GoKawiilA .docx CLI built for AI agents. Leave comments, suggest redlines, and edit Word documents without breaking the formatting or losing content — a human accepts or rejects in Word afterward.
Hand a .docx to Claude or Codex and get back a redlined copy with comments — open it in Word, accept or reject as usual.
to Claude or Codex and get back a redlined copy with comments — open it in Word, accept or reject as usual. Agents address text by stable locators with character offsets ( p3:5-20 ); humans see normal Word formatting on disk.
with character offsets ( ); humans see normal Word formatting on disk. Custom styles, theme colors, embedded objects — all of it survives. The CLI mutates XML in place rather than re-emitting from a lossy model.
Why docx-cli?
The default way agents edit Word docs is to unzip the .docx and hand-write the OOXML inside. That takes a strong model to get right, burns tokens, and routinely produces a file Word won't open. docx-cli hands the agent plain commands plus an annotated-Markdown read view, so it never has to reason about the XML.
We measured it — a controlled A/B bake-off: six real document tasks (fill an NDA, fill an invoice, restyle a résumé, redline a contract, finalize a contract, author a journal), the same starting files, and one independent judge grading every result from the Word-rendered pages. Three runs per arm at each of two model tiers:
Haiku (weak, cheap) Sonnet (strong) docx-cli default skill docx-cli default skill Tasks solved (of 6) 4.3 (4–5) 0.7 (0–1) 6.0 (6–6) 4.0 (4–4) Rendered correctly (of 6) 6 3.7 6.0 4.7 Outright-broken documents 0 ~1/run (up to 2) 0 0 Input tokens 2.4M 6.1M (2.6×) 1.6M 3.6M (2.2×) Wall-clock 924 s 1,882 s (2.0× slower) 1,175 s 2,029 s (1.7× slower)
The correctness gap is widest on the cheap Haiku tier (~6×) , and a frontier model never closes it — the default skill caps at 4/6, losing the contract redline and the résumé every Sonnet run.
, and a frontier model never closes it — the default skill caps at 4/6, losing the contract redline and the résumé every Sonnet run. The cost and speed penalties are model-independent — ~2.2–2.6× more tokens and ~1.7–2× slower at both tiers, with token/time ranges that never overlap.
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