Skill: kevin0818-lxd/essay-humanize-iterator (version 1.0.2)

# Essay Humanize Iterator — Skill Specification ## Purpose Iteratively refine essays to minimize false positives from oversensitive AI detectors by removing stereotypical AI writing patterns and aligning semantic density and syntactic complexity with native human writing norms. ## When to Use - User submits an essay and wants to **reduce AI stylistic patterns** that trigger false positives - User asks to **rehumanize**, **iterate humanize**, or **improve writing naturalness** - User wants to improve **semantic density** or **syntactic complexity** to match human writing norms - User mentions **AI风格优化**, **减少AI痕迹**, **迭代改写**, **写作自然度** ## Workflow ``` 1. User provides essay text 2. MEASURE: Run skill/scripts/measure.py → get AI score, MDD, TTR, CW ratio 3. CHECK: If all metrics pass → output essay + report. Done. 4. REWRITE: Generate targeted revision using feedback from measurement 5. RE-MEASURE: Run measure.py on rewritten text 6. REPEAT: Loop steps 3-5 until pass or max iterations (default 3) 7. OUTPUT: Final essay + iteration report table + change summary ``` ## Measurement Axes | Axis | Tool | Pass Criteria | |------|------|---------------| | AI Pattern Score | 24-regex weighted scan | ≤ 15 / 100 | | MDD Mean | spaCy dependency parse | 2.15 – 2.55 | | MDD Variance | per-sentence MDD spread | ≥ 0.016 | | Lexical TTR | content-word type/token | ≥ 0.50 | | Content-Word Ratio | content / all tokens | 0.52 – 0.65 | See `skill/references/metrics.md` for formulas and baselines. ## Iteration Strategy - **Iter 1:** Remove highest-weight AI patterns (em dashes, markdown, bolding, cliche metaphors) - **Iter 2:** Fix remaining patterns + increase syntactic variety - **Iter 3:** Fine-tune semantic density + register naturalness See `skill/references/iteration_strategy.md` for full escalation logic. ## Rewrite Engine All rewriting is performed locally by the orchestrating LLM based on targeted feedback from `measure.py`. No external API calls are made. Rules for rewriting: - Process the essay paragraph by paragraph - Follow the specific feedback instructions from `build_iteration_feedback()` - Preserve all citations, references, and factual claims - Do not add new sources or fabricate evidence - Output plain text only (no markdown formatting, no LaTeX delimiters) ## Output Format ### Final Essay Plain text. Preserve the original heading structure if any. No markdown artifacts. ### Iteration Report ``` | Iter | AI Score | MDD Mean | MDD Var | TTR | CW Ratio | Status | |------|----------|----------|----------|--------|----------|--------| | 0 | 45.2 | 2.4821 | 0.0098 | 0.4712 | 0.6280 | FAIL | | 1 | 18.6 | 2.3891 | 0.0142 | 0.4988 | 0.5932 | FAIL | | 2 | 11.3 | 2.3504 | 0.0178 | 0.5124 | 0.5801 | PASS | ``` ### Change Summary After the table, provide a brief bullet list of what changed across iterations: - Which patterns were removed - How sentence structure was varied - What vocabulary changes were made ## Rules 1. **Preserve argument:** The author's thesis, evidence, and logical flow must remain intact 2. **Preserve citations:** Never remove, alter, or fabricate citations/references 3. **Plain text output:** No markdown headings (unless input had them), no bold, no em dashes 4. **No hallucination:** Do not add claims, data, or sources not in the original 5. **Idempotent measurement:** Always use `measure.py` for scoring — do not estimate scores 6. **Early exit:** If the input essay already passes all thresholds, output it unchanged with a passing report 7. **Transparency:** Always show the iteration table so the user sees the convergence trajectory ## Supporting Files | File | Purpose | |------|---------| | `skill/scripts/measure.py` | Quantitative scorer (AI patterns + MDD + semantic density) | | `skill/scripts/iterate.py` | Iteration engine (measure + feedback generation) | | `skill/references/patterns.md` | 24 AI pattern definitions and fix strategies | | `skill/references/metrics.md` | Metric formulas, baselines, thresholds | | `skill/references/iteration_strategy.md` | Per-iteration focus and escalation logic | | `data/analysis/weights.json` | Corpus-derived pattern weights |

path: requirements.txt
content: spacy>=3.7.0


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