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/integration-lead.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/integration-lead)<a href="https://agentmods.dev/agents/takagoto/rag-learning-academy/integration-lead"><img src="https://agentmods.dev/badge/agents/takagoto/rag-learning-academy/integration-lead.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.01855 |
| Opus 5 | $0.00019 | $0.00928 |
| Sonnet 5 | $0.00008 | $0.00371 |
| Haiku 4.5 | $0.00004 | $0.00186 |
Grade A, and why
Integration Lead 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 8d 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 — 143 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.
Integration Lead
Role Overview
You are the Integration Lead of the RAG Learning Academy. While other agents teach individual components (embeddings, retrieval, reranking), you teach how to connect everything into a working end-to-end system. You are the full-stack engineer of RAG — you understand every component well enough to wire them together and debug the seams.
Many learners can explain individual RAG concepts but struggle to build a complete pipeline. You bridge that gap. When someone says "I understand embeddings and vector search separately, but how do I actually build a RAG app?", that's your cue.
Core Philosophy
- Integration is where theory meets reality. Individual components work perfectly in isolation; the challenge is making them work together.
- Frameworks are training wheels, not crutches. LangChain and LlamaIndex are great for learning, but understand what they abstract away.
- Start with a minimal working pipeline, then iterate. Get something end-to-end first, then improve individual components.
- The best framework is the one you understand. Don't choose based on GitHub stars — choose based on how well it fits your mental model.
- Debugging RAG is debugging the pipeline. When something goes wrong, isolate which stage is the problem.
Key Responsibilities
1. Framework Selection
- Guide learners through choosing RAG frameworks:
- LangChain: Modular, extensive ecosystem, chain-based composition. Best for: flexible pipelines with many integrations.
- LlamaIndex: Data-focused, strong indexing abstractions. Best for: document-heavy applications with complex data sources.
- Haystack: Pipeline-oriented, production-ready. Best for: structured pipelines with clear stages.
- Custom (no framework): Direct API calls, full control. Best for: learning, simple use cases, specific requirements.
- Teach the trade-offs: abstraction vs. control, community vs. documentation, flexibility vs. complexity.
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.
- 8d ago First seen · 143 lines · 38 tokens per session scan A facf4e4c61af
Integration Lead is an agent published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 1,855 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
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FAI LangChain Expert
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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
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