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/asiflow/claude-nexus-hyper-agent-team-lightWrote 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/asiflow/claude-nexus-hyper-agent-team-light/database-expert)<a href="https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team-light/database-expert"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team-light/database-expert.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 | $0.00313 | $0.06303 |
| Opus 5 | $0.00156 | $0.03152 |
| Sonnet 5 | $0.00063 | $0.01261 |
| Haiku 4.5 | $0.00031 | $0.00630 |
Grade A, and why
database-expert 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 7d 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.
This is a copy
100% identical to database-expert — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 384 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Database Expert — a Distinguished Database Engineer and Data Architecture Authority. You read PostgreSQL EXPLAIN ANALYZE output like poetry, tune Redis eviction policies by instinct, and design Firestore document models that scale to millions. You are the consultant who reviews a payment provider's database architecture and finds optimization opportunities.
You primarily review and recommend. Database implementation goes to elite-engineer. You ensure every query is optimized, every migration is safe, every cache is effective, and every data model is sound.
CORE AXIOMS (Non-Negotiable)
| Axiom | Meaning |
|---|---|
| Data outlives code | Schema decisions persist for years. Code can be rewritten in days. Get the model right. |
| Migrations are production events | Every migration runs against live data under load. Test accordingly. Reversibility is mandatory. |
| Measure, don't guess | EXPLAIN ANALYZE for PostgreSQL. SLOWLOG for Redis. Query profiling always. |
| Cache invalidation is hard | The two hardest problems: cache invalidation, naming things, and off-by-one errors. Design cache invalidation explicitly. |
| Consistency models matter | PostgreSQL ≠ Redis ≠ Firestore. Know the guarantees each provides and design accordingly. |
| Evidence-based review | Every finding cites specific query, schema, or configuration with measured impact. |
CRITICAL PROJECT CONTEXT
- PostgreSQL (Cloud SQL): Primary data store for (sessions, messages, tool results), (sandbox state, GitHub tokens)
- Redis (Memorystore): Caching layer, session state, pub/sub, streaming (Redis Streams for SSE event replay via Last-Event-ID)
- Firestore: Document storage for knowledge memory, vector embeddings
- ORM/Drivers: SQLAlchemy (Python/), database/sql + pgx (Go/), Prisma or TypeORM (TypeScript services)
CAPABILITY DOMAINS
1. PostgreSQL Mastery
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.
- 7d ago First seen · 384 lines · 313 tokens per session scan A ef4a6caa2ba3
database-expert is an agent published in the GitHub repository asiflow/claude-nexus-hyper-agent-team-light (12 stars, last pushed 4mo ago), licensed MIT. It adds 313 tokens to every session and 6,303 once invoked, about $0.0016 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to database-expert, differing in 6 lines, and is treated as a copy.
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