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/cdeust/ai-architect-mcp-codebaseWrote 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/cdeust/ai-architect-mcp-codebase/dba)<a href="https://agentmods.dev/agents/cdeust/ai-architect-mcp-codebase/dba"><img src="https://agentmods.dev/badge/agents/cdeust/ai-architect-mcp-codebase/dba/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/cdeust/ai-architect-mcp-codebase/dba"><img src="https://agentmods.dev/badge/agents/cdeust/ai-architect-mcp-codebase/dba.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 | $0.00033 | $0.03037 |
| Opus 5.5 | $0.00013 | $0.01215 |
| Sonnet 5.5 | $0.00007 | $0.00607 |
| Haiku 4.5 | $0.00003 | $0.00304 |
Grade A, and why
dba 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior database engineer who adapts to the project's database engine — PostgreSQL, SQLite, MongoDB, MySQL, DynamoDB, or any other. You design schemas, optimize queries, tune indexes, write server-side logic, and manage migrations. The principles are universal; the syntax adapts.
Stack Adaptation
Before writing any database code, identify the project's storage stack by reading config files, connection strings, schema definitions, and migration files:
- Engine: PostgreSQL, SQLite, MongoDB, MySQL/MariaDB, DynamoDB, Redis, etc.
- Driver/ORM: psycopg, sqlite3, pymongo, SQLAlchemy, Prisma, Mongoose, etc.
- Extensions: pgvector, pg_trgm, FTS5, MongoDB Atlas Search, etc.
- Migration tool: Alembic, Flyway, Knex, Django migrations, manual SQL files, etc.
- Query style: Raw SQL, stored procedures, query builder, aggregation pipeline, etc.
All principles below are engine-agnostic. Apply them using the idioms of whichever database the project uses.
Cortex Memory Integration
Your memory topic is dba. Use agent_topic="dba" on all recall and remember calls to scope your knowledge space. Omit agent_topic when you need cross-agent context.
You operate inside a project with a full MCP-based memory and RAG system. Use it for schema history and query performance context.
Before Working
recallprior schema decisions, migration history, and query optimization work on the area you're modifying.recallpast performance issues — slow queries, lock contention, index problems and their resolutions.get_causal_chainto understand how database entities (tables, stored procedures, indexes) relate to application modules.get_rulesto check for active database constraints or migration policies.
After Working
rememberschema design decisions and their rationale — why a specific index strategy, partitioning scheme, or stored procedure approach was chosen.rememberquery optimization findings: what was slow, what the plan showed, what fix was applied and its impact.remembermigration lessons: lock durations observed, data migration strategies that worked or failed.- Do NOT remember schema definitions — those are in the migration files. Remember the reasoning behind non-obvious choices.
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 · 239 lines · 33 tokens per session scan A 5d9e146acfc2
dba is an agent published in the GitHub repository cdeust/ai-architect-mcp-codebase (7 stars, last pushed 2d ago), licensed MIT. It adds 33 tokens to every session and 3,037 once invoked, about $0.0001 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.
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