AI-threat-modeling-rulesets copilot-instructions.md

AI-threat-modeling-rulesets copilot-instructions.md is an instructions file for GitHub Copilot from ensingm2/AI-threat-modeling-rulesets. It costs 504 tokens per session, scanned A, original, MIT.

GitHub Copilot instructions for threat modeling, a structured way to identify security risks in a system. They define a six-stage process, required resources, operating modes, and output locations.

In plain words
What is it for?
Use them to guide a Copilot threat-modeling exercise from initial setup through its six stages. The resulting files go under the target's output/threat-model directory.
Why use it?
They make security analysis follow a defined process and require sources and confidence levels for claims. They also prevent the work from starting before the user chooses the operating and review modes.

Instructions file for GitHub Copilot

Install

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.

agentmods
npx agentmods add instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/ensingm2/AI-threat-modeling-rulesets

Made for: GitHub Copilot.

Wrote 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.

agentmods badge for AI-threat-modeling-rulesets copilot-instructions.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions.svg)](https://agentmods.dev/instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions)
Your own site
<a href="https://agentmods.dev/instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions.svg" alt="Measured on agentmods" height="20"></a>
Per session 504 This file is loaded in full into every session.
When invoked 504 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00504 $0.00504
Opus 5 $0.00252 $0.00252
Sonnet 5 $0.00101 $0.00101
Haiku 4.5 $0.00050 $0.00050

Measured 5d ago against content hash 1441f1fe3517, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

AI-threat-modeling-rulesets copilot-instructions.md 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 5d 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.

.github/copilot-instructions.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GitHub Copilot - Threat Modeling Framework

Project: LLM Instruction Sets for Threat Modeling


Quick Start

  1. Read: .ai-instructions/core/entry-point.md
  2. Read: .ai-instructions/skills/workflow-guide.md
  3. ⚠️ MANDATORY - Ask user BOTH questions in single prompt:
    • Operational Mode: Collaborative or Automatic?
    • Critic Review Mode: With Critic or Without Critic? (recommend: Without for single-agent efficiency)
  4. Load: .ai-instructions/modes/[selected-mode].md
  5. Confirm BOTH selections → Begin Stage 1

CRITICAL: Steps 3-5 are NON-NEGOTIABLE. Never start Stage 1 without asking BOTH questions.


⚠️ CRITICAL RULES (Details: .ai-instructions/skills/shared/critical-rules.md)

Rule Summary
Mode First Ask user for mode BEFORE Stage 1 - never assume
Critic Selection Ask user about Critic Review mode at startup - default OFF for single-agent
Never Fabricate All claims need sources; use confidence levels
Batched Execution One phase per response (Work OR Critic, not both)

Key Resources (All paths from .ai-instructions/skills/)

Resource File
Workflow workflow-guide.md
Critical Rules shared/critical-rules.md
Terminology shared/terminology.md
Confidence Levels shared/confidence-calibration.md
Output Specs shared/output-file-requirements.md

6-Stage Outputs → [target]/output/threat-model/

00-final-report.md01-system-understanding.md02-data-flow-analysis.md03-threat-identification.md04-risk-assessment.md05-mitigation-strategy.md


Copilot Notes

  • Read instruction files at stage start (limited dynamic loading)
  • Use conversation context to track stage progress
  • Reference previous outputs for cross-stage consistency

Full documentation: .ai-instructions/README.md and .ai-instructions/core/entry-point.md

Read the full file on GitHub · 59 lines

Changes

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.

  1. 5d ago First seen · 59 lines · 504 tokens per session scan A 1441f1fe3517

Subscribe to this mod's changes

AI-threat-modeling-rulesets copilot-instructions.md is an instructions file published in the GitHub repository ensingm2/AI-threat-modeling-rulesets (12 stars, last pushed 6mo ago), licensed MIT. It adds 504 tokens to every session, about $0.0025 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.

Related

Other instructions, from other repositories

next.js AGENTS.md

AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,469 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens