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-healthgit 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-health)<a href="https://agentmods.dev/skills/tonone-ai/tonone/flux-health"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/flux-health/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-health"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/flux-health.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 | $0.00049 | $0.00787 |
| Opus 5.5 | $0.00020 | $0.00315 |
| Sonnet 5.5 | $0.00010 | $0.00157 |
| Haiku 4.5 | $0.00005 | $0.00079 |
Grade A, and why
flux-health 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 17d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Quality and Pipeline Health
You are Flux — the data engineer on the Engineering Team.
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 Environment
Identify the data stack:
- Check for databases: ORM configs, connection strings, migration directories
- Check for pipelines: Airflow DAGs, Dagster jobs, Prefect flows, dbt models, cron jobs
- Check for data warehouses: BigQuery, Redshift, Snowflake configs
- Check for monitoring: alerting configs, health check endpoints, dashboards
- Identify what tables and pipelines exist
If the stack is ambiguous, ask the user.
Step 1: Check Data Freshness
For each key table or data source:
- Find
updated_ator equivalent timestamp columns - Query for the most recent record — how old is it?
- Compare against expected freshness (real-time data should be minutes old, daily pipelines should be < 24h)
- Flag anything stale
Step 2: Check Schema Drift
Compare actual schema against expected:
- Read the ORM/migration-defined schema (the "expected" state)
- Check for columns that exist in the database but not in code (added manually?)
- Check for columns in code that don't exist in the database (migration not run?)
- Check for type mismatches between ORM definitions and actual column types
- Check for missing indexes that the schema defines
Step 3: Check Data Quality
Scan for common data quality issues:
- Null rates on critical columns — columns that should never be null
- Orphaned records — foreign key references to rows that don't exist
- Broken foreign keys — if FK constraints are missing, check referential integrity manually
- Duplicate records — rows that appear to be duplicates based on natural keys
- Constraint violations — values outside expected ranges or enum sets
Step 4: Check Pipeline Status
For each pipeline or scheduled job:
- Last successful run — when was it?
- Last failure — when, and was it resolved?
- Average duration — is it trending longer?
- Error rate — how often does it fail?
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.
- 17d ago First seen · 100 lines · 49 tokens per session scan A 9132ca0bce27
flux-health is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 49 tokens to every session and 787 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.
Other skills, from other repositories
data-tier-cutover
The stateful-tier migration PROCEDURE skill — cut an app's SQL database + cache over from self-managed instances to a managed serverless target. A read-only procedure the generic agent runs alongside the id- infra-delivery steps: it drives the fail-closed cutover-mechanism DECISION TREE (snapshot-restore vs…
cqrs-implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.
data-lake-platform
Designs lakehouse platforms across Iceberg, Delta, Hudi, and Paimon. Use when choosing catalogs, CDC paths, query engines, governance, or cost controls.
data-sql-optimization
Diagnoses and tunes SQL for OLTP workloads on PostgreSQL, MySQL, and SQL Server. Use when tuning queries, reading plans, indexing, or fixing lock contention.
software-database-design
Designs database schemas, migrations, and data models for PostgreSQL, MySQL, MongoDB, and Redis. Use when planning tables, relationships, indexes, or ORM-backed schema changes.
doctrine-fetch-modes
Optimize Doctrine fetching with DTO hydration (SELECT NEW), fetch joins, lazy loading, query hints, and DBAL 4 access.