LLM Mart Basic

가장 최근 윤문 결과를 2차로 다시 다듬는다 — 특정 카테고리·문단·강도 조정도 가능. humanize-korean strict 윤문(Phase B)을 기존 run_id에 재실행해 잔존 finding을 처리한다. 트리거 — "/humanize-redo".

AI(ChatGPT·Claude·Gemini)가 쓴 한글 텍스트를 사람이 쓴 글처럼 윤문한다. 번역투·영어 인용 과다·기계적 병렬·관용구·피동 남용·접속사 남발·리듬 균일·이모지/불릿 과다 등 10대 카테고리 70개 AI 티 패턴을 탐지·분류해 내용은 한 글자도 건드리지 않고 문체·리듬·표현만 자연스럽게 재작성한다. 트리거 — "AI 티 없애줘", "AI 윤문", "ChatGPT 티 제거", "번역투 고쳐", "사람이 쓴 것처럼", "humanize Korean". 단순 맞춤법 교정·번역·내용 추

정밀(strict) 모드 1단계 진단 에이전트. 글 전체를 한 번에 보고 "가장 지배적인 AI 티 패턴 3~6개"를 taxonomy ID와 함께 진단한다. 불안정한 span 열거(0↔18개로 요동) 대신 "무엇이 이 글을 지배하는가"라는 안정적 판단을 내려, 후속 윤문 콜이 그 진단을 겨냥하게 한다. 산출물은 02_diagnosis.md 1개. 도구 호출 3회 캡(Read 결합입력 + Read taxonomy + Write 진단). 이 진단이 정밀 모드 품질의 결정 변수다.

정밀(strict) 모드 3단계 마무리 에이전트. 원문과 윤문본을 직접 대조해 ①의미 보존(15항 — 각주·제목·없던 주장 주입 포함) ②자연성(잔존 AI 티 + 과윤문 양방향)을 한 콜로 병합 판정하고, 문제 구간만 국소 보정한다. 전체 재작성 금지 — 의미 드리프트(빈 수사를 없던 주장으로 대체)를 막는 게 존재 이유. 은퇴한 content-fidelity-auditor·naturalness-reviewer 2인을 대체한다. 산출물은 final.md + 09_finalize.json. 도구 호

v1.6.1 Fast Path 단일 호출 윤문 에이전트. 한 호출 안에서 탐지·윤문·자체검증을 일괄 수행하여 5,000자 이하 한글 입력을 2~3분 안에 처리한다. 산출물은 final.md 1개(본문 끝에 `<!-- HUMANIZE-SUMMARY -->` HTML 주석 블록으로 메트릭·등급·자체검증 통합). 도구 호출 chain 3회 캡. 깊은 검증이 필요하면 정밀 모드(진단→윤문→finalize 3콜) 사용.

AI가 생성한 한글 글의 "AI 티" 패턴을 체계적으로 분류·확장·버전 관리하는 도메인 전문가. `references/ai-tell-taxonomy.md`를 단일 진실 원천(SSOT)으로 유지하며, 실제 입력에서 관찰된 신규 패턴을 검증해 v1 → v2로 승격한다.

한국 번역학계(이근희·김정우·김도훈·김순영·김혜영·이영옥·곽은주·조의연)와 국제 번역학(Baker·Toury·Laviosa·Chesterman·Toral·Sarti)의 학술 인용 계보를 Humanize KR 본진 SSOT(taxonomy.md)와 외부 references/scholarship.md 양면에 안전하게 안착시키는 학술 정통성 큐레이터. 보고서의 학술 자산을 본진 분류 체계가 검증 가능한 형태로 흡수하되, SSOT 룰북 슬림성을 해치지 않게 메타필드 + 외부 파일로 분리. 본진 패턴에 출

Toral 2019 post-editese 3축(단순화·정규화·간섭)을 한국어 정량 지표로 구체화하고, 보고서 8유형 검출 시그널을 metrics.py에 추가해 회귀 검증 가능 상태로 만드는 정량 엔지니어. 표준 라이브러리만, 형태소 분석은 정규식·접미사 사전으로 근사(konlpy·mecab 금지 — v1.6 정책 보존). monolith 외부 사전 처리(prepare_monolith_input.py)에 결합되어 도구 호출 캡 3회 보존. 신규 metric 추가 또는 metric 회귀 검증 시 호

