Borrowing it
Nothing to install: this file belongs to ensingm2/AI-threat-modeling-rulesets. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ensingm2/AI-threat-modeling-rulesets/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/ensingm2/AI-threat-modeling-rulesetsWrote 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/instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions)<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/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/instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/ensingm2/ai-threat-modeling-rulesets/copilot-instructions.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 | $0.00504 | $0.00504 |
| Opus 5.5 | $0.00202 | $0.00202 |
| Sonnet 5.5 | $0.00101 | $0.00101 |
| Haiku 4.5 | $0.00050 | $0.00050 |
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 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 — 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
- Read:
.ai-instructions/core/entry-point.md - Read:
.ai-instructions/skills/workflow-guide.md - ⚠️ 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)
- Load:
.ai-instructions/modes/[selected-mode].md - 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.md ← 01-system-understanding.md → 02-data-flow-analysis.md → 03-threat-identification.md → 04-risk-assessment.md → 05-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
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 · 59 lines · 504 tokens per session scan A 1441f1fe3517
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 7mo ago), licensed MIT. It adds 504 tokens to every session, about $0.0020 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 2026-08-30.
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