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/armanzeroeight/fastagent-pluginsWrote 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/armanzeroeight/fastagent-plugins/validate-pipeline)<a href="https://agentmods.dev/commands/armanzeroeight/fastagent-plugins/validate-pipeline"><img src="https://agentmods.dev/badge/commands/armanzeroeight/fastagent-plugins/validate-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/commands/armanzeroeight/fastagent-plugins/validate-pipeline"><img src="https://agentmods.dev/badge/commands/armanzeroeight/fastagent-plugins/validate-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 | $0.00008 | $0.00191 |
| Opus 5.5 | $0.00003 | $0.00076 |
| Sonnet 5.5 | $0.00002 | $0.00038 |
| Haiku 4.5 | $0.00001 | $0.00019 |
Grade A, and why
validate-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 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.
What it actually says
Validate Pipeline
Validate data pipeline configuration and suggest improvements.
Your Task
Review pipeline configuration and data quality checks.
Arguments
$1: Pipeline configuration file (e.g., dags/etl_pipeline.py)
Steps
- Read pipeline configuration
- Check for:
- Error handling
- Retry logic
- Monitoring
- Data quality checks
- Idempotency
- Provide recommendations:
- Add missing error handling
- Implement data quality checks
- Add monitoring and alerts
- Ensure idempotent operations
Examples
/validate-pipeline dags/etl_pipeline.py
Notes
- Focus on reliability and data quality
- Suggest specific improvements
- Check for best practices
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 First seen · 45 lines · 8 tokens per session scan A db62242d9f82
validate-pipeline is a command published in the GitHub repository armanzeroeight/fastagent-plugins (29 stars, last pushed 2mo ago), licensed MIT. It adds 8 tokens to every session and 191 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
evaluation-report
Generate a comprehensive evaluation report for a trained model, highlighting performance, baselines, and subgroup metrics.
experiment-preflight
Audit an ML training script or pipeline for reproducibility, tracking, and basic hygiene before a long run.
leakage-check
Audit data preprocessing and splitting logic for target leakage.
ml-run-review
Review a machine learning training run or experiment configuration for soundness, reproducibility, and evaluation rigor.
prompt-optimize
Legacy slash-entry shim for the prompt-optimizer skill. Prefer the skill directly.
flutter-build
Fix Dart analyzer errors and Flutter build failures incrementally. Invokes the dart-build-resolver agent for minimal, surgical fixes.