auto-improve은(는) 무엇을 하나요?
Autonomous, judge-gated self-improvement for any text artifact. See README.md for the
full guide; this is the agent-facing quick reference.
When to use
The user wants something measurably better, not just rewritten — and is willing to express "better" as a rubric. Good fits: cold emails, landing copy, prompts, skill definitions, blog posts, cover letters, docs, contracts, menus.
Run it
export GEMINI_API_KEY=... # https://aistudio.google.com/apikey
python3 improve.py \
--artifact path/to/file.md \ # must be inside a git repo
--tag v1 \ # → branch improve/v1, results/v1.tsv
--max-iterations 8 \
--criteria path/to/rubric.md # OPTIONAL — omit to auto-infer a rubric; or add
# --goal "what good looks like" to steer the inferred one
If you don't pass --criteria, auto-improve writes a rubric from the artifact first and
saves it to results/<tag>.rubric.md — read it to see what it's optimizing for.
Check a finished run: python3 improve.py --status --tag v1.
The result lives on the improve/<tag> git branch — git diff main improve/v1 -- <file>.
How it decides (the anti-slop part)
- Mutate — best-of-N candidate edits, applied as surgical diffs (crash-proof).
- Score — each candidate graded against the rubric by a separate model call.
- Decide — the best candidate is pairwise-judged against the champion (both orderings, debiased); kept only if it genuinely wins, else reverted.
The mutator never grades its own work, and confident-but-worse rewrites are discarded — so the climb is real and every commit is a verified gain.
Writing the rubric
A markdown file of weighted dimensions totaling 100 — anchored (50/70/90), reward-framed,
specific. See criteria/README.md and the worked examples in criteria/.
Key rules
- The mutator and evaluator never share context.
- One artifact, one rubric, one
--tagper run. - The git branch is the source of truth;
results/<tag>.tsvis the climb log (untracked). - Never edit outside the artifact; small surgical diffs, not wholesale rewrites.