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/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/dbt-model-review)<a href="https://agentmods.dev/commands/yeaight7/agent-powerups/dbt-model-review"><img src="https://agentmods.dev/badge/commands/yeaight7/agent-powerups/dbt-model-review.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 5.1 | $0.00016 | $0.00838 |
| Opus 5 | $0.00008 | $0.00419 |
| Sonnet 5 | $0.00003 | $0.00168 |
| Haiku 4.5 | $0.00002 | $0.00084 |
Grade A, and why
dbt-model-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Feature Development
CRITICAL BEHAVIORAL RULES
- Execute steps in order. Do NOT skip ahead or merge steps.
- Write output files. Each step produces its output in
.analytics-feature/before the next step begins. Read from prior files — do NOT rely on context window memory. - Stop at checkpoints. When reaching a
PHASE CHECKPOINT, stop and wait for explicit user approval. - Halt on failure. If any step fails, stop immediately and ask how to proceed.
- Never enter plan mode autonomously. This command IS the plan — execute it.
Pre-flight Checks
1. Check for existing session
Check if .analytics-feature/state.json exists:
- If
statusis"in_progress": Display current step and ask to resume or start fresh. - If
statusis"complete": Ask whether to archive and start fresh.
2. Initialize state
Create .analytics-feature/ and state.json:
{
"feature": "$ARGUMENTS",
"status": "in_progress",
"current_step": 1,
"completed_steps": []
}
Phase 1: Business Analysis & Data Discovery (Steps 1–2)
Step 1: Business Requirements Analysis
Use the Task tool to analyze the business requirements for: $FEATURE.
Identify the domain, business question, key metrics, dimensions, grain, required source systems, and potential data quality concerns.
Save to .analytics-feature/01-business-requirements.md. Update state.json.
Step 2: Data Source Exploration
Explore the data sources using the MCP dbt tools or by analyzing the project structure.
Identify which source tables or existing dbt models contain the required data.
Save to .analytics-feature/02-data-exploration.md. Update state.json.
PHASE CHECKPOINT 1 — User Approval Required
Present findings from Phase 1 and ask the user for approval to proceed to model design. Do NOT proceed until approved.
Phase 2: Dimensional Model Design (Steps 3–4)
Step 3: Model Architecture Design
Design the dimensional model architecture. Define whether it's a fact, dimension, or mart. Define the grain, column types, dependencies, CTE structure, and DAG plan.
Save to .analytics-feature/03-model-design.md. Update state.json.
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.
- 7d ago First seen · 108 lines · 16 tokens per session scan A 893204e880fc
dbt-model-review is a command published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 838 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
audit-accessibility
Audit web page or component for WCAG accessibility compliance.
t800-update
A command for updating the T-800 agent system from its GitHub repository. GitHub is a service for storing and versioning code.
validate-pipeline
Validate data pipeline configuration and data quality rules.
t800-bootstrap
A first-run setup command for the T-800 plugin, intended for installation or a new user.
advisor
Get a second opinion from Codex (GPT-5) on your current plan, diff, or a specific question.
aidd-churn
Rank files by hotspot score to identify prime candidates for refactoring before PR review.