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/jassics/awesome-claude-securityWrote 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/jassics/awesome-claude-security/safety-review)<a href="https://agentmods.dev/commands/jassics/awesome-claude-security/safety-review"><img src="https://agentmods.dev/badge/commands/jassics/awesome-claude-security/safety-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/jassics/awesome-claude-security/safety-review"><img src="https://agentmods.dev/badge/commands/jassics/awesome-claude-security/safety-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 | $0.00023 | $0.00298 |
| Opus 5 | $0.00012 | $0.00149 |
| Sonnet 5 | $0.00005 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
safety-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 10d 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 10d ago First seen · 17 lines · 23 tokens per session scan A bfbe99c5d5e3
safety-review is a command published in the GitHub repository jassics/awesome-claude-security (6 stars, last pushed 1mo ago), licensed GPL-3.0. It adds 23 tokens to every session and 298 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
prompt
Create or optimize prompts using best practices.
format-converter
Convert between data formats (JSON, CSV, YAML, XML, TOML, Excel). Bidirectional conversion with schema preservation and intelligent type handling.
briefing-claude
Use when Claude keeps building the wrong UI and you are re-rolling prompts. How to write the brief, which references and constraints to give, how to iterate instead of starting over, and how to spot when it is guessing.
index
Index this repository for local RAG search, then report which rung it is on — descriptions still to write, a promotion to apply, or nothing left.
ml-project
Start a professional AI/ML research-engineer workflow for a task (any domain - CV, medical imaging, NLP/LLM, tabular, time-series). Researches papers first, picks the best method, trains/evaluates rigorously and honestly.
ds-package
Package a model as a servable unit and prove prediction parity.