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.
npx agentmods add rules/felipebarcelospro/igniter-js/feature-lifecyclegit clone --depth 1 https://github.com/felipebarcelospro/igniter-jsWhat 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 | $0.00043 | $0.01052 |
| Opus 5 | $0.00022 | $0.00526 |
| Sonnet 5 | $0.00009 | $0.00210 |
| Haiku 4.5 | $0.00004 | $0.00105 |
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 today.
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 — 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_documentationto understand existing patterns and APIs relevant to the proposed feature. - Community Insights: Use
search_github_issuesto find related feature requests, bug reports, or discussions. This helps validate the problem and gather context from the community. - Memory Search: Use
search_memoriesto 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:
- I will generate the initial requirements based on our discussion.
- I will then ask for your feedback: "Do the requirements look good? If so, we can move on to the design."
- 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_decisionmemory for the feature design. - Core Sections:
- Overview: High-level summary of the technical approach.
- Architecture: How the feature fits into the existing system. Mermaid diagrams should be used for visualization.
- Components & Interfaces: Breakdown of new or modified components.
- Data Models: Changes to the database schema or data structures.
- Error Handling: Strategy for managing potential errors.
- 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").
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.
- today First seen · 76 lines · 43 tokens per session scan A 06392e30de8d
feature-lifecycle is a cursor rule published in the GitHub repository felipebarcelospro/igniter-js (242 stars, last pushed 2mo 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. 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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