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 tobihagemann/turbo --skill create-threat-modelgit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/create-threat-model)<a href="https://agentmods.dev/skills/tobihagemann/turbo/create-threat-model"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/create-threat-model/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/tobihagemann/turbo/create-threat-model"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/create-threat-model.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 | $0.00090 | $0.02125 |
| Opus 5 | $0.00045 | $0.01063 |
| Sonnet 5 | $0.00018 | $0.00425 |
| Haiku 4.5 | $0.00009 | $0.00213 |
Grade A, and why
create-threat-model 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 11d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Threat Model
Analyze the current codebase and produce a structured threat model at .turbo/threat-model.md.
The threat model describes the current state of the codebase: what it protects, where trust boundaries are, how it can be attacked, what defenses exist, and how severe each risk is. It is descriptive, not prescriptive. Do not include remediation recommendations.
Optional: $ARGUMENTS may specify scope (directories, modules, or focus areas). When scope is provided, limit reconnaissance and code discovery to the specified directories or modules. Still produce all four sections, but title the overview to reflect the narrowed scope and note what is excluded.
Step 1: Reconnaissance
Build a mental model of the system before analyzing threats.
- Read the project README, CLAUDE.md, and any architecture or security documentation.
- Examine top-level directory structure, build files, and dependency manifests to identify modules, languages, frameworks, and deployment model.
- Classify the application type: library, CLI tool, web service, desktop app, mobile app, or hybrid. This determines which threat categories and trust boundary patterns apply.
- Identify security-critical dependencies (crypto libraries, auth providers, network stacks, native/FFI libraries). Note what this codebase delegates versus what it owns.
- Read any existing security documentation:
SECURITY.md, audit reports, threat models, or changelog entries mentioning CVEs.
Step 2: Security-Relevant Code Discovery
Search the codebase for code that handles security-sensitive operations. Do not read every file. Use targeted searches.
Categories to search for:
- Authentication and authorization (login, OAuth, tokens, sessions, RBAC, API keys)
- Cryptographic operations (encryption, signing, hashing, key generation, key derivation)
- Secret and credential storage (keychains, vaults, env vars, config files with secrets)
- Network communication (HTTP clients, TLS configuration, certificate handling, WebSocket, gRPC)
- Untrusted input processing (file parsing, deserialization, XML/JSON/YAML from external sources)
- IPC and process boundaries (sockets, pipes, CLI subprocesses, shared memory)
- Plugin and extension loading (dynamic imports, ServiceLoader, plugin directories)
- Update and distribution mechanisms (auto-update, download verification, signature checking)
- Implicit network behavior (link previews, auto-fetches, thumbnail generation triggered by remote data)
- Native code / FFI boundaries (C interop, JNI, ctypes, unsafe blocks, bridging headers)
What ships with it
1 file 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.
- 11d ago First seen · 135 lines · 90 tokens per session scan A 776796c41aad
create-threat-model is a skill published in the GitHub repository tobihagemann/turbo (402 stars, last pushed yesterday), licensed MIT. It adds 90 tokens to every session and 2,125 once invoked, about $0.0005 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 2026-08-30.
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