신규 분류 체계 v2.0과 metrics·playbook 패치를 quick-rules.md(monolith 전용 슬림 룰북, 126줄 → ≤180줄)에 안착하고 monolith 도구 호출 3회 캡(v1.6.1) 회귀를 검증한 뒤, GitHub PR 초안과 CHANGELOG를 작성하는 통합 엔지니어. 본진 룰북 슬림성·monolith 정의 무수정·v1.x 발행 정책(사용자 명시 승인 후 푸시)을 3대 가드로 삼음. v2.0 변경 묶음을 PR로 발행 직전 단계에서 호출.

Humanize KR 본진 v1.6 분류 체계(10대 카테고리·61+ 패턴)와 외부 학술 보고서 후보 풀(translationese-research-distiller 산출물)을 3-축 매트릭스(이미 본진·보강·신규)로 매핑해 분류학자에게 승격 결정 입력을 제공하는 갭 분석가. 사실 발견만 하고 승격 결정은 하지 않는다 — taxonomist가 최종 판정자. 본진 v1.6 → v2.0 업그레이드 회차 또는 외부 보고서를 본진과 합칠 때 호출.

한국어 번역투(translationese) 학술 보고서를 8유형·15항목 PE 체크리스트·post-editese 3축·학술 인용 계보·예문 코퍼스로 분해해 후속 분류·승격 단계가 직접 소비할 수 있는 구조화 JSON으로 증류하는 도메인 추출가. 보고서 본문에 명시된 사실만 추출하고 자체 추정·확장은 금지. 보고서가 한국 번역학계의 8대 번역투 유형(무생물 주어·피동·대명사·-들·관계절·have-make·조사 결합·종결어미)을 다루거나 Toral 2019 post-editese·Baker 1993

Handle ZenTao (禅道) Bugs and Tasks end to end, including updating or writing back an item after code changes, managing Task status and hours, and reading linked Stories. Use for referenced ZenTao items, requirements, status changes, time entries, or post-implementation synchroniza

Generate a Conventional Commits message from staged changes and wait for confirmation before committing. Use when the user asks to commit or generate a commit message.

Summarize each day's Git activity into a concise daily work log, for a single date or a range. Uses the current repository, optional configured work projects, or paths named in conversation; configuration is never required.

Review code changes for bugs, regressions, convention violations, and high-value cleanup opportunities. Use for diffs, commit ranges, hosted PR/MR URLs, branches, paths, staged changes, or working-tree changes.

Summarize a contributor's Git history, a provided work log, or both into a concise, review-friendly self-evaluation for quarterly, semi-annual, or promotion cycles.

Refactor changed code to reduce duplication, complexity, and wasted work. Use for diffs, commit ranges, PRs, paths, staged changes, or working-tree changes.

Query and display the current API provider balance and recent usage.

Inspect screenshots, photos, diagrams, image paths, and image URLs when the task depends on visible content. Use when the prompt lacks actual image content, native inspection fails, or the user requests inspect_image; prefer the MCP tool, then the installed CLI.

Use when working on accessibility, a11y, WCAG, ARIA, screen readers, keyboard nav, focus order, contrast, alt text, captions, reduced motion, or target sizes; not language/culture/device (see inclusive-design).

A green PR, a controller reporting success, and not one line of the new code running

Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.

Health-check your meeting knowledge for contradictions, stale commitments, and decision conflicts. Use when the user asks "any conflicts in my meetings", "check for stale action items", "lint my meetings", "consistency check", "are there contradictions", or wants to audit their decision history.

List recent meetings and voice memos. Use when the user asks "what meetings did I have", "show my recent recordings", "any meetings today", "list my voice memos", or wants an overview of their meeting history. Also use when they need to find a specific meeting by browsing rather than searching.

