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 Robotti-io/copilot-security-instructions --skill threat-modelgit clone --depth 1 https://github.com/Robotti-io/copilot-security-instructionsWrote 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/robotti-io/copilot-security-instructions/threat-model)<a href="https://agentmods.dev/skills/robotti-io/copilot-security-instructions/threat-model"><img src="https://agentmods.dev/badge/skills/robotti-io/copilot-security-instructions/threat-model.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.01544 |
| Opus 5 | $0.00019 | $0.00772 |
| Sonnet 5 | $0.00008 | $0.00309 |
| Haiku 4.5 | $0.00004 | $0.00154 |
Grade A, and why
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 8d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Model
Purpose
Provide a repeatable, evidence-first threat modeling workflow for GitHub Copilot users who need durable Markdown output and Mermaid diagrams, including a fallback path for GitHub Copilot CLI users who cannot call the VS Code Mermaid Chart tools directly.
When to use
Use this skill when you need to:
- threat model a repository, feature, architecture, or PR diff
- prepare a security architecture review with data flows and trust boundaries
- produce a 4Q report with actionable mitigations and a validation plan
- work from GitHub Copilot CLI and still validate Mermaid diagrams before publishing the report
Inputs to collect
- in-scope components, deployables, and entry points
- deployment and reachability assumptions
- privileged roles and high-impact workflows
- sensitive data categories and likely consequence of misuse
- existing controls, especially authn/authz, ingress, logging, and environment isolation
- repository evidence for code paths, IaC, manifests, and configuration
How to use
-
Collect repository evidence before relying on operator answers.
-
Ask only the branching intake questions that materially change exposure, privilege, or data-sensitivity scoring.
-
Draft the report in a root-level file named
Threat Model Review - YYYY-MM-DD.md. -
Use the bundled Mermaid helper scripts when the Mermaid Chart extension tools are unavailable:
npm run threat-model:mermaid-docs -- --list npm run threat-model:mermaid-docs -- --type flowchart npm run threat-model:mermaid-docs -- --type sequenceDiagram npm run threat-model:mermaid-validate -- --file "Threat Model Review - 2026-04-15.md" -
Fix Mermaid failures and rerun validation until the script exits successfully.
-
Deliver the final report plus a short PR-ready summary.
Rules
- MUST use this evidence hierarchy for factual claims: repo-confirmed, runtime/deployment evidence, operator-stated, ASSUMPTION, UNKNOWN.
- MUST keep confirmed facts separate from inference.
- MUST ask 4-8 concise intake questions when reachability, privileged workflows, data sensitivity, or environment isolation are unclear.
- MUST produce at least these diagrams unless the repository clearly cannot support them: DFD Level 0, DFD Level 1, trust-boundary view, and top 2-3 sequence diagrams.
- MUST validate every Mermaid block before finalizing the report.
- MUST include at least 3 code-anchored or IaC-anchored findings that do not depend primarily on operator answers.
- MUST assign an overall application risk score from 0-100 with confidence, volatility, and top score drivers.
- MUST mark mitigations as PRESENT, ABSENT, or UNKNOWN.
- MUST mark threats as Mitigated, Partially Mitigated, Open, or Unknown based on whether controls materially close the exploit path.
- SHOULD prefer simple Mermaid syntax over advanced styling.
- SHOULD call out contradictions between repo evidence and operator statements before finalizing prioritization.
- MAY omit optional diagrams when the repository does not expose the needed evidence; label the gap as UNKNOWN.
What ships with it
3 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.
- 8d ago First seen · 161 lines · 39 tokens per session scan A 806ab27bdcce
threat-model is a skill published in the GitHub repository Robotti-io/copilot-security-instructions (42 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 1,544 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 2026-08-30.
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