humanizer

humanizer is a skill for Claude Code, Codex from Borda/AI-Rig. It costs 247 tokens per session (1,487 once invoked), scanned A, original, Apache-2.0.

A writing cleanup tool for text meant for people, such as documentation, pull requests, reports, release notes, and messages. It looks for word choices and sentence patterns often associated with machine-written prose.

In plain words
What is it for?
Use it to review or clean up inline text and Markdown or plain-text files before sharing them with readers.
Why use it?
It helps remove writing habits that can make text sound artificial while keeping the original meaning and structure. It can either report matches or edit the text.

Skill for Claude CodeCodex

Part of the foundry plugin — 10 skills, 10 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/borda/ai-rig/humanizer
Any agent
npx skills add Borda/AI-Rig --skill humanizer
Clone the repo
git clone --depth 1 https://github.com/Borda/AI-Rig

Made for: Claude Code, Codex.

Or install foundry, the plugin that ships this one along with the rest of its 10 skills, 10 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for humanizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/borda/ai-rig/humanizer.svg)](https://agentmods.dev/skills/borda/ai-rig/humanizer)
Your own site
<a href="https://agentmods.dev/skills/borda/ai-rig/humanizer"><img src="https://agentmods.dev/badge/skills/borda/ai-rig/humanizer.svg" alt="Measured on agentmods" height="20"></a>
Per session 247 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00247 $0.01487
Opus 5 $0.00123 $0.00744
Sonnet 5 $0.00049 $0.00297
Haiku 4.5 $0.00025 $0.00149

Measured 4d ago against content hash aaeff49873d5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

humanizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/cc_foundry/skills/humanizer/SKILL.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Detect and remove statistical AI-writing fingerprints from human-facing prose before it ships. Grounded in Wikipedia's crowd-sourced AI-detection corpus (Wikipedia:Signs of AI writing) — a maintained list of vocabulary, syntax, and formatting patterns that over-represent in LLM output vs human baseline. Apply as a final pass, not a rewrite-from-scratch: preserve meaning, facts, and structure; only excise the tells.

  • text or file path to humanize: optional. Inline text, or a file path (Markdown/plain text) to edit in place.
  • check <file>: read-only mode — report findings without editing.
  • No argument: humanize the draft already composed earlier in this turn (self-review pass) — only reachable when the model chooses to invoke this skill mid-task; there is no platform hook that guarantees a pre-send interception, so treat this path as best-effort, not a hard gate.

1. Load the target text

  • Inline text → work on it directly, no file I/O.
  • File path → Read the file.
  • No argument → treat the draft already composed earlier in this turn as the target.

2. Scan against the checklist

Walk the text once per category below; flag every hit before editing anything (report-first, matches check mode output).

Vocabulary — cut or replace with plain equivalent:

Banned Plain replacement
delve, boasts, testament, underscore(s), showcase, tapestry, intricate/intricacies, meticulous, robust, vibrant, pivotal, crucial, garner, foster(ing), align with, landscape, interplay, enduring, enhance say the specific thing instead — drop the word, don't swap in another vague one
"stands as", "serves as", "marks a", "represents" (as copula dodge) "is" / "was"
"Additionally,", "Moreover,", "It is important to note that" delete, or state the fact directly

Syntax — flag and restructure:

  • Negative parallelism: "not just X, but Y" / "not X, but Y" / "not only X but also Y" / "X rather than Y" used as a crutch
  • Rule-of-three triads used for false comprehensiveness ("fast, reliable, and scalable")
  • "-ing" superficial-analysis tails: "highlighting...", "underscoring...", "contributing to..." tacked onto a claim with no source
  • Vague attribution / weasel words: "industry reports", "observers", "experts argue", "some critics" with no named source
  • Formulaic "Despite its [positives], X faces challenges..." conclusion pattern

Read the full file on GitHub · 82 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 82 lines · 247 tokens per session scan A aaeff49873d5

Subscribe to this mod's changes

humanizer is a skill published in the GitHub repository Borda/AI-Rig (25 stars, last pushed today), licensed Apache-2.0. It adds 247 tokens to every session and 1,487 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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