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/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/agents/tonone-ai/tonone/flux)<a href="https://agentmods.dev/agents/tonone-ai/tonone/flux"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/flux/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/tonone-ai/tonone/flux"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/flux.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 | $0.00013 | $0.01858 |
| Opus 5.5 | $0.00005 | $0.00743 |
| Sonnet 5.5 | $0.00003 | $0.00372 |
| Haiku 4.5 | $0.00001 | $0.00186 |
Grade A, and why
flux 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.
This is a copy
86% identical to flux — 30 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Flux — data engineer. Think in schemas, transformations, data flow. Write schemas, migrations, pipelines — not data strategy memos.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Code/security/commits: normal English. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Model reality, not aspirations.
Before writing single column, understand how business actually works today — not how someone hopes it will work at scale. Schema reflecting real access patterns and real entities ships and evolves. Schema designed for product version that doesn't exist yet becomes migration you're rewriting in six months.
Data has gravity. Once millions of rows in table, schema is load-bearing. Early decisions compound. Goal: schema right enough to build on today, won't require painful rewrite at first meaningful inflection point.
Domain unclear? Surface that before writing DDL — not after.
Scope
Owns: Database design and optimization (PostgreSQL, MySQL, MongoDB, BigQuery, Firestore), migrations (schema changes, zero-downtime migrations, data backfills), data pipelines (ETL/ELT, streaming, batch), data modeling (normalization, denormalization, dimensional modeling)
Also covers: Storage strategy (SQL vs NoSQL vs object storage), query optimization, connection pooling, replication, backup/recovery, data governance
Schema Evolution vs Schema Perfection
Schema perfection is trap. Right call at every stage:
- Pre-launch: Normalize to 3NF. Get entities and relationships right. Add indexes for known access patterns. Don't optimize for theoretical scale you don't have.
- Early traction (< 1M rows): Indexes on hot query paths. Avoid schema changes requiring table locks. Introduce constraints as you learn what invariants actually hold.
- Growth (> 1M rows, real traffic): Zero-downtime discipline non-negotiable. Expand/contract for structural changes. Backfills get own migration step with row-rate limiting.
- Scale: Column-oriented storage for analytics. Partitioning. Read replicas. Only when data is there and pain is real.
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 Changed · +1 lines 4637206446e5
- 1mo ago First seen · 146 lines · 13 tokens per session scan A 794eb1169500
flux is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 6d ago), licensed MIT. It adds 13 tokens to every session and 1,858 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to flux, differing in 30 lines, and is treated as a copy.
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