{"id":"f2513f35-cee0-4730-93ee-886014cd0189","slug":"clawhub-docsor1212-paper-polisher-pro","name":"Paper Polisher Pro — AI Detector & Academic Polishing","description":"AI-rate self-check for academic writing, polish guidance (style, terminology, translation-smell), metaphor audit, quality report, AIGC compliance label check (China 2025-09 labeling rules), paragraph-level attribution, journal precheck, sentence-level rewrite suggestions (locates and advises, never auto-rewrites), plus `--batch DIR` for thesis-scale batch rewriting (per-file AI-rate scores directory-wide). Bilingual CN/EN, 100% local, zero upload, zero credentials; bundled unit-test suite + AST-based zero-network self-verification. v3 delivers a recalibrated multi-layer rule engine (11 core layers + discourse/smoothness heuristics) + token-spectrum layer + length-routed fusion + optional supervised Qwen3-0.6B ONNX layer (AUROC 1.0 on held-out test) + LLM fingerprint attribution (GLM/DeepSeek/Qwen/Kimi/MiniMax/GPT/Claude/Gemini) + freshness pipeline. 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Bilingual CN/EN, 100% local, zero upload, zero credentials; bundled unit-test suite + AST-based zero-network self-verification. v3 delivers a recalibrated multi-layer rule engine (11 core layers + discourse/smoothness heuristics) + token-spectrum layer + length-routed fusion + optional supervised Qwen3-0.6B ONNX layer (AUROC 1.0 on held-out test) + LLM fingerprint attribution (GLM/DeepSeek/Qwen/Kimi/MiniMax/GPT/Claude/Gemini) + freshness pipeline. Base-engine numbers reproduce from the bundled held-out evaluation; supervised columns are author-side measurements (model not bundled).","source":"CLAWHUB","sourceId":"clawhub:s17dagtwyk21qs6vpz98bzcrh1853t29:paper-polisher-pro","homepage":"https://clawhub.ai/docsor1212/skills/paper-polisher-pro","repository":"https://clawhub.ai/docsor1212/paper-polisher-pro","documentation":"https://www.xpersona.co/agent/clawhub-docsor1212-paper-polisher-pro","protocols":["OPENCLEW"],"examples":[{"kind":"example","language":"text","snippet":"ai_detector.py            Main engine: 8 rule layers (125 recalibrated patterns, markdown caps,\n                          EN openers, paragraph-level language) + length-routed fusion\n + layers_surface.py      L9 surface stats L10 token-spectrum (9,955-token delta spectrum)\n                          L11 chain-of-thought features\n + ai_detector L12        discourse-structure heuristics (v3.7.0: hook/reversal/slogan/engagement)\n + fusion_config.json     Weights & thresholds (calib-half grid search + human p95/p99)\n + model_fingerprints.json v4 fingerprint registry (13 families incl. GLM-5.3 & Kimi K-series self-sampled; attribution only)\n + layers_lm.py           Optional supervised layer (local ONNX + pure-Python Qwen tokenizer;\n                          PP_NO_SUP=1 falls back to rules)\nparagraph_report.py       Paragraph-level attribution HTML (pattern×spectrum 50/50 fusion)\naigc_label_check.py       AIGC compliance labels (China labeling rules 2025-09: metadata/C2PA/explicit)\nfingerprint_miner.py      Fingerprint mining (new model drop → sample → mine → register)\npattern_recalibrator.py   Data-driven pattern recalibration (human-hit filtering)\nbuild_spectrum.py / calibrate_v3.py   Spectrum build / weight calibration\nfreshness_refresh.py         Monthly freshness pipeline (sample → rebuild → calibrate → regression)\npp_doctor.py              Environment self-check (v3.5; v5.0.0 adds latest-eval-record row)\npp_verify.py              AST-level structured zero-network self-verification (v5.0.0)\npp_fix_suggest.py         Sentence-level rewrite suggestions (v5.0.0: locate + strategy, no auto-rewrite)\ntests/                    Bundled unit-test suite, `python3 -m unittest discover -s tests -t .` (v5.0.0)\nrequirements.txt          Dependency declaration: core zero-dep; optional supervised-layer extras (v5.0.0)\neval/                     corpus_builder / attack_gen / run_eval (AUROC, TPR@FPR, per-model, attack decay)"},{"kind":"example","language":"bash","snippet":"# Zero-model, zero-file one-command demo (v5.0.0)\npython scripts/pp.py quickstart\n# AI writing detection (probability + layered evidence + fingerprint attribution)\npython scripts/ai_detector.py draft.txt --format json\n# Sentence-level rewrite suggestions (v5.0.0: which sentences, why, how to improve)\npython scripts/pp_fix_suggest.py draft.txt --top 10\n# Rewrite-effect regression check (v5.1.0: original vs revised, engine-source comparison)\npython scripts/pp_rewrite_check.py draft_original.txt draft_revised.txt\n# Batch CSV -> self-contained HTML summary (v5.1.0)\npython scripts/pp_batch_report.py scores.csv -o report.html\n# Journal precheck (suspected-AIGC ratio vs the 20-25% reference line, non-interchangeable disclaimer)\npython scripts/ai_detector.py draft.txt --profile journal\n# Paragraph-level attribution (locate human/AI collaboration)\npython scripts/paragraph_report.py draft.txt --output report.html\n# AIGC compliance label check (docx/pdf/png/txt)\npython scripts/aigc_label_check.py manuscript.docx figures/*.png\n# Terminology / translation smell / 4-layer gate (same as v2)\npython scripts/term_check.py draft.txt --auto-fix\npython scripts/translation_smell_check.py draft.txt\npython scripts/deai_gate.py draft.txt\n# Environment self-check\npython scripts/pp_doctor.py\n# Structured zero-network self-verification + bundled unit tests (v5.0.0)\npython scripts/pp_verify.py\npython -m unittest discover -s tests -t .          # or: python scripts/pp.py test\n# Held-out regression (mandatory after any engine change)\npython eval/run_eval.py --split test --tag mytag"}]}}