harness-tools/skills/document/unslop/references/commands/mimic.md

/unslop mimic

Single-pass voiced writing. Draft or rewrite in a taught voice, then clear the full gate battery. Route here on "write this like me", "match my voice", or "mimic this author" once a voice card exists. Read references/mimic.md for card anatomy, scoring, and failure modes.

Flow

1. Load the situation sheet the task needs. Read card.md for the always-on core (rhythm, contraction habits, punctuation, openers, the Never list) and the one card/<situation>.md sheet that matches what you are writing. Load only what the task needs; the card is layered so generation stays cheap.
2. Draft or rewrite in that voice. Follow the card's measured markers and verbatim snippets; do not invent a habit the samples cannot support.
3. Run the output through every removal gate:

   python3 scripts/banned_phrase_scan.py <<< "$OUTPUT"
   python3 scripts/structure_scan.py <<< "$OUTPUT"
   python3 scripts/readability_metrics.py <<< "$OUTPUT"
   python3 scripts/validate_preservation.py original.txt "$OUTPUT_FILE"
   python3 scripts/diff_check.py original.txt "$OUTPUT_FILE"
   

4. Score the voice:

   python3 scripts/voice_score.py --profile profile.json "$OUTPUT_FILE"
   

The rule

A mimic that scores well on voice but trips a slop gate is rejected. Voice never buys an exemption from the constitution — the removal gates are hard, the voice score is a guide. Meaning preservation versus the original draft is its own hard gate, never blended into the voice score.

Refine — when one pass is not enough

Most users teach once and mimic cheaply thereafter. When a single pass keeps scoring short of the target voice, hill-climb with the refine loop — worth the cost only then:

python3 evals/run_mimic_refine.py --samples samples/ --draft draft.md --out OUT

LIVE (default) calls --generate-cmd (default claude -p) once per beam per iteration; --candidates-dir DIR runs the loop logic with no model calls. Hard gates (banned-phrase, structure, draft→candidate preservation, copy-gate, 150- word floor) discard a candidate regardless of score; survivors are scored against a held-out DEV split with the full voice_score composite, and the best-so-far never regresses. The divergence guard halts and sets reward_hacking_warning when the A composite improves while DEV worsens for two iterations — the reward-hacking signature. Confirm a win only when mimic_stats.py shows the CI lower bound > 0 and p < 0.05. Internals, baselines, and the failure table are in references/mimic.md.

Voice check

"Does this sound like me?" — score only, change nothing. Run python3 scripts/voice_score.py --profile .unslop/voice/<name>/profile.json --impostors evals/fixtures/voice/impostors --seed 7 <draft> and report the composite (lower is more them), the GI rank, and the two or three metric deltas that explain the score, in plain words ("your sentences run longer than usual; contractions match"). No rewrite unless asked. This is the cheapest voice interaction and the usual one after teach: check drafts often, commission rewrites rarely.