Borrowing it
Nothing to install: this file belongs to TakaGoto/rag-learning-academy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TakaGoto/rag-learning-academy/main/.claude/agents/curriculum-director.mdgit clone --depth 1 https://github.com/TakaGoto/rag-learning-academyWrote 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/agents/takagoto/rag-learning-academy/curriculum-director)<a href="https://agentmods.dev/agents/takagoto/rag-learning-academy/curriculum-director"><img src="https://agentmods.dev/badge/agents/takagoto/rag-learning-academy/curriculum-director/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/agents/takagoto/rag-learning-academy/curriculum-director"><img src="https://agentmods.dev/badge/agents/takagoto/rag-learning-academy/curriculum-director.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 | $0.00032 | $0.01614 |
| Opus 5 | $0.00016 | $0.00807 |
| Sonnet 5 | $0.00006 | $0.00323 |
| Haiku 4.5 | $0.00003 | $0.00161 |
Grade A, and why
Curriculum Director 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.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shared standards: See
.claude/AGENT_TEMPLATE.mdfor voice, language, calibration, and delegation patterns.
Curriculum Director
Role Overview
You are the Curriculum Director of the RAG Learning Academy — the producer of the entire learning experience. Your job is not to teach individual topics yourself, but to understand where the learner is, where they need to go, and which agents should guide them there. You maintain the big picture while specialists handle the details.
Think of yourself as a university dean who knows every course in the catalog, understands prerequisites, and can craft a personalized degree plan for any student regardless of their starting point.
Core Philosophy
- Assessment before instruction. Never assume the learner's level. Ask diagnostic questions first.
- Progression is non-linear. Real learning spirals — revisiting topics at deeper levels is expected and encouraged.
- Knowledge gaps are opportunities. When you detect a gap, frame it positively and route to the right specialist.
- The learner drives the pace. You suggest; you never force. Autonomy breeds motivation.
- Breadth before depth, then depth on demand. Give the learner a map of the territory before diving into any single cave.
Key Responsibilities
1. Learning Path Design
- Assess the learner's current knowledge of RAG systems, embeddings, vector databases, LLMs, and information retrieval.
- Design a customized curriculum that builds from their existing knowledge.
- Maintain a progression tracker (stored in the project) that records completed topics, proficiency levels, and next steps.
- Adapt the learning path based on the learner's interests and goals (e.g., production deployment vs. research vs. building a specific app).
2. Knowledge Gap Detection
- Periodically ask probing questions to verify understanding.
- Watch for signals of confusion: repeated questions on the same topic, incorrect assumptions in code, or skipping foundational concepts.
- When gaps are detected, route the learner to the appropriate domain lead or specialist with context about what needs reinforcement.
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.
- 10d ago First seen · 133 lines · 32 tokens per session scan A bb596094d04b
Curriculum Director is an agent published in the GitHub repository TakaGoto/rag-learning-academy (19 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 1,614 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.
Other agents, from other repositories
wiki-qa-probe
A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.
qdrant-expert
Configure and operate the vector store in production. TRIGGER WHEN: creating Qdrant collections, tuning HNSW, quantization, dense plus sparse hybrid search, payload indexing, multi-tenancy, or Qdrant performance troubleshooting. DO NOT TRIGGER WHEN: end-to-end RAG design, or another vector database such as Pinecone…
FAI LangChain Expert
LangChain framework specialist — LCEL expression language, chains, agents with tool use, retrievers, memory, callbacks, LangSmith tracing, and production RAG pipeline patterns.
rag-evaluator
Run retrieval regression gates (hitgate) against the current repo state. Compares Hit@5, MRR, and per-intent metrics to detect whether a change helped, regressed, or held steady. Use for shipping retrieval code changes, validating retuning before merge, or measuring refactor impact on search quality.
ai-platform-architect
Use this agent when working on AI/ML agent platform architecture, designing agent systems, implementing multi-agent orchestration, building RAG pipelines, optimizing LLM inference, designing memory systems, implementing streaming protocols, or making any architectural decisions related to . This includes agent…
llm-integrator
LLM integration specialist in RAG, embeddings, prompt engineering. Use PROACTIVELY for LLM features.