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.
git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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.
[](https://agentmods.dev/commands/engineerwithai/engineerwith-agents/feature-development)<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/feature-development"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/feature-development/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.
<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/feature-development"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/feature-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.02053 |
| Opus 5.5 | $0.00000 | $0.00821 |
| Sonnet 5.5 | $0.00000 | $0.00411 |
| Haiku 4.5 | $0.00000 | $0.00205 |
Grade A, and why
feature-development 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.
This is a copy
100% identical to feature-development — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate end-to-end feature development from requirements to production deployment:
[Extended thinking: This workflow orchestrates specialized agents through comprehensive feature development phases - from discovery and planning through implementation, testing, and deployment. Each phase builds on previous outputs, ensuring coherent feature delivery. The workflow supports multiple development methodologies (traditional, TDD/BDD, DDD), feature complexity levels, and modern deployment strategies including feature flags, gradual rollouts, and observability-first development. Agents receive detailed context from previous phases to maintain consistency and quality throughout the development lifecycle.]
Configuration Options
Development Methodology
- traditional: Sequential development with testing after implementation
- tdd: Test-Driven Development with red-green-refactor cycles
- bdd: Behavior-Driven Development with scenario-based testing
- ddd: Domain-Driven Design with bounded contexts and aggregates
Feature Complexity
- simple: Single service, minimal integration (1-2 days)
- medium: Multiple services, moderate integration (3-5 days)
- complex: Cross-domain, extensive integration (1-2 weeks)
- epic: Major architectural changes, multiple teams (2+ weeks)
Deployment Strategy
- direct: Immediate rollout to all users
- canary: Gradual rollout starting with 5% of traffic
- feature-flag: Controlled activation via feature toggles
- blue-green: Zero-downtime deployment with instant rollback
- a-b-test: Split traffic for experimentation and metrics
Phase 1: Discovery & Requirements Planning
- Business Analysis & Requirements
- Use Task tool with subagent_type="business-analytics::business-analyst"
- Prompt: "Analyze feature requirements for: $ARGUMENTS. Define user stories, acceptance criteria, success metrics, and business value. Identify stakeholders, dependencies, and risks. Create feature specification document with clear scope boundaries."
- Expected output: Requirements document with user stories, success metrics, risk assessment
- Context: Initial feature request and business context
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 · 144 lines · 0 tokens per session scan A 2ae17a829510
feature-development is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,053 tokens. A static security scan graded it A with 0 findings. It is 100% identical to feature-development, differing in 5 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.