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.
git clone --depth 1 https://github.com/jhlee0409/claude-pluginsWrote 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.
[](https://agentmods.dev/commands/jhlee0409/claude-plugins/explain)<a href="https://agentmods.dev/commands/jhlee0409/claude-plugins/explain"><img src="https://agentmods.dev/badge/commands/jhlee0409/claude-plugins/explain/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.
<a href="https://agentmods.dev/commands/jhlee0409/claude-plugins/explain"><img src="https://agentmods.dev/badge/commands/jhlee0409/claude-plugins/explain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00008 | $0.02274 |
| Opus 5.5 | $0.00003 | $0.00910 |
| Sonnet 5.5 | $0.00002 | $0.00455 |
| Haiku 4.5 | $0.00001 | $0.00227 |
Grade A, and why
explain 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/fsdarch:explain
FSD 개념을 프로젝트 맥락에서 설명합니다. 학습 곡선을 낮추고 팀 내 이해도를 통일합니다.
Syntax
/fsdarch:explain <topic>
Available Topics
| Topic | Description |
|---|---|
layers |
7개 레이어와 각각의 역할 |
feature-vs-widget |
Feature와 Widget의 차이 |
entity-vs-feature |
Entity와 Feature의 차이 |
public-api |
Public API 패턴과 이유 |
segments |
ui, model, api, lib 세그먼트 |
cross-imports |
Cross-import 금지 규칙 |
hierarchy |
레이어 계층 규칙 |
shared |
Shared 레이어 사용법 |
slices |
슬라이스 분리 기준 |
<custom> |
자유 질문 (FSD 관련) |
Execution Flow
Step 1: Parse Topic
- 미리 정의된 토픽인지 확인
- 아니면 자유 질문으로 처리
Step 2: Load Project Context
.fsd-architect.json로드 (있으면)- 현재 프로젝트 구조 분석
- 실제 예시 준비
Step 3: Generate Explanation
- 개념 설명 (이론)
- 프로젝트 예시 (실제)
- 관련 규칙
- 추가 학습 자료
Step 4: Display Result
토픽별 맞춤형 출력.
Examples
Example 1: Feature vs Widget
/fsdarch:explain feature-vs-widget
═══════════════════════════════════════════════════════════════
FEATURE vs WIDGET
═══════════════════════════════════════════════════════════════
📚 핵심 개념
Feature (기능)
→ 사용자 시나리오 단위의 기능
→ "사용자가 ~할 수 있다"로 설명 가능
→ 비즈니스 로직 포함 가능
→ 다른 Feature와 독립적
Widget (위젯)
→ 여러 Feature를 조합한 UI 블록
→ 자체 비즈니스 로직 최소화
→ 페이지에서 재사용되는 "덩어리"
───────────────────────────────────────────────────────────────
🎯 결정 기준
"이것이 독립적인 사용자 행동인가?"
→ YES: Feature (예: "로그인", "장바구니 추가", "리뷰 작성")
→ NO: Widget (예: "헤더", "사이드바", "제품 카드 그리드")
"다른 기능과 조합되어야 하는가?"
→ YES: Widget (예: HeaderWidget = auth + cart + search 조합)
→ NO: Feature
───────────────────────────────────────────────────────────────
📁 당신의 프로젝트 예시
Features (현재 8개):
├── auth/ → 사용자가 "로그인할 수 있다"
├── cart/ → 사용자가 "장바구니를 관리할 수 있다"
├── checkout/ → 사용자가 "결제할 수 있다"
├── favorites/ → 사용자가 "찜 목록을 관리할 수 있다"
└── ...
Widgets (현재 3개):
├── header/ → auth + cart + search 조합
├── product-card/ → product entity + favorites feature 조합
└── sidebar/ → navigation + user-info 조합
───────────────────────────────────────────────────────────────
⚠️ 흔한 실수
1. Feature가 너무 큼
✗ features/user/ (너무 포괄적)
✓ features/user-profile/, features/user-settings/
2. Widget에 비즈니스 로직
✗ widgets/header/model/authLogic.ts
✓ features/auth/ 에서 로직, widget은 조합만
3. Feature 간 의존성
✗ features/auth → features/cart
✓ widgets/auth-cart 에서 조합
───────────────────────────────────────────────────────────────
📚 더 알아보기
• https://feature-sliced.design/docs/reference/layers#features
• https://feature-sliced.design/docs/reference/layers#widgets
═══════════════════════════════════════════════════════════════
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.
- 1mo ago First seen · 339 lines · 8 tokens per session scan A 8e9e551a2c6a
explain is a command published in the GitHub repository jhlee0409/claude-plugins (4 stars, last pushed 9mo ago), licensed MIT. It adds 8 tokens to every session and 2,274 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
speckit.gym.drop
Record a drop card from a mis-fire. A mis-fire is not a mistake to punish — it is a discovery to keep. Drop cards capture what was done, what was expected, and what happened, persisted to the configured redundant stores (default /drops/ plus the extension's append-only .drop-ledger.md).
speckit.gym.run
Full training run: warmup reps grow reflexes, graded exercises prove skill under load, and the gate opens only when every rep is grown and every exercise is passed.
speckit.gym.gate
Query the current gate state. This is the host-enforcement surface: a host (CLI, CI, service, or the optional beforeimplement hook) queries it before letting an operator perform the real task.
speckit.gym.init
Scaffold a starter GYM curriculum in the current Spec Kit project so its operators can be trained and gated.
speckit.gym.warmup
Warmup only — grow the operator's reflexes without the graded exercises. Skips Phase 2 entirely.
learn
Start learning a new topic — asks clarifying questions, researches resources, and creates a structured learning plan.