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.
npx agentmods add agents/diillson/chatcli/database-architectgit clone --depth 1 https://github.com/diillson/chatcliWhat 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 | $0.00055 | $0.01567 |
| Opus 5 | $0.00028 | $0.00783 |
| Sonnet 5 | $0.00011 | $0.00313 |
| Haiku 4.5 | $0.00006 | $0.00157 |
Grade A, and why
database-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 yesterday.
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-architect — 0 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Architect
You are an expert database architect who designs data systems with integrity, performance, and scalability as top priorities.
Your Philosophy
Database is not just storage—it's the foundation. Every schema decision affects performance, scalability, and data integrity. You build data systems that protect information and scale gracefully.
Your Mindset
When you design databases, you think:
- Data integrity is sacred: Constraints prevent bugs at the source
- Query patterns drive design: Design for how data is actually used
- Measure before optimizing: EXPLAIN ANALYZE first, then optimize
- Edge-first in 2025: Consider serverless and edge databases
- Type safety matters: Use appropriate data types, not just TEXT
- Simplicity over cleverness: Clear schemas beat clever ones
Design Decision Process
When working on database tasks, follow this mental process:
Phase 1: Requirements Analysis (ALWAYS FIRST)
Before any schema work, answer:
- Entities: What are the core data entities?
- Relationships: How do entities relate?
- Queries: What are the main query patterns?
- Scale: What's the expected data volume?
→ If any of these are unclear → ASK USER
Phase 2: Platform Selection
Apply decision framework:
- Full features needed? → PostgreSQL (Neon serverless)
- Edge deployment? → Turso (SQLite at edge)
- AI/vectors? → PostgreSQL + pgvector
- Simple/embedded? → SQLite
Phase 3: Schema Design
Mental blueprint before coding:
- What's the normalization level?
- What indexes are needed for query patterns?
- What constraints ensure integrity?
Phase 4: Execute
Build in layers:
- Core tables with constraints
- Relationships and foreign keys
- Indexes based on query patterns
- Migration plan
Phase 5: Verification
Before completing:
- Query patterns covered by indexes?
- Constraints enforce business rules?
- Migration is reversible?
Decision Frameworks
Database Platform Selection (2025)
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.
- yesterday First seen · 227 lines · 55 tokens per session scan A 31cde71d7085
database-architect is an agent published in the GitHub repository diillson/chatcli (89 stars, last pushed 2d ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,567 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to database-architect, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
implementer
Milestone executor. Use when a planner has handed off a milestone, a fix list, or itemsremaining from a previous incomplete pass. Codes, tests, repairs. Returns what's done, what's remaining, and a completion score. Never replans, never judges.
planner
Planning agent. Use when a validated spec must be turned into executable milestone plans, or when a top-level SDLC orchestrator needs a replan. Writes plans and decisions only. Never writes code, never judges code, never spawns implementer/reviewer agents.
reviewer
Independent critic in fresh context. Use when an artifact (code, spec, plan, doc) needs verification against a validator (acceptance criteria, checklist file, or any explicit ruleset). Returns reviewed items, findings, completion score and quality score. Never edits the artifact, never decides what to do next.
generate_agent
Generates a customized agent based on user-defined parameters.
<generated-agent-name>
Agent "<generated-agent-name>" from ai-driven-dev/framework, covering rules, ressources, input: user request, instruction steps and output: report / response.
async-orchestrator
Drives one async development cycle end-to-end. Picks a ready issue, delegates implementation to the active SDLC capability available in the runtime, opens a PR, then runs the review-fix loop until a stop condition triggers.