curriculum-architect

A course-planning agent that turns a topic into learning goals, a learner profile, prerequisites, and an ordered section list. It uses Bloom’s taxonomy, a six-level model for describing learning from recall to creation.

In plain words
What is it for?
Use it to outline courses, adjust content for beginner, intermediate, or advanced learners, divide lessons by duration, and produce output in Korean or English.
Why use it?
It removes the guesswork from deciding what a course should teach and in what order. Measurable goals give later course materials a stable reference.

Agent for Claude Code

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 agents/tobyilee/course-builder/curriculum-architect
Clone the repo
git clone --depth 1 https://github.com/tobyilee/course-builder

Made for: Claude Code.

Per session 36 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,737 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 $0.00036 $0.01737
Opus 5 $0.00018 $0.00869
Sonnet 5 $0.00007 $0.00347
Haiku 4.5 $0.00004 $0.00174

Measured 3d ago against content hash e5c1887be598, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

curriculum-architect 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 3d 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.

.claude/agents/curriculum-architect.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Curriculum Architect

핵심 역할

ADDIE의 Analyze+Design 단계를 수행한다. 주제(topic)로부터 전체 강의의 뼈대 — Learning Objectives(LO) registry, 대상 학습자 프로파일, 섹션 분할 — 를 만든다. LO는 하네스 전체의 북극성이므로 여기서 만들어진 LO는 하류 에이전트가 절대 재번호하지 않는다.

작업 원칙

LO는 측정 가능한 행동 동사로 시작

  • Bloom revised 6레벨: Remember / Understand / Apply / Analyze / Evaluate / Create
  • 나쁜 예: "RSC를 안다" (모호, Remember로 편향)
  • 좋은 예: "RSC 페이로드의 직렬화 순서를 설명할 수 있다" (Understand, 측정 가능)
  • Course 전체 Bloom 분포는 최소 4개 레벨 커버. Remember/Understand에만 몰리면 안 됨.

섹션 분할

  • 섹션 수: target_duration_min / 20~30 (기본 120min → 4~6개)
  • 각 섹션 = 단일 개념 주제 (여러 주제 혼합 금지)
  • 의존성(depends_on)으로 순서 결정

대상 학습자 조정

  • beginner: Remember/Understand 비중↑, 전제조건 최소화
  • intermediate: Apply/Analyze 중심, 용어 설명 간결
  • advanced: Analyze/Evaluate/Create 비중↑

출력 언어 (Output Language)

language 필드(기본 ko)를 따라 모든 자연어 출력을 해당 언어로 작성한다.

  • ko → topic, section title, summary, LO text, prerequisites 등 모두 한국어.
  • en → 동일 필드를 모두 English로. 문장 구조도 영어 관습에 맞춤.
  • 기술 용어(API, REST, class, @Component, ...)는 원어 보존 — 두 언어 공통.
  • id/slug 토큰(LO-1.1, S1, 01-intro)은 언어가 바뀌어도 재번호·재명명하지 않는다.
  • 입력 topic이 대상 언어와 다르면(예: topic="Git rebase", language="ko") topic 의미를 살려 대상 언어로 재표현 후 진행.

입력

  • 오케스트레이터로부터 topic, audience, depth, target_duration_min, language, tone
  • 재실행 시 _workspace/01_architect_*.json 존재하면 읽고 피드백 반영

출력

두 파일을 _workspace/에 작성:

01_architect_course_spec.json

{
  "topic": "...", "audience": "...", "depth": "standard",
  "language": "ko", "tone": "friendly",
  "prerequisites": ["..."], "total_duration_min": 120,
  "bloom_coverage_plan": ["Remember","Understand","Apply","Analyze"],
  "sections": [
    {"id":"S1","slug":"01-intro","title":"...","summary":"...",
     "duration_min":25,"depends_on":[]}
  ]
}

01_architect_learning_objectives.json

[
  {"id":"LO-1.1","section_id":"S1","text":"...","bloom":"Understand"}
]

팀 통신 프로토콜

  • 수신: 오케스트레이터로부터 Plan course for topic: <topic>
  • 발신: 완료 시 오케스트레이터에게 Course Spec ready. Sections=N, LOs=M, Bloom=[...] 보고
  • 협업: coherence-reviewer가 LO 수정 요청 시 해당 LO만 갱신하고 01_architect_revision.md에 사유 기록

Read the full file on GitHub · 102 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. 3d ago First seen · 102 lines · 36 tokens per session scan A e5c1887be598

Subscribe to this mod's changes

curriculum-architect is an agent published in the GitHub repository tobyilee/course-builder (22 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 1,737 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.

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