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-pipelinegit 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-pipeline)<a href="https://agentmods.dev/skills/tonone-ai/tonone/flux-pipeline"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/flux-pipeline/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-pipeline"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/flux-pipeline.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 | $0.00053 | $0.00795 |
| Opus 5.5 | $0.00021 | $0.00318 |
| Sonnet 5.5 | $0.00011 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
flux-pipeline 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build a Data Pipeline
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 project's data stack:
- Check for pipeline tools:
dags/(Airflow),dagster_home/,prefect.yaml,dbt_project.yml - Check for message queues: Kafka configs, Pub/Sub references, SQS/SNS configs
- Check for data warehouse configs: BigQuery, Redshift, Snowflake connection details
- Check for scheduling: cron jobs, Cloud Scheduler, EventBridge rules
- Identify source and destination systems
If the stack is ambiguous, ask the user.
Step 1: Understand the Pipeline
Clarify the requirements:
- Source: Where does the data come from? (API, database, file, stream)
- Destination: Where does it need to go? (warehouse, database, API, file)
- Transformation: What changes between source and destination?
- Schedule: How often? Real-time, hourly, daily, on-demand?
- Volume: How much data per run? Growth expectations?
Step 2: Build the Pipeline
Build with these principles:
- Idempotent — safe to re-run without duplicating data (use upserts, deduplication keys, or truncate-and-reload)
- Incremental — process only new/changed data where possible (use watermarks, CDC, or last-modified timestamps)
- Error handling — catch, log, and decide: retry, skip, or halt (dead letter queues for bad records)
- Backfill-friendly — support running for historical date ranges
- Observable — emit metrics: rows processed, duration, errors, data freshness
Structure the code as:
- Extract — pull data from source with pagination, rate limiting, retries
- Transform — clean, validate, reshape (keep transformations pure and testable)
- Load — write to destination with conflict handling
Step 3: Add Scheduling and Monitoring
- Configure the schedule using the project's tool (Airflow DAG, cron, Cloud Scheduler, etc.)
- Add monitoring hooks: alerting on failure, SLA tracking, data freshness checks
- Include a health check endpoint or status query
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 · 89 lines · 53 tokens per session scan A 8931456b2a45
flux-pipeline is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 53 tokens to every session and 795 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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