kramme-cc-workflow/skills/kramme:text:humanize/SKILL.md
Remove signs of AI-generated writing from text to make it sound natural and human-written. Use when editing or reviewing prose for AI-isms. Can write the result back to a source file on confirmation. Not for code, quoted passages, or text that must stay verbatim.
npx skillsauth add abildtoft/kramme-cc-workflow kramme:text:humanizeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are a writing editor that identifies and removes signs of AI-generated text to make writing sound more natural and human. The pattern catalog is based on Wikipedia's "Signs of AI writing" page, maintained by WikiProject AI Cleanup.
AI-writing signs are evidence to inspect, not proof that a passage was AI-written. Do not accuse the author or present the pattern scan as detection. Treat obvious pasted assistant artifacts as cleanup targets, especially citation placeholders or tracking residue such as turn0search0, oaicite, [cite: ...], and utm_source=chatgpt.com.
Skip rewriting and instead point out the AI patterns when the input is:
$ARGUMENTS may be one or more file paths, raw text, or a mix./ or \, or ends in a known text extension) but does not resolve, stop and report the unresolved path. Do not silently treat a missing path as raw text.references/ai-writing-patterns.md (summarized below).Preserving meaning and matching the existing voice take priority over every other goal. Do not add facts, opinions, or feelings the source does not support.
Read the full catalog from references/ai-writing-patterns.md. It covers:
Removing patterns can leave clean-but-lifeless prose. Adding personality (opinions, first person, humor) is a stronger edit that changes tone and risks introducing content the author never wrote, so apply it only when both are true:
Even then, never invent facts, opinions, or feelings the source does not support. See the "Personality and soul" section of references/ai-writing-patterns.md for technique.
When the input came from a file:
Before (AI-sounding):
The new software update serves as a testament to the company's commitment to innovation. Moreover, it provides a seamless, intuitive, and powerful user experience-ensuring that users can accomplish their goals efficiently. It's not just an update, it's a revolution in how we think about productivity. Industry experts believe this will have a lasting impact on the entire sector, highlighting the company's pivotal role in the evolving technological landscape.
After (humanized):
The software update adds batch processing, keyboard shortcuts, and offline mode. Early feedback from beta testers has been positive, with most reporting faster task completion.
Changes made:
tools
Requires Linear MCP. Implements one Linear issue end to end, selects applicable code-review, convention, and PR-refactor gates, runs them to bounded convergence, verifies, and optionally opens the PR and iterates on CI and review feedback until green. Use when the user wants a single Linear issue taken from implementation through a clean Pull Request. Not for implementation-only work, SIW-tracked issues, stacked PRs, existing PR updates, or post-merge rollout.
development
Reviews PR and local changes for convention drift and overcaution against documented rules and mined peer-file practice. Use for new patterns, dependencies, abstractions, or defensive complexity that departs from established practice; every finding cites evidence. Supports --inline. Not for general code quality (use kramme:pr:code-review) or spec review (use kramme:siw:spec-audit --team).
testing
Charts huge or foggy initiatives into a local `.context` decision map and resolves one typed frontier ticket per session until the work is ready for SIW or another execution workflow. Use when the route to a destination cannot fit in one agent session or parallel workspaces need coordinated planning state. Not for clear specs, ordinary issue decomposition, implementation, or Linear-native tracking.
development
Investigates a question against primary sources and saves one cited Markdown artifact. Use for reading legwork: official docs/API facts, source-code or spec checks, standards, and first-party service behavior before planning or implementation. Not for making product or architecture decisions, implementing code, broad web search, secondary blog summaries, or uncited answers.