Borrowing it
Nothing to install: this file belongs to NomaDamas/AutoRAG-Research. 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/NomaDamas/AutoRAG-Research/main/.claude/agents/schema-architect.mdgit clone --depth 1 https://github.com/NomaDamas/AutoRAG-ResearchWrote 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/nomadamas/autorag-research/schema-architect)<a href="https://agentmods.dev/agents/nomadamas/autorag-research/schema-architect"><img src="https://agentmods.dev/badge/agents/nomadamas/autorag-research/schema-architect.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 | $0.00338 | $0.00431 |
| Opus 5 | $0.00169 | $0.00216 |
| Sonnet 5 | $0.00068 | $0.00086 |
| Haiku 4.5 | $0.00034 | $0.00043 |
Grade A, and why
schema-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 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.
What it actually says
Read and follow ai_instructions/schema_architect.md.
Task:
- Convert
Source_Data_Profile.jsonintoMapping_Strategy.md - Choose the parent ingestor class and service layer
- State assumptions clearly when the source profile is ambiguous
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 · 15 lines · 0 tokens per session scan A cb3524b21928
schema-architect is an agent published in the GitHub repository NomaDamas/AutoRAG-Research (148 stars, last pushed 29d ago), licensed Apache-2.0. It adds 338 tokens to every session and 431 once invoked, about $0.0017 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
data-manager
Data manager. Handles storage, deduplication, quality validation, and export of parsed data. Establishes schema design, indexing, and incremental update strategies.
supabase-rag-implementer
Materializa RAG em Supabase em 3 layers - migration vector(N)+HNSW, RPC matchdocuments security invoker com RLS por tenant, Edge Function embedding server-side. Use ao implementar RAG.
postgres-expert
Optimizes Postgres schemas, migrations, and queries with a focus on performance, reliability, and maintainability.
vector-search-expert
Expert in semantic search, vector embeddings, and pgvector v0.8.0 optimization for memory retrieval. Specializes in OpenAI embeddings, HNSW/IVFFlat indexes with iterative scans, hybrid search strategies, and similarity algorithms.
PostgreSQL Database Administrator
Work with PostgreSQL databases using the PostgreSQL extension.
data-scientist
Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.