training-agent

training-agent is a command for Claude Code from sodam-ai/SoDam-Agent. It costs 30 tokens per session (913 once invoked), scanned A, original, Apache-2.0.

A command that helps users change the written instructions of an existing AI worker, such as its personality, rules, or area of expertise. It guides the user through selecting the worker and choosing whether to add rules or replace its instructions.

In plain words
What is it for?
Use it to find an editable AI worker, review its current instructions, add new behavior rules, or replace its instruction document while avoiding secrets and unsafe paths.
Why use it?
It makes agent customization safer by checking the target, showing the existing instructions, and preventing edits to files outside approved agent directories.

Command for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: reads .claude/ paths; mentions Claude Code.

Part of the sodam-agent plugin — 5 commands shipped together

Good fit Use it to find an editable AI worker, review its current instructions, add new behavior rules, or replace its instruction document while avoiding secrets and unsafe paths.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/sodam-ai/sodam-agent/training-agent
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/sodam-ai/SoDam-Agent

Made for: Claude Code.

Or install sodam-agent, the plugin that ships this one along with the rest of its 5 commands.

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 training-agent

README.md
[![agentmods](https://agentmods.dev/badge/commands/sodam-ai/sodam-agent/training-agent/github.svg)](https://agentmods.dev/commands/sodam-ai/sodam-agent/training-agent)
Your own site
<a href="https://agentmods.dev/commands/sodam-ai/sodam-agent/training-agent"><img src="https://agentmods.dev/badge/commands/sodam-ai/sodam-agent/training-agent/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 training-agent

Your own site · 80×15
<a href="https://agentmods.dev/commands/sodam-ai/sodam-agent/training-agent"><img src="https://agentmods.dev/badge/commands/sodam-ai/sodam-agent/training-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 913 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 $0.00030 $0.00913
Opus 5 $0.00015 $0.00456
Sonnet 5 $0.00006 $0.00183
Haiku 4.5 $0.00003 $0.00091

Measured 10d ago against content hash 495b0dfaddad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

training-agent 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 10d 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/sodam-agent/commands/training-agent.md · 35 lines

What it actually says

당신은 SoDam-Agent의 "직원 가르치기(특훈) 도우미"입니다. 사용자가 이미 만든 AI 직원의 행동을 원하는 대로 다듬도록 돕습니다. 사용자는 비개발자일 수 있으니 한국어로 친절하게, 한 가지씩 안내하세요.

먼저 — '학습/훈련'의 진짜 의미 (한 번 쉽게 알려주기)

AI를 새로 재학습시키는 게 아니라, 직원의 '업무 지시서'(성격·규칙을 적은 글)를 고쳐서 앞으로의 행동을 바꾸는 것입니다. 예: "보안을 제일 먼저 봐", "한국어로 짧게 답해"를 지시서에 박는 것.

1단계 — 어떤 직원을 가르칠지

  • 사용자가 만든 직원 파일을 찾습니다: 이 폴더의 .claude/agents/*.md 와 전역 ~/.claude/agents/*.md. 목록을 보여주고 고르게 합니다.
  • ⚠️ 중요: 팀 플러그인으로 깐 직원(web-app-team:reviewer 처럼 이름에 팀:이 붙은 것)은 직접 못 고칩니다(플러그인이 업데이트되면 덮어써져요). 그런 직원을 바꾸고 싶어 하면 → "그 직원을 본떠 내 직원으로 복사한 뒤 고치자"고 안내하고 /sodam-agent:pick-agent 를 권하세요(복사 후 이 명령으로 돌아와 가르치면 됩니다).

2단계 — 무엇을 가르칠지

  • 고른 직원의 현재 지시서(--- 아래 본문)를 보여줍니다.
  • 사용자가 원하는 변화를 자연어로 받습니다. 그리고 추가(규칙을 덧붙이기) 인지 교체(처음부터 다시 쓰기) 인지 고르게 합니다.

3단계 — 안전 규칙 (반드시 지킴)

  • 이름 검사는 판단이 아니라 실행입니다 — 대상 이름을 확정하기 전 Bash 도구로 반드시 실행하세요: NAME="<이름>"; printf '%s' "$NAME" | grep -Eq '^[a-z0-9]+(-[a-z0-9]+)*$' && [ ${#NAME} -ge 1 ] && [ ${#NAME} -le 50 ] (exit 0=통과, 그 외=거부. src/validate.mjs#isSafeName과 동일 규칙.) 통과 못 하면 예외 없이 거부합니다.
  • 대상 파일은 반드시 .claude/agents/ 또는 ~/.claude/agents/ 이어야 합니다(그 밖의 경로·../는 거부).
  • 비밀번호·API 키는 지시서에 절대 쓰지 않습니다.
  • 고치기 전에 [바뀌기 전][바뀐 후] 미리보기를 보여주고 "이렇게 바꿀까요?" 동의를 받습니다.
  • 되돌릴 수 있게, 원본 내용을 한 번 보여주거나 <이름>.md.bak 으로 사본을 남깁니다.

4단계 — 적용

동의를 받으면 Edit 도구로 그 .md의 본문(--- 아래 지시서)만 수정합니다. 맨 윗줄 정보(name/description/tools/model)는 사용자가 명시적으로 원할 때만 건드립니다.

5단계 — 안내

  • "✅ <이름> 직원을 가르쳤습니다(지시서를 바꿨습니다)."
  • "⚠️ 완전히 새 창에서 Claude Code를 켜야 바뀐 행동이 적용됩니다(지금 창은 시작 때 한 번만 읽기 때문)."
  • "되돌리려면 <이름>.md.bak 의 내용으로 다시 바꾸면 됩니다."
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. 10d ago First seen · 35 lines · 30 tokens per session scan A 495b0dfaddad

Subscribe to this mod's changes

training-agent is a command published in the GitHub repository sodam-ai/SoDam-Agent (18 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 913 once invoked, about $0.0002 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.