learning-engine

learning-engine is a skill for Claude Code, Codex from mupengi-bot/mupengism. It costs 18 tokens per session (1,722 once invoked), scanned A, original, MIT.

A system for recording mistakes, successes, and performance results, then turning repeated patterns into written rules.

In plain words
What is it for?
Use it to review error logs and self-evaluations, create learned rules, and improve processes such as image posting and browser automation.
Why use it?
It reduces repeated mistakes by preserving what worked and what failed across tasks and sessions.

Skill for Claude CodeCodex

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/mupengi-bot/mupengism/learning-engine
Any agent
npx skills add mupengi-bot/mupengism --skill learning-engine
Clone the repo
git clone --depth 1 https://github.com/mupengi-bot/mupengism

Made for: Claude Code, 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 learning-engine

README.md
[![agentmods](https://agentmods.dev/badge/skills/mupengi-bot/mupengism/learning-engine.svg)](https://agentmods.dev/skills/mupengi-bot/mupengism/learning-engine)
Your own site
<a href="https://agentmods.dev/skills/mupengi-bot/mupengism/learning-engine"><img src="https://agentmods.dev/badge/skills/mupengi-bot/mupengism/learning-engine.svg" alt="Measured on agentmods" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,722 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 $0.00018 $0.01722
Opus 5 $0.00009 $0.00861
Sonnet 5 $0.00004 $0.00344
Haiku 4.5 $0.00002 $0.00172

Measured 5d ago against content hash 6d1f2419b2b3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

learning-engine 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 5d 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/learning-engine/SKILL.md · 249 lines

How it starts

The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.

learning-engine

시스템이 실수와 성공을 기록하고, 자동으로 패턴을 학습하여 스킬을 개선합니다. "같은 실수 반복 안 함" 원칙을 자동화.

학습 소스

1. memory/errors/

에러 로그에서 실패 패턴 추출

# memory/errors/2026-02-14.md

## 10:30 - insta-post 실패
- 원인: PNG 파일 업로드 → "문제가 발생했습니다" 에러
- 해결: JPG 변환 후 재시도 → 성공
- 교훈: 인스타 게시는 항상 JPG로 변환 후 업로드

2. self-eval 결과

주간 자기평가에서 개선점 추출

# memory/self-eval/2026-W07.md

## 이번 주 실수
- 브라우저 스냅샷 너무 많이 찍음 (토큰 낭비)
- → 개선: exec로 API 직접 호출

## 이번 주 성공
- insta-cli v2로 DM 체크 토큰 95% 절약

3. performance 데이터

성과 추적에서 잘된/안 된 패턴 학습

{
  "insight": "오후 7-9시 게시물이 좋아요 +30%",
  "rule": "인스타 게시는 19:00-21:00 권장"
}

자동 규칙 생성

학습된 패턴을 규칙으로 변환:

위치: memory/learned-rules/

memory/
  learned-rules/
    instagram-posting.md
    browser-automation.md
    api-usage.md
    error-recovery.md

규칙 포맷

# Instagram 게시 규칙

## 규칙 #1: 항상 JPG 변환
- **상황**: 인스타에 이미지 업로드
- **실패 패턴**: PNG 파일 → "문제가 발생했습니다"
- **해결**: `convert input.png -quality 92 output.jpg`
- **근거**: 2026-02-10, 2026-02-14 에러 로그
- **적용 스킬**: insta-post, cardnews, social-publisher

## 규칙 #2: 1:1 비율 필수
- **상황**: 인스타 카드뉴스
- **실패 패턴**: 16:9 가로형 → 피드에서 잘림
- **해결**: 1024x1024 정사각형으로 생성
- **근거**: 2026-02-13 형님 피드백
- **적용 스킬**: cardnews, nano-banana-pro

스킬에 규칙 주입

학습된 규칙을 해당 스킬 SKILL.md에 자동 추가:

위치: skills/{skill-name}/SKILL.md

# insta-post

...

## 학습된 교훈

### 이미지 처리
- ✅ 항상 JPG로 변환 (PNG는 에러 발생)
- ✅ 1:1 비율 필수 (1024x1024 권장)
- ✅ 파일 크기 < 8MB

### 타이밍
- ✅ 19:00-21:00 게시 시 참여율 +30%
- ❌ 새벽 시간대 게시 지양

### 자동화
- ✅ exec로 API 호출 (스냅샷 0회)
- ❌ 브라우저 자동화는 최소화

주간 학습 리포트

매주 월요일 자동 생성:

위치: memory/learning/weekly-YYYY-Www.md

# 2026-W07 학습 리포트

## 새로 배운 것 (5개)

1. **인스타 PNG 금지**
   - 실수 3회 → 규칙 생성
   - 적용: insta-post, cardnews

2. **토큰 절약: exec > 브라우저**
   - v1: 스냅샷 5회 → v2: exec 1회
   - 95% 절약

3. **게시 최적 시간대**
   - 19:00-21:00 좋아요 +30%

4. **브랜드 톤 효과**
   - 무펭이 톤 참여율 +40%

5. **에러 복구 자동화**
   - browser-dependent 실패 시 → 브라우저 재시작

## 적용된 스킬
- insta-post (규칙 2개)
- cardnews (규칙 1개)
- performance-tracker (인사이트 1개)

## 다음 주 목표
- [ ] A/B 테스트 시스템 구축
- [ ] 자동 복구 패턴 3개 추가

Read the full file on GitHub · 249 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. 5d ago First seen · 249 lines · 18 tokens per session scan A 6d1f2419b2b3

Subscribe to this mod's changes

learning-engine is a skill published in the GitHub repository mupengi-bot/mupengism (10 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 1,722 once invoked, about $0.0001 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.

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