k-humanizer

k-humanizer is a skill for Codex from evergreentree97/K-Humanizer. It costs 130 tokens per session (2,127 once invoked), scanned A, original, MIT.

A Korean-language editor for resumes, career descriptions, portfolio summaries, and job applications. It makes the writing sound natural to fluent Korean readers while preserving the supplied facts and intent.

In plain words
What is it for?
Use it to polish Korean career materials across fields such as operations, planning, quality assurance, design, marketing, customer service, research, and education.
Why use it?
It helps avoid wording that sounds machine-written or overly polished, which can make career documents seem less credible.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to polish Korean career materials across fields such as operations, planning, quality assurance, design, marketing, customer service, research, and education.

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Install with agentmods
npx agentmods add skills/evergreentree97/k-humanizer/k-humanizer
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.

Any agent
npx skills add evergreentree97/K-Humanizer --skill k-humanizer
Clone the repo
git clone --depth 1 https://github.com/evergreentree97/K-Humanizer

Made for: Codex.

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 k-humanizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/evergreentree97/k-humanizer/k-humanizer.svg)](https://agentmods.dev/skills/evergreentree97/k-humanizer/k-humanizer)
Your own site
<a href="https://agentmods.dev/skills/evergreentree97/k-humanizer/k-humanizer"><img src="https://agentmods.dev/badge/skills/evergreentree97/k-humanizer/k-humanizer.svg" alt="Measured on agentmods" height="20"></a>
Per session 130 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,127 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00130 $0.02127
Opus 5 $0.00065 $0.01064
Sonnet 5 $0.00026 $0.00425
Haiku 4.5 $0.00013 $0.00213

Measured 8d ago against content hash 0c6e20798b48, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

k-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 8d 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.

skills/k-humanizer/SKILL.md · 179 lines

How it starts

The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.

K-Humanizer

Core Rule

Make Korean text sound naturally written by a fluent Korean speaker without changing facts, intent, constraints, names, numbers, or quoted text.

Do not optimize for "AI detector bypass." Optimize for reader trust: clear meaning, believable rhythm, context-appropriate wording, and no over-polishing.

Never output U+2013 (en dash), U+2014 (em dash), or U+00B7 (middle dot) in rewritten prose. If quoted text, a code identifier, or a URL contains one, preserve the verbatim material separately instead of silently changing it.

Resume First

Treat resumes and career documents as a high-stakes humanization task. The default is to improve the supplied wording, regardless of field, without choosing a different career story for the user. For every resume, career description, portfolio summary, or application-writing task:

  1. Read references/resume.md and references/resume-workflow.md.
  2. If a target role or job description is supplied, also read references/resume-roles.md.
  3. Preserve the field's established terms and the writer's actual experience.
  4. Unless the user asks for composition or tailoring, do not select, remove, or reorder experience. Remove only the AI-like wording and awkward Korean.

Resume writing can be a light edit or a structural rewrite. Humanization is the default, and the user's request sets any broader scope:

  • Polish or humanize: edit only the supplied wording and follow the change budget.
  • Turn notes into bullets: organize only facts present in the notes and expose any missing evidence that affects the claim.
  • Tailor to a posting: only when requested, rank verified experience for one primary role and at most one supporting role. Do not copy requirements into the resume.
  • Review a full resume: check section roles, repetition, evidence gaps, reader fit, and contribution boundaries before rewriting affected sections.

Do not force every career into one problem-action-metric formula. A policy decision, exception rule, research method, design choice, release decision, customer follow-up, or program operation can be useful experience without a percentage.

Read the full file on GitHub · 179 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 8d ago First seen · 179 lines · 130 tokens per session scan A 0c6e20798b48

Subscribe to this mod's changes

k-humanizer is a skill published in the GitHub repository evergreentree97/K-Humanizer (8 stars, last pushed 14d ago), licensed MIT. It adds 130 tokens to every session and 2,127 once invoked, about $0.0006 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 2026-08-31.