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 RedHatProductSecurity/agentic-threat-modeling --skill threat-model-discovergit clone --depth 1 https://github.com/RedHatProductSecurity/agentic-threat-modelingWrote 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/redhatproductsecurity/agentic-threat-modeling/threat-model-discover)<a href="https://agentmods.dev/skills/redhatproductsecurity/agentic-threat-modeling/threat-model-discover"><img src="https://agentmods.dev/badge/skills/redhatproductsecurity/agentic-threat-modeling/threat-model-discover/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/redhatproductsecurity/agentic-threat-modeling/threat-model-discover"><img src="https://agentmods.dev/badge/skills/redhatproductsecurity/agentic-threat-modeling/threat-model-discover.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 | $0.00042 | $0.02870 |
| Opus 5 | $0.00021 | $0.01435 |
| Sonnet 5 | $0.00008 | $0.00574 |
| Haiku 4.5 | $0.00004 | $0.00287 |
Grade A, and why
threat-model-discover 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.
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Context Discovery
You are performing the discovery phase of threat modeling. Your job is to build a complete picture of the system being modeled — its components, how data flows, where trust boundaries exist, what assets matter, and where attackers can enter.
You operate in two modes. The dispatcher skill will tell you which to use.
Step 0: External Context Ingestion
If context: arguments were provided, split on commas and ingest each source. In guided or full mode, ask if there's anything else:
"Do you have any other external documentation I should ingest? Architecture docs, prior threat models, API specs, deployment diagrams, SBOMs, or SAR reports?"
If no context: arguments were provided and the mode is guided or full, ask the same question without "other." In quick mode, skip prompting entirely — just ingest any context: arguments and move on.
Supported external sources:
| Source Type | What's Extracted | How to Provide |
|---|---|---|
| Google Docs / Confluence pages | Architecture descriptions, component relationships, data flows | Public URL, or paste/export content. Authenticated pages (behind SSO, Google login) require exporting first. |
| Mermaid / PlantUML diagrams | Component topology, data flows, trust boundaries | Paste content or provide file path |
| Prior threat models (THREAT_MODEL.md) | Previously identified threats, accepted risks, implemented mitigations | File path or URL |
| SAR reports (Security Assessment Reports) | Previous findings, compliance status, remediation state | File path or URL |
| OpenAPI / Swagger specs (YAML/JSON) | Endpoints, auth schemes, data models, request/response shapes | File path in repo or external |
| SBOM (CycloneDX or SPDX) | Dependency inventory, known vulnerabilities, license risks | File path |
| Product definition JSON | Service catalog, ownership, dependencies | File path |
| Deployment topology YAML | Network segments, trust zones, cloud services, regions | File path |
| Terraform / CloudFormation templates | Infrastructure components, network rules, IAM policies, storage configs | File path in repo or external |
| Kubernetes manifests / Helm values | Pod security, network policies, RBAC, secrets management | File path in repo or external |
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.
- 10d ago First seen · 252 lines · 42 tokens per session scan A 307f99dd5b73
threat-model-discover is a skill published in the GitHub repository RedHatProductSecurity/agentic-threat-modeling (4 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 42 tokens to every session and 2,870 once invoked, about $0.0002 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.
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