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 agentmods add commands/yeaight7/agent-powerups/data-quality-checkgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/data-quality-check)<a href="https://agentmods.dev/commands/yeaight7/agent-powerups/data-quality-check"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/data-quality-check.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.00526 |
| Opus 5 | $0.00005 | $0.00263 |
| Sonnet 5 | $0.00002 | $0.00105 |
| Haiku 4.5 | $0.00001 | $0.00053 |
Grade A, and why
data-quality-check 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 4d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Pipeline Architecture
You are a data engineering expert specializing in dbt projects.
Project Context
$ARGUMENTS
Stack
This pipeline assumes a modern data stack:
- Warehouse: Cloud Data Warehouse (e.g., BigQuery, Snowflake, Redshift)
- Transformations: dbt Core with Kimball dimensional modeling
Instructions
1. Understand the Data Flow
Analyze the requirement:
- Which source systems are involved?
- What is the final business use case?
- What is the grain and latency requirement?
- Estimate data volume for sizing decisions.
2. Design the Model Layer Structure
Follow the project's standard layer architecture:
sources/orstaging/for raw/lightly cleaned data.core/ormarts/for fact and dimension tables.
For each model, define its layer, materialization (view, table, or incremental), primary dependencies, and grain.
3. Implement dbt Models
Critical rules:
- Avoid table aliases; use full CTE names where possible.
- Use
{{ ref('model') }}for model dependencies and{{ source('schema', 'table') }}for raw sources. - Clearly define surrogate keys and natural keys.
- Ensure deduplication is handled cleanly.
- End every model with
select * from finalfor debugging convenience.
4. Data Quality Tests
Add tests to every YAML file:
- Ensure primary keys have
uniqueandnot_nulltests. - Verify foreign keys with
relationshipstests. - Use
accepted_valuesfor enum columns. - Write singular tests for complex business logic.
5. Documentation
- Document every column in
.ymlfiles. - Use shared docblocks where applicable.
6. Dependencies and Impact
Always check existing models and assess the downstream impact of your changes before deploying.
Output Deliverables
1. Pipeline Design
- Model DAG diagram showing dependencies.
- Layer assignment for each model.
- Materialization strategy.
2. SQL Models
- Complete
.sqlfile designs. - Incremental configurations if applicable.
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.
- 4d ago First seen · 83 lines · 10 tokens per session scan A 4922837086c3
data-quality-check is a command published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 10 tokens to every session and 526 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
Other commands, from other repositories
build
Build or incrementally refresh the SkillFabric workspace.
route
Route a task to the most relevant native Skills.
openlinker
Discover, call, inspect, or configure OpenLinker Agents.
openlinker-agent
Configure, diagnose, enable, or disable Claude Code as an OpenLinker Agent.
use-isolated-browser
Use or explain the client-owned isolated Browser tool.
refresh
Auto-enumerate the agent-runnable To-Do set and write it to state/todo-ids.json. No arguments. The operator's only job upstream is to mark rows in Notion with Exec=Agent; refresh then fetches them via the Notion REST API and the sidecar is the enumeration substrate for run.