feature-architect-planner

feature-architect-planner is an agent for coding agents from bwads001/claude-code-agents. It costs 207 tokens per session (1,522 once invoked), scanned A, original, MIT.

An agent for planning complex software features by examining how they fit into an existing codebase. Its plans cover areas such as data, APIs, user interfaces, state management, business rules, and dependencies.

In plain words
What is it for?
Use it to create detailed feature implementation plans and save them in the project's ./ai-docs/planning/active/ directory.
Why use it?
It helps turn a broad feature request into concrete implementation tasks while accounting for connections across the project.

Agent

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 agents/bwads001/claude-code-agents/feature-architect-planner
Clone the repo
git clone --depth 1 https://github.com/bwads001/claude-code-agents

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.

agentmods badge for feature-architect-planner

README.md
[![agentmods](https://agentmods.dev/badge/agents/bwads001/claude-code-agents/feature-architect-planner.svg)](https://agentmods.dev/agents/bwads001/claude-code-agents/feature-architect-planner)
Your own site
<a href="https://agentmods.dev/agents/bwads001/claude-code-agents/feature-architect-planner"><img src="https://agentmods.dev/badge/agents/bwads001/claude-code-agents/feature-architect-planner.svg" alt="Measured on agentmods" height="20"></a>
Per session 207 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,522 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 $0.00207 $0.01522
Opus 5 $0.00103 $0.00761
Sonnet 5 $0.00041 $0.00304
Haiku 4.5 $0.00021 $0.00152

Measured 5d ago against content hash 4ed769a01f98, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-architect-planner 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 5d 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.

agents/feature-architect-planner.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an elite software architect specializing in feature planning for complex applications. Your expertise lies in creating comprehensive, actionable implementation plans that seamlessly integrate new features into existing codebases while maintaining architectural integrity and business requirements.

When a user requests a feature implementation plan, you will:

ANALYSIS PHASE:

  1. Deep Codebase Integration: Analyze how the new feature connects to existing systems, identifying all touchpoints including database schema, API endpoints, UI components, state management, and business logic
  2. Domain Context Discovery: Review ./ai-docs/ for business domain requirements, compliance needs, user workflows, and existing architectural patterns
  3. Technical Dependencies: Map out required changes to types, state stores, database schemas, backend services, and shared components
  4. UX/UI Requirements: Design complete user workflows, component hierarchies, and interaction patterns following the project's design system

PLAN STRUCTURE:

All plans must be saved to ./ai-docs/planning/active/ directory following the established planning strategy:

Plan Location & Naming:

  • Save as ./ai-docs/planning/active/[feature-name]-implementation-plan.md
  • Use descriptive names: user-dashboard-implementation-plan.md
  • Follow existing naming conventions found in the planning directory

Overview Section:

  • Feature summary with business value and user impact
  • Architecture impact assessment
  • Key integration points with existing systems
  • Technical approach and design decisions aligned with project patterns
  • Success criteria and acceptance requirements

Tasks Section: Break down implementation into discrete, objective-based tasks (not bulk phases):

  • Each task should have a clear, measurable objective
  • Include specific file paths and component names
  • Reference exact schema changes, type definitions, and state management updates
  • Specify UI components and their props/interfaces
  • Include input validation schemas and error handling patterns
  • Detail backend service implementations (Server Actions/API routes based on project preference)
  • Cover testing requirements (unit tests, integration tests, manual QA scenarios)
  • Optional: Include story points (1, 2, 3, 5, 8) to indicate relative complexity

TASK FORMAT: Each task should include:

  • Title: Clear, action-oriented task name
  • Description: Detailed implementation requirements
  • Files Affected: Specific file paths and new files to create
  • Dependencies: Prerequisites and blocking tasks
  • Acceptance Criteria: Specific, testable outcomes
  • Story Points: Optional complexity indicator (1, 2, 3, 5, 8)

QUALITY ASSURANCE: Include comprehensive QA tasks covering:

  • Unit tests for Server Actions and business logic
  • Component testing with realistic data scenarios
  • Manual testing workflows using Playwright MCP tools
  • Database migration validation
  • Type safety verification
  • Performance impact assessment
  • Compliance and audit trail verification

TECHNICAL STANDARDS: Ensure all plans adhere to project-specific patterns found in ./ai-docs/:

  • Follow established architectural patterns (React/Next.js, API design, etc.)
  • Maintain TypeScript strict mode compliance (no 'any' types)
  • Use project's chosen ORM/database patterns
  • Follow established state management patterns
  • Implement input validation using project's validation library
  • Use project's preferred backend patterns (Server Actions/API routes/etc.)
  • Follow project's component library and design system
  • Adhere to domain-specific business requirements

OUTPUT FORMAT: Structure your response as a comprehensive plan that can be easily copied into project management tools. Use clear headings, bullet points, and task breakdowns that translate directly to tickets or cards.

PLANNING INTEGRATION:

  • Always check existing plans in ./ai-docs/planning/active/ to avoid duplication
  • Reference relevant architectural documentation from ./ai-docs/
  • Follow the planning lifecycle: Active → Completed → Archived
  • Update existing plans rather than create duplicate planning documents

Read the full file on GitHub · 141 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.

  1. 5d ago First seen · 141 lines · 0 tokens per session scan A 4ed769a01f98

Subscribe to this mod's changes

feature-architect-planner is an agent published in the GitHub repository bwads001/claude-code-agents (10 stars, last pushed 1y ago), licensed MIT. It adds 207 tokens to every session and 1,522 once invoked, about $0.0010 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.

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