AI Feature Lifecycle

A rule for managing the lifecycle of AI features, from development through evaluation. Its description points to a companion skill that contains the detailed process.

In plain words
What is it for?
Applying the project’s AI-feature lifecycle gate when the companion skill and its detailed instructions are available.
Why use it?
It acts as a required checkpoint for AI features and addresses the gap between building several agents and evaluating them. The available description does not specify the individual checks.

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/databrickslabs/ontobricks/12-ai-feature-lifecycle
Clone the repo
git clone --depth 1 https://github.com/databrickslabs/ontobricks

Made for: Cursor.

Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 753 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown 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 $0.00000 $0.00753
Opus 5 $0.00000 $0.00377
Sonnet 5 $0.00000 $0.00151
Haiku 4.5 $0.00000 $0.00075

Measured 3d ago against content hash b22b2325127c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

AI Feature Lifecycle 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 3d 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.

.cursor/12-ai-feature-lifecycle.mdc · 54 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

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. 3d ago First seen · 54 lines · 0 tokens per session scan A b22b2325127c

Subscribe to this mod's changes

AI Feature Lifecycle is a cursor rule published in the GitHub repository databrickslabs/ontobricks (298 stars, last pushed 4d ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 753 tokens. 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.