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/refactor)<a href="https://agentmods.dev/commands/smart-ai-memory/attune-ai/refactor"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/attune-ai/refactor/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/refactor"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/attune-ai/refactor.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 | $0.00012 | $0.00092 |
| Opus 5.5 | $0.00005 | $0.00037 |
| Sonnet 5.5 | $0.00002 | $0.00018 |
| Haiku 4.5 | $0.00001 | $0.00009 |
Grade A, and why
refactor 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
Analyze refactoring opportunities in $ARGUMENTS.
If no path was provided, ask the user what to refactor.
Use uv run attune workflow run refactor-plan --path <target>
to execute. Scope with AskUserQuestion first: target path,
focus (complexity reduction, extraction, tech debt).
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 · 14 lines · 12 tokens per session scan A caf87474f815
refactor is a command published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed 5d ago), licensed Apache-2.0. It adds 12 tokens to every session and 92 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 2026-09-03.
Other commands, from other repositories
doctor
Diagnose what is busting Claude Code's DeepSeek prefix cache.
distil-doctor
Diagnose your distil setup — ledger, shadow validation, proxy round-trip, wiring.
doctor
Diagnose a broken llm-router setup.
fire-resurrect
Autonomous Resurrection Mode — reverse-engineer intent from messy code, then rebuild clean from scratch.
analyze
Analyze Claude Code transcripts to surface subagent behavior and improvement opportunities.
fire-reflect
After any failure (debug resolution, test failure, approach rotation, stalled loop), capture what was tried, why it failed, and what actually worked as a persistent reflection. Future sessions search these before investigating.