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/security)<a href="https://agentmods.dev/commands/smart-ai-memory/attune-ai/security"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/attune-ai/security/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/security"><img src="https://agentmods.dev/badge/commands/smart-ai-memory/attune-ai/security.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 | $0.00109 |
| Opus 5.5 | $0.00008 | $0.00044 |
| Sonnet 5.5 | $0.00004 | $0.00022 |
| Haiku 4.5 | $0.00002 | $0.00011 |
Grade A, and why
security 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
Run a security audit on $ARGUMENTS.
If no path was provided, ask the user what to scan.
Use uv run attune workflow run security-audit --path <target>
to execute. Scope the audit with AskUserQuestion first:
target path, focus areas (OWASP top 10, path traversal,
secrets, or all).
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 · 15 lines · 19 tokens per session scan A 60fcddc4fedb
security 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 19 tokens to every session and 109 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
pm-competitive
Competitive teardown — for a given competitor or for the whole landscape around a JTBD. Spawns market-analyst. Outputs structured brief with positioning gaps.
pm-ownership
Show who owns what across initiatives, gates, drafts, and Beads issues. Surfaces orphaned artefacts (no owner) and over-loaded owners.
pm-pricing
Author or refresh a pricing plan — 2–3 monetization models with willingness-to-pay rationale per audience. Spawns pricing-strategist.
pm-save
Extract lessons from this session and save them to .great-pm/lessons.md. Optional cross-project promotion to /.great-pm/decisions.md for patterns seen 3+ times (proposed, never auto-promoted).
pm-burn
Cost burn-rate — LLM cost over the last N days vs a human-equivalent baseline (savingsx). Flags cost outliers and abnormal-burn agents.
agentos-status
Show AgentOS system status — agents, workers, health.