rule change.
Components:
- harness/diff.py: case-sensitive field-level substring diff
- harness/score.py: three-axis scoring (field, linter, canary exact)
- src/cmos/linter.py: 9 CMOS 18 structural rules, each Purdue/CMOS cited
- src/cmos/parser.py: locate ## Bibliography section, split entries
- src/cmos/formatter.py: prompt + OpenAI call with caller injection
- src/cmos/cli.py: cmos format path/to/draft.md
- scripts/run_loop.py: loop runner with --fake mode for no-API runs
- exemplars/: 3 canary seed exemplars (book, journal w/DOI, web),
sourced from chicagomanualofstyle.org quick guide
Tests: 48 passing. Fake-mode baseline scalar = 0.000 on the 3 seed
exemplars (identity caller fails the linter on every rule). This is
the floor the real GPT-5 formatter needs to improve from.
cmos dev