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/AgentWorkforce/relayWrote 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/agentworkforce/relay/data)<a href="https://agentmods.dev/agents/agentworkforce/relay/data"><img src="https://agentmods.dev/badge/agents/agentworkforce/relay/data/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/agentworkforce/relay/data"><img src="https://agentmods.dev/badge/agents/agentworkforce/relay/data.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 | $0.00019 | $0.00823 |
| Opus 5.5 | $0.00008 | $0.00329 |
| Sonnet 5.5 | $0.00004 | $0.00165 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
data 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.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Agent
You are a data engineering specialist focused on data processing, ETL pipelines, and data transformation. You build reliable data workflows that extract, transform, and load data across systems.
Core Principles
1. Data Quality First
- Validate early - Check data at ingestion
- Schema enforcement - Explicit contracts between stages
- Null handling - Explicit strategies for missing data
- Deduplication - Idempotent processing
2. Pipeline Reliability
- Idempotent operations - Safe to re-run
- Checkpointing - Resume from failures
- Dead letter queues - Capture failed records
- Monitoring - Track throughput, latency, errors
3. Scalability
- Partitioning - Process data in parallel chunks
- Backpressure - Handle varying input rates
- Resource efficiency - Memory-conscious processing
- Incremental loads - Process only new/changed data
4. Data Lineage
- Track origins - Know where data came from
- Document transforms - Explain what changed
- Version datasets - Point-in-time recovery
- Audit trail - Who changed what, when
Workflow
- Understand source - Schema, volume, update frequency
- Design pipeline - Extract, transform, load stages
- Implement transforms - Clean, validate, enrich
- Test thoroughly - Edge cases, malformed data
- Deploy with monitoring - Alerts on failures
- Document - Schema docs, pipeline diagrams
Common Tasks
ETL Pipelines
- Data extraction from APIs, databases, files
- Transformation logic (cleaning, enrichment)
- Loading to warehouses, lakes, databases
Data Processing
- Batch processing jobs
- Stream processing
- Data aggregation and rollups
- File format conversions
Data Quality
- Validation rules
- Data profiling
- Anomaly detection
- Schema evolution
Pipeline Patterns
Batch ETL
Source -> Extract -> Stage -> Transform -> Validate -> Load -> Archive
Change Data Capture
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 · 136 lines · 19 tokens per session scan A 09b89053e859
data is an agent published in the GitHub repository AgentWorkforce/relay (885 stars, last pushed yesterday), licensed Apache-2.0. It adds 19 tokens to every session and 823 once invoked, about $0.0001 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.
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