Borrowing it
Nothing to install: this file belongs to 1Password/SCAM. 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/1Password/SCAM/main/AGENTS.mdgit clone --depth 1 https://github.com/1Password/SCAMWrote 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/1password/scam/agents-md)<a href="https://agentmods.dev/instructions/1password/scam/agents-md"><img src="https://agentmods.dev/badge/instructions/1password/scam/agents-md/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/1password/scam/agents-md"><img src="https://agentmods.dev/badge/instructions/1password/scam/agents-md.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 | $0.03027 | $0.03027 |
| Opus 5.5 | $0.01211 | $0.01211 |
| Sonnet 5.5 | $0.00605 | $0.00605 |
| Haiku 4.5 | $0.00303 | $0.00303 |
Grade A, and why
SCAM AGENTS.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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — AI Coding Guidelines for SCAM
Project Overview
SCAM (Security Comprehension Awareness Measure) is a CLI benchmark that evaluates the safety of AI agents with tool access. It runs multi-turn conversations where an AI agent uses tools (inbox, browser, forms, credential vault) and must proactively protect the user from threats without being told to look for them.
Architecture
CLI (typer) → Agentic Runner → Model.chat() (tool-calling loop) → ToolRouter (simulated env)
↓ ↓
Checkpoint evaluator Environment (emails, URLs, vault)
↓
Reporting (rich + markdown)
↓
Export (HTML, video, terminal replay)
Module Responsibilities
| Module | Purpose |
|---|---|
scam/cli.py |
CLI commands: run, evaluate, replay, export, compare, report, scenarios |
scam/models/base.py |
BaseModel (abstract: chat() required), ChatResponse, ToolCall |
scam/models/__init__.py |
create_model() factory function, re-exports |
scam/models/anthropic.py |
Anthropic Claude adapter (Messages API + tool calling) |
scam/models/openai.py |
OpenAI adapter (Chat Completions API + tool calling) |
scam/models/gemini.py |
Google Gemini adapter (google-genai SDK + function calling) |
scam/models/discovery.py |
Dynamic model listing from provider APIs, interactive picker |
scam/agentic/scenario.py |
YAML parser, AgenticScenario dataclass, STANDARD_TOOLS |
scam/agentic/environment.py |
ToolRouter — simulates inbox, URLs, forms, vault; tracks dangerous calls |
scam/agentic/evaluator.py |
Checkpoint-based scoring for agentic scenarios (regex + optional LLM judge) |
scam/agentic/judge.py |
LLM-as-judge evaluator — semantic fallback when regex pattern matching misses |
scam/agentic/runner.py |
Multi-turn conversation orchestrator for agentic evaluation |
scam/agentic/reporting.py |
Terminal reports, comparison, markdown reports (agentic) |
scam/agentic/aggregate.py |
Multi-run statistical aggregation (mean, std, CI, stability) |
scam/agentic/replay.py |
Interactive terminal replay viewer with typing effects and scorecard |
scam/agentic/export_html.py |
Self-contained HTML export with animated replay, per-scenario and combined index pages |
scam/agentic/export_video.py |
MP4 video export using Pillow for frame rendering and FFmpeg for encoding. Includes title cards, animated message bubbles with markdown rendering, tool call visualization, and scorecard overlays |
scam/utils/config.py |
Paths, model pricing, API keys, skill_hash(), agentic_scenario_hash(), estimate_agentic_cost(), calculate_cost() |
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 · 270 lines · 3,027 tokens per session scan A f4759343acd4
SCAM AGENTS.md is an instructions file published in the GitHub repository 1Password/SCAM (136 stars, last pushed 8mo ago), licensed MIT. It adds 3,027 tokens to every session, about $0.0121 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.
Other instructions, from other repositories
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).
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.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
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.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deno copilot-instructions.md
Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.