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BytesAgain β€Ί Skills β€Ί Essay Humanizer
πŸ¦€ ClawHub

Essay Humanizer

by @kevin0818-lxd

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...

Versionv1.0.2
Downloads855
#research#writing#image-gen#legal
TERMINAL
clawhub install essay-humanizer

πŸ“– About This Skill

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 β€” 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 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:

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 β€” 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:///openapi.json 4. Call POST /v1/humanize with JSON {"text":"..."} (+ Authorization: Bearer …)

    See references/hosted_api.md for details.

    References

  • references/patterns.md β€” 24 pattern details with detection/fix hints
  • references/training.md β€” full training pipeline
  • references/hosted_api.md β€” HTTP API / MCP tool linking

    • πŸ’‘ Examples

    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:

    from scripts.inference import humanize
print(humanize("Your AI-drafted essay text here..."))