architecture-decision-records

A set of rules for recording important software design choices in Architecture Decision Records (ADRs), including decisions about AI systems.

In plain words
What is it for?
It helps document choices such as which agent framework to use, how triggers are classified, and how changes to AI behaviour should be assessed.
Why use it?
It prevents significant decisions from being made without a written reason and ensures changes affecting people are assessed for their impact.

Cursor rule for Cursor

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 rules/onesimplecode/agent-engineering-standards/architecture-decision-records
Clone the repo
git clone --depth 1 https://github.com/onesimplecode/agent-engineering-standards

Made for: Cursor.

Per session 20 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 131 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00020 $0.00131
Opus 5 $0.00010 $0.00066
Sonnet 5 $0.00004 $0.00026
Haiku 4.5 $0.00002 $0.00013

Measured 2d ago against content hash 23240110cafd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

architecture-decision-records 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 2d 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.

examples/cursor-rules/.cursor/rules/architecture-decision-records.mdc · 17 lines

What it actually says

Architecture Decision Records

TR-ADR-001 — ADR required for significant design decisions

An ADR must be created for each significant design decision including agent framework choice and trigger classification (TR-AGT-004).

TR-GOV-005 — AI system impact assessment

ADRs that change AI behavior toward people require templates/ai-impact-assessment.md.

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. 2d ago First seen · 17 lines · 20 tokens per session scan A 23240110cafd

Subscribe to this mod's changes

architecture-decision-records is a cursor rule published in the GitHub repository onesimplecode/agent-engineering-standards (3 stars, last pushed 5d ago), licensed MIT. It adds 20 tokens to every session and 131 once invoked, about $0.0001 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.