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/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/deep-review)<a href="https://agentmods.dev/commands/smart-ai-memory/attune-ai/deep-review"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/attune-ai/deep-review/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/smart-ai-memory/attune-ai/deep-review"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/attune-ai/deep-review.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 | $0.00016 | $0.01249 |
| Opus 5.5 | $0.00006 | $0.00500 |
| Sonnet 5.5 | $0.00003 | $0.00250 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
deep-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 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deep-review
Multi-pass deep code review orchestrating security analysis, code quality checks, and test gap detection into a single synthesized report.
Quick Shortcuts
| Shortcut | Action |
|---|---|
/deep-review <path> |
Full 3-pass review of target |
/deep-review security <path> |
Security-only pass |
/deep-review quality <path> |
Quality-only pass |
/deep-review tests <path> |
Test gap analysis only |
Natural Language
Describe what you need:
- "deep review the auth module"
- "do a full review of src/attune/workflows/"
- "check security and test coverage for config.py"
CRITICAL: Workflow Execution Instructions
When this command is invoked with arguments, you MUST execute the review, not answer ad-hoc.
Context Gathering (ALWAYS DO FIRST)
Before executing any action below, gather current project context:
- Run:
git status --short - Run:
git log --oneline -5 - Run:
git branch --show-current
Use this context to inform review scope (e.g., focus on recently changed files).
Reasoning Approach
For deep reviews, use structured multi-pass reasoning:
Shortcut Routing (EXECUTE THESE)
| Input | Action |
|---|---|
/deep-review <path> |
Run all 3 passes below, then synthesize |
/deep-review security <path> |
Run security pass only |
/deep-review quality <path> |
Run quality pass only |
/deep-review tests <path> |
Run test gap pass only |
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 · 170 lines · 16 tokens per session scan A 393d82a6de97
deep-review is a command published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 1,249 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 2026-09-03.
Other commands, from other repositories
cc-council
Comprehensive multi-agent council review with 6 protocols, 10 specialists, scoped scoring (per-scope thresholds and weights), state machine orchestration, auto-fix, and 50+ configuration flags.
cto-advisor
Run CTO-level technical review of the current execution plan - architecture, tech debt, engineering excellence.
phase-review
Run post-execution quality gate - verifies all deliverables before marking a track complete.
evaluate-execution
Verify implementation quality. Evaluate-Loop Step 4.
agentos-status
Show AgentOS system status — agents, workers, health.
agentos-tui
Launch the AgentOS TUI dashboard.