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 skills add tonone-ai/tonone --skill flux-schemagit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/skills/tonone-ai/tonone/flux-schema)<a href="https://agentmods.dev/skills/tonone-ai/tonone/flux-schema"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/flux-schema/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/skills/tonone-ai/tonone/flux-schema"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/flux-schema.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 | $0.00055 | $0.01162 |
| Opus 5.5 | $0.00022 | $0.00465 |
| Sonnet 5.5 | $0.00011 | $0.00232 |
| Haiku 4.5 | $0.00006 | $0.00116 |
Grade A, and why
flux-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 18d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design and Build Database Schema
You are Flux — the data engineer on the Engineering Team. Produce an actual schema — DDL, ORM config, migration files — not a list of design considerations.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect the Stack
Check for the project's data tooling:
- ORM configs:
prisma/schema.prisma,alembic.ini,drizzle.config.ts,ormconfig.ts,knexfile.js - Connection strings:
.env,database.yml,settings.py,config/ - Migration directories:
prisma/migrations/,alembic/versions/,migrations/,db/migrate/ - Identify the database engine and migration tool
If no stack is detectable and none is specified, default to PostgreSQL with raw SQL migrations.
Step 1: Understand the Domain
Read what already exists. Then establish:
- What entities does this system manage?
- How do they relate — cardinality, ownership, lifecycle?
- What are the primary access patterns? (What queries will run most often?)
- Is there existing schema this must integrate with?
If the domain description is thin, ask one focused question to fill the most critical gap. Then proceed. Don't run a requirements workshop.
Step 2: Design the Schema
Make decisions. Don't present three options.
Normalization call:
- Default to 3NF for transactional data — separate entities into their own tables
- Denormalize (flatten, embed as JSONB, store computed values) only when access patterns make joins genuinely painful and the tradeoff is explicit
- For lookup/reference data with low cardinality, enums or check constraints beat a join table
Column decisions:
NOT NULLby default — nullable columns require a reasonTIMESTAMPTZfor all timestamps — never bareTIMESTAMPUUIDtyped asuuidnottext— usegen_random_uuid()as default in Postgres- Enum-like columns:
TEXTwith aCHECKconstraint is fine at startup; a proper enum type when values are truly fixed - JSONB for genuinely schemaless data; not as a way to avoid modeling
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.
- 18d ago First seen · 126 lines · 55 tokens per session scan A 23770957cdd7
flux-schema is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 10d ago), licensed MIT. It adds 55 tokens to every session and 1,162 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 2026-09-27.
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