What is feature-lifecycle?

A feature-development process covering research, requirements, design, planning, and task creation.

Use it to research documentation and GitHub issues, record requirements, define user stories and acceptance criteria, and create an implementation plan.

It gives teams a repeatable way to gather evidence and turn an idea into implementable work.

feature-lifecycle — Cursor rule for Cursor by felipebarcelospro

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.

Clone the repo

git clone --depth 1 https://github.com/felipebarcelospro/igniter-js

Made for: Cursor.

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.

README.md

[![agentmods](https://agentmods.dev/badge/rules/felipebarcelospro/igniter-js/feature-lifecycle/github.svg)](https://agentmods.dev/rules/felipebarcelospro/igniter-js/feature-lifecycle)

Your own site

<a href="https://agentmods.dev/rules/felipebarcelospro/igniter-js/feature-lifecycle"><img src="https://agentmods.dev/badge/rules/felipebarcelospro/igniter-js/feature-lifecycle/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.

Your own site · 80×15

<a href="https://agentmods.dev/rules/felipebarcelospro/igniter-js/feature-lifecycle"><img src="https://agentmods.dev/badge/rules/felipebarcelospro/igniter-js/feature-lifecycle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 43 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,052 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00043	$0.01052
Opus 5.5	$0.00017	$0.00421
Sonnet 5.5	$0.00009	$0.00210
Haiku 4.5	$0.00004	$0.00105

Measured 1mo ago against content hash 06392e30de8d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

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 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.

apps/sample-realtime-chat/.cursor/rules/feature-lifecycle.mdc · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feature Development Lifecycle

This document outlines the structured workflow for developing new features, ensuring that every stage—from conception to implementation planning—is systematic, well-researched, and aligned with project goals.

Phase 1: Requirement Gathering & Research

This initial phase focuses on defining what needs to be built. It integrates research from documentation and community feedback to create a solid foundation.

1.1. Initial Research

Explore Documentation: Use get_documentation to understand existing patterns and APIs relevant to the proposed feature.
Community Insights: Use search_github_issues to find related feature requests, bug reports, or discussions. This helps validate the problem and gather context from the community.
Memory Search: Use search_memories to find internal knowledge, past decisions, or similar features.

1.2. Defining Requirements

Store Requirements: Create a memory (type: "insight", category: "requirements") for the feature.
Content Format:
- Introduction: A brief summary of the feature.
- User Stories: "As a [role], I want [feature], so that [benefit]."
- Acceptance Criteria (EARS format):
  - WHEN [event] THEN [system] SHALL [response]
  - IF [precondition] THEN [system] SHALL [response]
Iteration and Approval:
1. I will generate the initial requirements based on our discussion.
2. I will then ask for your feedback: "Do the requirements look good? If so, we can move on to the design."
3. This process will be repeated until you give explicit approval. I will not proceed without it.

Phase 2: Design & Architecture

Once requirements are approved, this phase defines how the feature will be built.

2.1. Technical Design

Store Design: Create an architectural_decision memory for the feature design.
Core Sections:
1. Overview: High-level summary of the technical approach.
2. Architecture: How the feature fits into the existing system. Mermaid diagrams should be used for visualization.
3. Components & Interfaces: Breakdown of new or modified components.
4. Data Models: Changes to the database schema or data structures.
5. Error Handling: Strategy for managing potential errors.
6. Testing Strategy: Plan for unit, integration, and end-to-end tests.
Link to Requirements: The design memory must be related to the requirements memory (relationshipType: "implements").

Read the full file on GitHub · 76 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.

1mo ago First seen · 76 lines · 43 tokens per session scan A 06392e30de8d

Subscribe to this mod's changes

feature-lifecycle is a cursor rule published in the GitHub repository felipebarcelospro/igniter-js (241 stars, last pushed 8d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,052 once invoked, about $0.0002 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 2026-09-01.

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feature-lifecycle