post-editese-metric-engineer

post-editese-metric-engineer is an agent for Claude Code from epoko77-ai/im-not-ai. It costs 158 tokens per session (1,792 once invoked), scanned A, original, MIT.

A measurement tool for studying Korean writing patterns linked to post-editese, meaning wording that becomes simplified, standardized, or shaped by English sentence patterns during editing. It adds these measurements and eight detection signals to an existing metrics file.

In plain words
What is it for?
Use it when adding or checking metrics for Korean text quality. It measures vocabulary variety, content-word density, standardized sentence endings, English-influenced structure, passive forms, pronoun use, and other listed patterns using only the standard library.
Why use it?
It makes subjective judgments about unnatural or machine-like Korean easier to measure and test over time. Existing measurements and their function signatures remain available for regression checks.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the humanize-korean plugin — 3 skills, 9 agents shipped together

Good fit Use it when adding or checking metrics for Korean text quality. It measures vocabulary variety, content-word density, standardized sentence endings, English-influenced structure, passive forms, pronoun use, and other listed patterns using only the standard library.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/epoko77-ai/im-not-ai/post-editese-metric-engineer
About the project

Humanize KR is a command-line coding-agent skill that detects patterns making Korean text look machine-written and rewrites its style, rhythm, and wording while preserving the content. Korean-language writers use it to revise translation-like phrasing, repetitive structures, formulaic expressions, and other listed AI writing patterns. The catalogue includes its agents, skills, instructions, and plugin for supported coding-agent tools.

epoko77-ai/im-not-ai · 5,418 stars · on GitHub · imnotai.kr

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.

Clone the repo
git clone --depth 1 https://github.com/epoko77-ai/im-not-ai

Made for: Claude Code.

Or install humanize-korean, the plugin that ships this one along with the rest of its 3 skills, 9 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 post-editese-metric-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/epoko77-ai/im-not-ai/post-editese-metric-engineer/github.svg)](https://agentmods.dev/agents/epoko77-ai/im-not-ai/post-editese-metric-engineer)
Your own site
<a href="https://agentmods.dev/agents/epoko77-ai/im-not-ai/post-editese-metric-engineer"><img src="https://agentmods.dev/badge/agents/epoko77-ai/im-not-ai/post-editese-metric-engineer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for post-editese-metric-engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/epoko77-ai/im-not-ai/post-editese-metric-engineer"><img src="https://agentmods.dev/badge/agents/epoko77-ai/im-not-ai/post-editese-metric-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 158 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,792 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 $0.00158 $0.01792
Opus 5 $0.00079 $0.00896
Sonnet 5 $0.00032 $0.00358
Haiku 4.5 $0.00016 $0.00179

Measured 12d ago against content hash 06e70ec16b89, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

post-editese-metric-engineer 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 12d 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.

agents/post-editese-metric-engineer.md · 139 lines

How it starts

The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.

역할

v1.6 metrics.py(308줄, 8지표)에 post-editese 3축 정량 지표와 보고서 8유형 검출 시그널을 추가한다. 본진 monolith·5인 정의는 무수정.

입력

  • 기존 metrics.py: skills/humanize-korean/references/metrics.py
  • 기존 baseline.json: skills/humanize-korean/references/baseline.json
  • 기존 tests: tests/test_metrics.py
  • gap-analyzer 후보: 02_gap/02_gap_matrix.mdmetric_candidate 필드와 post-editese 3축 후보

출력

1) _workspace/v2.0-YYYY-MM-DD/03_metrics/metrics_v2.py

기존 308줄 + post-editese 3축 + 8유형 검출 시그널 추가. 기존 8지표 시그니처는 보존(회귀 안전).

신규 지표 권고 8~10개:

# === Post-Editese 3축 ===
def lexical_diversity_ttr(text: str) -> float:
    """type-token ratio, 단순화 지표"""

def lexical_density(text: str) -> float:
    """content word ratio (한자어·고유명사·동사·형용사)"""

def normalisation_score(text: str) -> float:
    """평서형 ~한다/~된다/~이다 정형구 수렴 비율"""

def interference_index(text: str) -> dict:
    """영어 통사구조 보존도 (8유형 가중 합산)"""

# === 8유형 검출 시그널 ===
def inanimate_subject_rate(text: str) -> float:
    """T1: 무생물 주어 + 만능 동사(보여준다/시사한다/만든다) 비율"""

def by_passive_count(text: str) -> int:
    """T2a: ~에 의해 + 피동 빈도 (단순 ~에 의해 제외)"""

def double_passive_count(text: str) -> int:
    """T2b: ~되어진다/~여지다/잊혀지다/보여지다/쓰여지다"""

def pronoun_density(text: str) -> float:
    """T3: 그/그녀/그것/그들 단락당 빈도 (영형 대명사 회피율의 역지표)"""

def deul_overuse_rate(text: str) -> float:
    """T4: 무정물·추상명사 + -들 (데이터들·정보들·결과들·연구들·아이디어들·문제들)"""

def relative_clause_nesting(text: str) -> int:
    """T5: 관형구 3중 이상 중첩 ('~한 ~의 ~을 ~한 ~이/가') 빈도"""

def have_make_literal_count(text: str) -> int:
    """T6: ~을 가지다/~을 만들다/~을 가지고 있다 빈도"""

def double_particle_count(text: str) -> int:
    """T7: ~에서의/~에로의/~으로의/~에의 빈도"""

def progressive_aspect_rate(text: str) -> float:
    """T8b: ~고 있다 빈도 (단순 시제로 환원 가능한 사례 우선)"""

def da_streak_rate(text: str) -> int:
    """T8a: '~다'로 끝나는 문장 4개 이상 연속 출현 횟수"""

각 함수는:

  • pure function (text in, score out)
  • 표준 라이브러리만 (re, collections, statistics, json)
  • konlpy·bareun·mecab 의존 금지
  • ko_genre_baseline에 z-score 매핑 가능한 형태

2) _workspace/v2.0-YYYY-MM-DD/03_metrics/test_metrics_v2.py

Read the full file on GitHub · 139 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. 12d ago First seen · 139 lines · 158 tokens per session scan A 06e70ec16b89

Subscribe to this mod's changes

post-editese-metric-engineer is an agent published in the GitHub repository epoko77-ai/im-not-ai (5,418 stars, last pushed 5d ago), licensed MIT. It adds 158 tokens to every session and 1,792 once invoked, about $0.0008 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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