Act as the user's live meeting sidekick inside the current terminal agent session. Use when the user explicitly asks you, the terminal agent, to watch a meeting, follow the live transcript, answer during the call, offer strategist thoughts, silently watch for risks, or track decisions. Do not use this skill to start or control the separate Minutes Coach HUD; explicit Coach or HUD lifecycle requests belong to minutes-copilot, and an ambiguous request such as "coach me live" requires one short surface clarification.

Self-coaching analysis of your own behavior across meetings — talk-time ratio, filler words, hedging language, monologue length, energy patterns, and (when meetings are tagged via /minutes-tag) what your behavior in winning meetings looks like vs losing ones. Use this whenever the user says "how did I do", "review my last meeting", "mirror", "self-review", "show my patterns", "coach me", "where am I weak", "talk time", "am I improving", "what do I do in meetings I win", "feedback on me", or asks for any kind of personal feedback on their own meeting behavior. This is the rare skill that gives the user a mirror to their own habits — surface it whenever they show curiosity about their own performance, even if they don't use the word "mirror".

Add a note to the current recording or annotate a past meeting. Use whenever the user says "note that", "remember this", "mark this as important", "add a note about", "annotate the meeting", or wants to capture a thought during or after a recording. Plain text input — no markdown needed.

Interactive meeting preparation — builds a relationship brief and talking points before a call. Use when the user says "prep me for my call with", "I'm meeting with X", "prepare me for", "what should I bring up with", "meeting prep", "get ready for my call", or wants to review history with someone before a meeting.

Generate a daily digest of today's policy-authorized meetings and voice memos — key decisions, action items, and themes across available recordings. Use when the user asks "recap my day", "what happened in my meetings today", "daily summary", "what did I discuss today", "any action items from today", or wants a consolidated view of the day's conversations.

Start or stop recording a meeting, call, or voice memo. Use this whenever the user says "record", "start recording", "capture this meeting", "stop recording", "I'm in a meeting", "take notes on this call", or wants to transcribe live audio. Also use when they ask about recording status or want to know if something is being recorded.

Draft user-facing Minutes release notes for a version from the commit range, recent GitHub releases, and the repository release checks. Use when the user asks to write, generate, prepare, revise, or review release notes or a changelog for a Minutes version.

Search past meeting transcripts and voice memos for specific topics, people, decisions, or ideas. Use this whenever the user asks "what did we discuss about X", "find that meeting where we talked about Y", "what did Alex say", "did we decide on", "what was that idea about", or any question that could be answered by searching their meeting history. Also use for "do I have any notes about" or "check my meetings for".

Guided first-time setup for Minutes — download whisper model, create directories, configure audio input. Use when the user says "set up minutes", "install minutes", "first time setup", "configure minutes", "get started with minutes", "how do I start using minutes", or when verify shows missing components.

Lightweight outcome tagging for meetings — won, lost, stalled, great, or noise. Use whenever the user says "tag this meeting", "mark that as a win", "that one was a loss", "tag yesterday's call as stalled", "mark this great", "that meeting was noise", "label that meeting", or any time they describe a meeting outcome in passing. Tagging takes 5 seconds and unlocks /minutes-mirror correlation analysis — the more meetings get tagged, the smarter mirror gets at telling the user what behavior patterns lead to wins. Surface this skill any time the user mentions a meeting result, win, loss, or wasted time.

Verify that Minutes is properly set up and working — model downloaded, mic accessible, directories exist, no stale state. Use when the user says "is minutes working", "check my setup", "verify minutes", "test recording setup", "why isn't minutes working", "minutes health check", or after running setup for the first time.

Analyze a product walkthrough, bug report video, Loom, or ScreenPal using Minutes transcription plus visual review. Use when the user wants a recorded demo or bug clip turned into a durable brief with transcript, key frames, issues, and next steps.

Weekly meeting synthesis — themes, decision arcs, stale commitments, and what deserves your attention next week. Use when the user says "weekly review", "what happened this week", "weekly summary", "recap my week", "any outstanding items", "week in review", or at the end of a work week.

Search agent history or trace code to its original agent session

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

Generate a custom checklist for the current feature based on user requirements.

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.