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.
npx skills add Raftersecurity/rafter-cli --skill rafter-secure-designgit clone --depth 1 https://github.com/Raftersecurity/rafter-cliWrote 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/skills/raftersecurity/rafter-cli/rafter-secure-design)<a href="https://agentmods.dev/skills/raftersecurity/rafter-cli/rafter-secure-design"><img src="https://agentmods.dev/badge/skills/raftersecurity/rafter-cli/rafter-secure-design/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/skills/raftersecurity/rafter-cli/rafter-secure-design"><img src="https://agentmods.dev/badge/skills/raftersecurity/rafter-cli/rafter-secure-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00133 | $0.01680 |
| Opus 5.5 | $0.00053 | $0.00672 |
| Sonnet 5.5 | $0.00027 | $0.00336 |
| Haiku 4.5 | $0.00013 | $0.00168 |
Grade A, and why
rafter-secure-design 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rafter Secure Design — Designing It Right The First Time
A designer's skill, not a scanner. The goal is to catch the flaw in the whiteboard sketch, not three weeks later in a PR. Each sub-doc asks the questions a security engineer would ask at kickoff — "which primitive, which boundary, which default?"
Pair with
rafter-code-review(structured review during PR) and therafterskill (automated detection of what slipped through). This skill is the earliest stage — prevention before the code exists.
When this applies (and when it doesn't)
Engage this design walk when the feature you're about to build touches a real security surface: auth or access control, credentials / secrets / tokens / sessions, user or untrusted input, SQL or query construction, shell / exec, file paths or uploads, (de)serialization, crypto, network-facing endpoints or outbound fetchers, data deletion, or new dependencies.
If the thing you're designing has none of that — a research / experimental / local-only / throwaway piece such as a training script, analysis pipeline, plot, model-eval harness, notebook, or pure computation over trusted local data — a quick surface check is enough and you can proceed to implementation without the full walk. Judge by the surface, not by whether the work is called "research": a research feature that stores user data, handles a token, or opens a network endpoint is back on the engage list.
How to use this skill
- Identify what's being designed (below). If multiple apply, walk them in the order listed —
threat-modelinglast, as a capstone. Readonly the matching sub-doc. Do not preload them all; pick-and-load keeps the conversation tight.- Work through its questions against the proposed design. Capture the answer inline (architecture doc, design RFC, PR description). If you can't answer a question, that's a design gap — resolve it before writing code.
- When the design is stable, run the
threat-modelingwalk to stress-test it. - Hand off to
rafter-code-reviewduring implementation.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 110 lines · 133 tokens per session scan A f039d753451a
rafter-secure-design is a skill published in the GitHub repository Raftersecurity/rafter-cli (27 stars, last pushed yesterday), licensed MIT. It adds 133 tokens to every session and 1,680 once invoked, about $0.0005 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 2026-09-04.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…