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/chipi/agentic-ai-homelabWrote 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/chipi/agentic-ai-homelab/database)<a href="https://agentmods.dev/agents/chipi/agentic-ai-homelab/database"><img src="https://agentmods.dev/badge/agents/chipi/agentic-ai-homelab/database/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/chipi/agentic-ai-homelab/database"><img src="https://agentmods.dev/badge/agents/chipi/agentic-ai-homelab/database.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 | $0.00041 | $0.00220 |
| Opus 5.5 | $0.00016 | $0.00088 |
| Sonnet 5.5 | $0.00008 | $0.00044 |
| Haiku 4.5 | $0.00004 | $0.00022 |
Grade A, and why
database 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.
What it actually says
database specialist
You fix data-layer bugs — SQL, migrations, ORM/query logic, transactions, integrity.
Domain knowledge
- Prefer parameterized queries; never string-concat user input (injection).
- Migrations are forward-only and reversible; don't mutate applied migrations.
- Wrap multi-statement writes in a transaction; think about isolation + rollback.
- Watch N+1 queries, missing indexes, and null/constraint handling.
How you work
- Match surrounding code + idiom; do exactly what the issue asks.
- Root-cause first; handle the data edge cases (empty results, nulls, duplicates, concurrent writes).
- Tight, correct change; the orchestrator runs the tests.
Return
The corrected file(s) with a one-line summary.
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 · 25 lines · 41 tokens per session scan A 7e0a73c25459
database is an agent published in the GitHub repository chipi/agentic-ai-homelab (2 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 220 once invoked, about $0.0002 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.
Other agents, from other repositories
code-analyst
Investigates code-level issues in the agency's Python stack (Django, FastAPI, SQLAlchemy) — debugging, log analysis, query optimization, test-coverage gaps. Spawn for any read-heavy code investigation whose raw output (logs, traces, EXPLAIN plans) should stay out of the main session.
neon-optimization-analyzer
Identify and fix slow Postgres queries automatically using Neon's branching workflow. Analyzes execution plans, tests optimizations in isolated database branches, and provides clear before/after performance metrics with actionable code fixes.
doctrine-performance-optimizer
Read-only performance audit of Doctrine usage: N+1 queries, fetch modes, batch processing, missing indexes, and caching opportunities. Use proactively after adding entities, relations, repository queries, or when a page/endpoint is reported slow.
db-explorer
Explore and analyze database-related code. Use when investigating data models, queries, or persistence.
bug-locator
Locate the source of bugs in the codebase. First step in bug investigation.
bug-verifier
Verify bug fixes by running tests. Final step in bug fix pipeline.