writing-humanizer
Writing Humanizer
Coverage
The full pipeline for transforming AI-generated or robotic text into clear, human-sounding prose:
- AI-tell detection and removal — Tier 1 zero-tolerance word list (
delve,testament,crucial,vital,paramount,furthermore,seamless,robust,comprehensive,cutting-edge,foster,empower,leverage,harness, etc.); Tier 2 conditional list (utilize,facilitate,streamline,implement,optimize) - Active-voice conversion — passive-to-active decision tree based on actor knownness; hedging-pattern removal table
- Readability scoring as diagnosis — Flesch-Kincaid Grade (target 8–10 for general docs), Gunning Fog Index (10–12 for technical docs), Flesch Reading Ease (60–70 for UI copy); the readability diagnostic tree for sentence length, word complexity, paragraph density, and nested clauses
- Sentence variety and rhythm — the 3-beat short-long-medium pattern; sentence-structure variety checklist (declarative, compound, conditional, question+answer, fragment); opening-word rotation rule
- Vocabulary diversity — repeat technical terms exactly, rotate generic verbs, avoid elegant variation; the abstract-vs-concrete table; the jargon decision tree
- Tone mapping — the formal-to-casual spectrum (1–5) and a context-tone table covering API documentation, error messages, UI tooltips, commit messages, PR descriptions, release notes, issue bodies, onboarding copy, empty states, and marketing copy
- AI-detector limits and prose fingerprints — how some detectors use probability signals such as perplexity and burstiness, why detector scores are not proof of authorship, and which clarity-preserving edits reduce robotic prose without gaming a review process
- Paragraph rhythm and structure — paragraph-length rules per context, the hook-body-landing pattern, the bullets-vs-prose decision tree
- Anti-patterns — over-qualification, repetitive transitions, the enumeration trap, hollow intensifiers
- The 5-step humanization workflow — Tell Scan, Readability Check, Structural Rewrite, Rhythm Pass, Voice Calibration
Philosophy
More from jacob-balslev/skills
layout-composition
Use when deciding responsive page or screen structure: section order, scan pattern, grid/flex composition, breakpoints, viewport hierarchy, responsive media, and density. Do NOT use for user-goal decomposition (use `task-analysis`), navigation taxonomy (use `information-architecture`), visual polish (use `visual-design-foundations`), or component/token contracts (use `design-system-architecture`).
8context-graph
Use when designing or auditing the multi-graph context architecture of an AI-coding workspace: skill graph, document routing graph, memory index, script registry, and the cross-graph edges between them. Covers edge typing, orphan detection, connectivity health, deterministic graph synthesis signals, change-propagation checks, and drift or hub-and-spoke anti-patterns. Do NOT use for authoring one SKILL.md (use `skill-scaffold`), validating one skill (use `graph-audit`), live routing decisions (use `skill-router`), context-window budgeting (use `context-window`), or session load/drop choices (use `context-management`).
8visual-design-foundations
Use when designing or auditing visual craft: color palette, typography, spacing, elevation, rhythm, density, visual hierarchy, brand fit, contrast intent, and motion feel. Do NOT use for sign-system meaning (use `semiotics`), token/component architecture (use `design-system-architecture`), responsive structure (use `layout-composition`), or accessibility compliance (use `a11y`).
7project-knowledge-extraction
Use when extracting durable project knowledge from code, docs, issues, incidents, reports, screenshots, or conversations into reusable context such as skills, ADRs, glossaries, context docs, or memory. Do NOT use for writing a new skill contract (use `skill-scaffold`), maintaining library tooling (use `skill-infrastructure`), or generic documentation polish (use `documentation`).
6problem-framing
Use when a team is converging on solutions before agreeing on the problem, when a brief reads as a feature request, when symptoms and root needs are tangled, or when assumptions need surfacing before design work proceeds. Do NOT use for code-level bug triage, runtime failure diagnosis, or root-cause analysis of system errors — those are engineering investigation tasks, not design problem framing.
6ai-native-development
Use when reasoning about agent autonomy levels, designing auto-improve loops, evaluating AI-generated code quality, or measuring agent productivity in an LLM-assisted codebase. Covers Karpathy's three eras of software (1.0 explicit / 2.0 learned / 3.0 natural-language), the vibe-coding-vs-agentic-engineering distinction, the 0–5 autonomy slider with task-type recommendations, the one-asset / one-metric / one-time-box AutoResearch loop, Software 3.0 productivity metrics, and the documented quality regressions of ungated AI-generated code (the 'vibe hangover'). Do NOT use for choosing a specific autonomy-loop topology (use `agent-engineering`), for the per-prompt authoring discipline (use `prompt-craft`), or for reviewing the AI-generated code that comes out of a Software 3.0 workflow (use `code-review`).
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