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Get Started Free →De-slot and harmonize paper voice across `sections/*.md` without changing meaning or citation keys. **Trigger**: style harmonizer, de-template stems, remove slot phrases, discourse stems, 写作风格统一, 去槽位句式, 去生成器味. **Use when**: `writer-selfloop` is PASS but `output/WRITER_SELFLOOP_TODO.md` flags Style Smells (e.g., repeated count-based openers), or the draft reads like many sections share the same rhythm.
.claude/skills/willoscar-style-harmonizer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 196% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 317% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 59% | 0% |
Purpose: remove subtle generator-voice signals that can survive structural gates.
This skill is not a full rewrite. It is a targeted rewrite queue:
sections/*.md files flagged under ## Style SmellsRequired:
output/WRITER_SELFLOOP_TODO.md (Style Smells section)sections/*.md filesOptional (helps you stay in-scope while rewriting):
outline/writer_context_packs.jsonl (allowed citations + opener_mode hints)Run this targeted pass after writer-selfloop and before opener-variator and logic polish. If major edits happened since the report, rerun writer-selfloop so ## Style Smells reflects current text. The deterministic script is a certification adapter: it refuses to create the marker while the latest report still names flagged files or predates any sections/*.md file. Perform the semantic rewrite through this Skill or the responsible upstream writer, rerun writer-selfloop, then retry the adapter. A passing marker records the certified Section-tree SHA256.
sections/*.md files (same filenames; still body-only; no headings)writer-selfloop is the audit trail (Style Smells should shrink).sections/style_harmonized.refined.ok (empty file) when you are done (pipeline contract signal; required if this unit is marked DONE).Mission: remove slot phrases and stem repetition while keeping meaning unchanged.
Do:
Avoid:
Mission: prevent style work from becoming content drift.
Do:
Why it is high-signal: it creates a reusable sentence slot that repeats across H3s.
Rewrite moves (choose one):
Mini example (paraphrase only):
Two limitations stand out. First, ...These results hinge on ...; this matters because it changes how results transfer across protocols.Rewrite moves:
Rewrite moves:
Why it is high-signal: it reads like a generated ToC narration rather than a paper argument.
Rewrite moves:
Mini example (paraphrase only):
This section provides an overview of tool interfaces for agents.Tool interfaces define what actions are executable; interface contracts therefore determine which evaluation claims transfer across environments.Why it is high-signal: the prose starts to sound mechanically stitched (each paragraph begins with the same connective), even when the content is solid.
Rewrite moves:
Mini example (paraphrase only):
Why it is high-signal: outside of NLP contexts (token budget/context window), "token" reads like internal shorthand. In this pipeline it often originates from packs/schemas and gets copied into prose, which makes the draft feel like an intermediate artifact.
Rewrite moves:
Mini example (paraphrase only):
Overall, self-improvement should be reported as a protocol with three explicit tokens: the feedback channel, the update rule, and the accounting rule.Self-improvement results are easiest to compare when papers make three reporting fields explicit: the feedback channel, the update rule, and the accounting assumptions.1) Read output/WRITER_SELFLOOP_TODO.md
## Style Smells and the file list.2) Rewrite only the flagged files
outline/writer_context_packs.jsonl for opener_mode hints and to stay citation-scope safe while rewriting.3) Re-run writer-selfloop
writer-selfloop still reports PASS, and Style Smells shrinks.uv run python .codex/skills/style-harmonizer/scripts/run.py --workspace <workspace>--workspace <dir> (required)--unit-id <U###>--inputs <semicolon-separated>--outputs <semicolon-separated>--checkpoint <C#>uv run python .codex/skills/style-harmonizer/scripts/run.py --workspace workspaces/survey-llm-agents| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-08 | fail→pass | 9,470 | 13,682 | +44% | 1 | 1 | 0% | 1,367 | 4,043 | +196% | 0 | 0 | — |
case-01 | fail→fail | 4,139 | 4,806 | +16% | 1 | 1 | 0% | 157 | 2,194 | +1297% | 0 | 0 | — |
case-02 | fail→fail | 5,377 | 4,538 | -16% | 1 | 1 | 0% | 345 | 2,090 | +506% | 0 | 0 | — |
case-03 | fail→fail | 5,329 | 4,008 | -25% | 1 | 1 | 0% | 198 | 2,093 | +957% | 0 | 0 | — |
case-04 | fail→pass | 9,769 | 5,596 | -43% | 1 | 1 | 0% | 1,686 | 2,808 | +67% | 0 | 0 | — |
case-05 | fail→pass | 4,584 | 6,204 | +35% | 1 | 1 | 0% | 711 | 2,966 | +317% | 0 | 0 | — |
case-06 | fail→fail | 4,966 | 5,169 | +4% | 1 | 1 | 0% | 289 | 2,161 | +648% | 0 | 0 | — |
case-07 | pass→pass | 6,260 | 5,179 | -17% | 1 | 1 | 0% | 945 | 2,672 | +183% | 0 | 0 | — |
case-09 | pass→pass | 6,975 | 4,761 | -32% | 1 | 1 | 0% | 1,009 | 2,612 | +159% | 0 | 0 | — |
case-10 | pass→pass | 20,384 | 12,809 | -37% | 1 | 1 | 0% | 3,128 | 3,914 | +25% | 0 | 0 | — |
case-11 | pass→pass | 6,558 | 3,612 | -45% | 1 | 1 | 0% | 1,021 | 2,525 | +147% | 0 | 0 | — |
case-12 | fail→pass | 21,451 | 1,755 | -92% | 1 | 1 | 0% | 1,707 | 2,107 | +23% | 0 | 0 | — |
case-13 | fail→pass | 9,716 | 3,913 | -60% | 1 | 1 | 0% | 1,580 | 2,519 | +59% | 0 | 0 | — |
case-14 | pass→pass | 9,124 | 3,188 | -65% | 1 | 1 | 0% | 1,395 | 2,372 | +70% | 0 | 0 | — |
case-15 | fail→pass | 8,857 | 4,469 | -50% | 1 | 1 | 0% | 1,398 | 2,657 | +90% | 0 | 0 | — |
case-16 | fail→fail | 11,787 | 3,422 | -71% | 1 | 1 | 0% | 1,615 | 2,439 | +51% | 0 | 0 | — |
case-17 | fail→pass | 7,913 | 2,137 | -73% | 1 | 1 | 0% | 1,134 | 2,220 | +96% | 0 | 0 | — |
case-18 | fail→pass | 5,047 | 10,960 | +117% | 1 | 1 | 0% | 705 | 3,749 | +432% | 0 | 0 | — |
case-19 | pass→pass | 11,997 | 9,196 | -23% | 1 | 1 | 0% | 1,744 | 3,307 | +90% | 0 | 0 | — |
case-20 | pass→pass | 9,436 | 6,730 | -29% | 1 | 1 | 0% | 1,318 | 2,927 | +122% | 0 | 0 | — |
case-21 | pass→pass | 9,238 | 2,938 | -68% | 1 | 1 | 0% | 1,278 | 2,271 | +78% | 0 | 0 | — |
case-22 | fail→pass | 8,122 | 2,231 | -73% | 1 | 1 | 0% | 1,195 | 2,220 | +86% | 0 | 0 | — |
case-23 | pass→pass | 5,205 | 2,548 | -51% | 1 | 1 | 0% | 685 | 2,269 | +231% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 18 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +39 percentage points is the difference between those two pass rates over the 18 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.