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 commands/ashfaqbs/software-dev-ai-claude-toolkit/db-schemagit clone --depth 1 https://github.com/Ashfaqbs/software-dev-ai-claude-toolkitWrote 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/commands/ashfaqbs/software-dev-ai-claude-toolkit/db-schema)<a href="https://agentmods.dev/commands/ashfaqbs/software-dev-ai-claude-toolkit/db-schema"><img src="https://agentmods.dev/badge/commands/ashfaqbs/software-dev-ai-claude-toolkit/db-schema.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 | $0.00006 | $0.00390 |
| Opus 5 | $0.00003 | $0.00195 |
| Sonnet 5 | $0.00001 | $0.00078 |
| Haiku 4.5 | $0.00001 | $0.00039 |
Grade A, and why
db-schema 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 4d 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 Schema Design
Design the database schema for: $ARGUMENTS
Process
- Identify entities — what are the main objects and their relationships?
- Choose database — PostgreSQL (relational, transactional) vs MongoDB (document, flexible) for each entity. Justify the choice.
- Design schema:
- PostgreSQL: tables, columns, types, constraints, indexes, foreign keys
- MongoDB: collection structure, document shape, embedded vs referenced, indexes
- Plan migrations — Flyway (Java) or Alembic (Python) migration files needed.
Output Format
For PostgreSQL
CREATE TABLE table_name (
id BIGSERIAL PRIMARY KEY,
...
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
CREATE INDEX idx_table_column ON table_name(column);
For MongoDB
{
"_id": "ObjectId",
"field": "type",
"embedded": { ... },
"createdAt": "ISODate",
"updatedAt": "ISODate"
}
With index definitions and schema validation rules.
Guidelines
- Every table/collection gets
created_atandupdated_at. - PostgreSQL: use
TIMESTAMPTZ,UUIDfor public IDs,BIGSERIALfor internal PKs. - MongoDB: embed data that's read together, reference data that's updated independently.
- Add indexes for every query pattern. Explain each index choice.
- Consider data growth — will this work at 10x/100x current scale?
Rules
- DO NOT write application code yet. Schema design only.
- WAIT for approval before generating migration files.
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.
- 4d ago First seen · 54 lines · 6 tokens per session scan A 90e95e580ead
db-schema is a command published in the GitHub repository Ashfaqbs/software-dev-ai-claude-toolkit (24 stars, last pushed 6mo ago), licensed MIT. It adds 6 tokens to every session and 390 once invoked, about $0.0000 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.
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