Skill: kevin0818-lxd/essay-humanizer (version 1.0.2)

--- name: essay-humanizer description: "Rewrite AI-drafted essays into more human-like academic prose. Fine-tuned LoRA over Qwen3-8B guided by 24 Wikipedia-style AI-writing pattern weights plus MDD/ADD syntactic targets from CAWSE/LOCNESS vs DeepSeek baselines. Includes trained LoRA adapter and inference script. Requires Apple Silicon macOS with MLX. Optional FastAPI host for MCP/tool linking. Orchestrator: output plain text only (no LaTeX dollar delimiters)." --- # Essay Humanizer (corpus-informed) Rewrites **AI-generated** argumentative/academic essays toward **human baseline** style informed by **CAWSE** (M/D bands) **LOCNESS**, and contrast with **DeepSeek**-generated counterparts. Ships with a fine-tuned **LoRA adapter** (9.3 MB) and inference script. ## Skill contract | Component | Path | Notes | |---|---|---| | Inference script | `scripts/inference.py` | Entry point — `humanize()` function or CLI | | LoRA adapters | `assets/adapters/adapters.safetensors.json` | 12.3 MB base64 JSON; auto-decoded to binary on first run | | Pattern weights | `data/analysis/weights.json` | Corpus-derived, loaded by inference at runtime | | Decoder | `scripts/decode_adapters.py` | Reconstructs .safetensors binary from JSON (auto or manual) | | Installer | `scripts/install_deps.sh` | One-time: `pip install mlx mlx-lm transformers` + decode | | Base model | `Qwen/Qwen3-8B-MLX-4bit` | Downloaded from HuggingFace on first run (~4.5 GB, cached) | **Requirements:** Apple Silicon macOS with Python 3.9+. ## Quick Start ```bash bash scripts/install_deps.sh # one-time: installs deps + decodes adapter python scripts/inference.py --file draft.txt # adapter auto-decodes if not already done ``` Or from Python: ```python from scripts.inference import humanize print(humanize("Your AI-drafted essay text here...")) ``` ## Weighted pattern table (descending priority) When humanizing, address **higher-weight** rows first. Weights are **data-driven** from corpus analysis (Mann-Whitney); zero-weight rows were not statistically significant. | ID | Weight | Category | Pattern | |---|---:|---|---| | P06_CLICHE_METAPHORS | 0.1358 | vocabulary | Cliche metaphors | | P15_EM_DASH_OVERKILL | 0.1358 | punctuation | Em dash overkill | | P21_MARKDOWN_ARTIFACTS | 0.1358 | formatting | Markdown artifacts | | P23_TEXTBOOK_BOLDING | 0.1358 | formatting | Textbook bolding | | P12_PRESENT_PARTICIPLE_TAIL | 0.1133 | rhetorical | Present participle tailing | | P10_RULE_OF_THREES | 0.0806 | rhetorical | Rule of threes | | P04_AI_VOCABULARY | 0.0621 | vocabulary | AI vocabulary | | P14_COMPULSIVE_SUMMARIES | 0.0598 | rhetorical | Compulsive summaries | | P05_EXCESSIVE_ADVERBS | 0.0540 | vocabulary | Excessive adverbs | | P13_OVER_ATTRIBUTION | 0.0529 | rhetorical | Over-attribution | | P11_FALSE_RANGES | 0.0341 | rhetorical | False ranges | | P17_TRANSITION_OVERUSE | 0.0001 | punctuation | Overuse of transition words | | P01_UNDUE_EMPHASIS | 0.0000 | content | Undue emphasis | | P02_SUPERFICIAL_ANALYSIS | 0.0000 | content | Superficial analysis | | P03_REGRESSION_TO_MEAN | 0.0000 | content | Regression to the mean | | P07_REDUNDANT_MODIFIERS | 0.0000 | vocabulary | Redundant modifiers | | P08_FILLER_HEDGING | 0.0000 | vocabulary | Filler hedging | | P09_NEGATIVE_PARALLELISM | 0.0000 | rhetorical | Negative parallelisms | | P16_EN_DASH_AVOIDANCE | 0.0000 | punctuation | En dash / hyphen misuse for ranges | | P18_COLLABORATIVE_REGISTER | 0.0000 | register | Collaborative register | | P19_LETTER_FORMALITY | 0.0000 | register | Letter-style formality | | P20_INSTRUCTIONAL_CONDESCENSION | 0.0000 | register | Instructional condescension | | P22_EXCESSIVE_LISTS | 0.0000 | formatting | Excessive bulleted/numbered lists | | P24_EMOJI_SYMBOL | 0.0000 | formatting | Emoji/symbol injection | ## Syntactic complexity (MDD / ADD advisory) Human **Merit / Distinction**-range writing in CAWSE often shows **variable** mean dependency distance (MDD); AI prose may cluster more tightly. When humanizing: - Reference MDD means from analysis: human ~2.333775514332394, AI ~2.4553791855163483. - **Variance ratio** (human/AI) ~1.7153931408079544: prefer natural mix of shorter and longer dependency links, not uniformly smoothed sentences. - Avoid flattening every sentence to minimal dependency length; that can read as a different kind of machine polish. ## Mandatory rule (orchestrator) 1. Output **continuous prose** suitable for submission (no chat-signoffs, no "hope this helps"). 2. **Plain text** only for math if any — no raw `$$` LaTeX unless user explicitly requests LaTeX. 3. Preserve **author stance** and **citations** if present; do not fabricate references. ## Hosted HTTP API (optional, for non-Mac or remote use) For non-Apple-Silicon machines or multi-user deployments, run the optional **FastAPI** server on a Mac host and connect via HTTP/OpenAPI: 1. Install: `pip install fastapi uvicorn[standard]` 2. Run: `uvicorn api.main:app --host 0.0.0.0 --port 8765` (set `HUMANIZE_API_KEY` env var for auth) 3. Point MCP / OpenAPI tools at `https://<your-host>/openapi.json` 4. Call `POST /v1/humanize` with JSON `{"text":"..."}` (+ `Authorization: Bearer …`) See [references/hosted_api.md](references/hosted_api.md) for details. ## References - [references/patterns.md](references/patterns.md) — 24 pattern details with detection/fix hints - [references/training.md](references/training.md) — full training pipeline - [references/hosted_api.md](references/hosted_api.md) — HTTP API / MCP tool linking


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)