feature-dev
The feature-dev skill guides developers through a structured seven-phase workflow for implementing new features. It emphasizes understanding the codebase deeply before coding, asking clarifying questions to eliminate ambiguity, designing elegant architectures, and maintaining quality throughout. Use this when you need a methodical, deliberate approach to feature development rather than jumping directly to implementation.
git clone --depth 1 https://github.com/waybarrios/opencode-power-pack /tmp/feature-dev && cp -r /tmp/feature-dev/skills/feature-dev ~/.claude/skills/feature-devSKILL.md
# Feature Development Help a developer implement a new feature systematically. Understand the codebase deeply, identify and ask about underspecified details, design elegant architectures, then implement. ## Core principles - **Ask clarifying questions** — Identify ambiguities, edge cases, and underspecified behaviors. Ask specific, concrete questions rather than making assumptions. Wait for user answers before proceeding. - **Understand before acting** — Read and comprehend existing code patterns first. - **Read files identified by sub-tasks** — When dispatching code-explorer sub-tasks, ask them to return lists of the most important files to read. After they complete, read those files yourself to build detailed context. - **Simple and elegant** — Prioritize readable, maintainable, architecturally sound code. - **Track progress** — Use a todo list throughout. ## Working discipline These bias toward caution over speed — use judgment on trivial tasks. - **Think before acting** — state assumptions; if the request has more than one reading, surface them instead of silently choosing; if a simpler path exists, say so. - **Simplicity first** — the minimum that solves the problem; no speculative features, abstractions, configurability, or handling of impossible cases. - **Surgical changes** — touch only what the task needs; do not refactor or restyle adjacent code; match existing style; clean up only the orphans your change created, and mention unrelated dead code rather than deleting it. - **Goal-driven** — turn the task into a concrete success check and iterate until it passes. ## Phase 1: Discovery Goal: Understand what needs to be built. 1. Create a todo list covering all seven phases. 2. If the feature is unclear, ask the user: - What problem are they solving? - What should the feature do? - Any constraints or requirements? 3. Summarize your understanding and confirm with the user before proceeding. ## Phase 2: Codebase exploration Goal: Understand relevant existing code at both high and low levels. 1. Dispatch 2–3 `code-explorer` sub-tasks in parallel. Each should: - Trace through the code comprehensively, focusing on abstractions, architecture, and control flow. - Target a different aspect (similar features, high-level architecture, UX, extension points). - Return a list of 5–10 key files to read. 2. After they return, read every file they identified to build deep understanding. 3. Present a comprehensive summary of findings and patterns to the user. ## Phase 3: Clarifying questions Goal: Fill gaps and resolve ambiguities before designing. **This is one of the most important phases. Do not skip.** 1. Review the codebase findings and the original feature request. 2. Identify underspecified aspects: edge cases, error handling, integration points, scope boundaries, design preferences, backward compatibility, performance. 3. Present all questions to the user as a clear, organized list. 4. **Wait for answers** before moving to architecture. If the user says "whatever you think is best", make your recommendation explicit and get confirmation. ## Phase 4: Architecture design Goal: Design multiple implementation approaches with different trade-offs. 1. Dispatch 2–3 `code-architect` sub-tasks in parallel, each with a different focus: - **Minimal changes** — smallest diff, maximum reuse of existing code. - **Clean architecture** — maintainability, elegant abstractions. - **Pragmatic balance** — speed plus quality. 2. Review all approaches and form an opinion on which fits best for this task. Consider scope (small fix vs. large feature), urgency, complexity, and team context. 3. Present to the user: a brief summary of each approach, a trade-offs comparison, your recommendation with reasoning, and concrete differences in implementation. 4. **Ask the user which approach they prefer.** ## Phase 5: Implementation Goal: Build the feature. **Do not start without explicit user approval.** 1. Wait for approval. 2. Re-read all relevant files identified earlier. 3. Implement following the chosen architecture. 4. Strictly follow codebase conventions (naming, style, error-handling patterns). 5. Update todos as you progress. ## Phase 6: Quality review Goal: Ensure the code is simple, DRY, elegant, readable, and correct. 1. Dispatch 3 `code-reviewer` sub-tasks in parallel, each with a different focus: - Simplicity / DRY / elegance - Bugs / functional correctness - Project conventions and abstractions 2. Consolidate findings and rank issues by severity. 3. Present findings to the user and ask what they want to do (fix now, fix later, proceed as-is). 4. Address issues based on their decision. ## Phase 7: Summary Goal: Document what was accomplished. 1. Mark all todos complete. 2. Summarize: - What was built - Key decisions made - Files modified - Suggested next steps
Audit and improve project-rules files (AGENTS.md, CLAUDE.md, .agents/instructions, local overrides) so the agent keeps accurate project context. Use when the user asks to check, audit, review, update, improve, or fix their AGENTS.md or CLAUDE.md, mentions "project rules maintenance" or "agent context optimization", or when the codebase has changed enough that the rules file may be stale. Scans the repository for every rules file, grades each against a quality rubric, outputs a quality report, and applies targeted edits only after user approval.
Capture learnings from the current session into the project-rules file (AGENTS.md, CLAUDE.md, or local override) so future sessions benefit. Use when the user says "revise the rules", "update AGENTS.md / CLAUDE.md with what we just learned", "save this to project memory", "remember this for next time", or at the end of a productive session when valuable context has emerged that is not yet documented. This is the COMPLEMENT to agents-md-improver: improver audits, this one captures.
Design a feature architecture by analyzing existing codebase patterns and conventions, then provide a comprehensive implementation blueprint with specific files to create or modify, component designs, data flows, and a build sequence. Use this skill when the user asks for an architecture design, an implementation plan for a non-trivial feature, or when dispatched as a sub-task during feature-dev architecture phase.
Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".
Review a pull request or a set of code changes for bugs, logic errors, and project-convention violations using a confidence-filtered, multi-agent process. Use this skill when the user asks to review a PR, audit pending changes, or inspect a diff for problems before merging.
Review code for bugs, logic errors, security vulnerabilities, code quality issues, and adherence to project conventions, using confidence-based filtering to report only high-priority issues that truly matter. Use this skill when reviewing a small set of changes locally (such as unstaged diff), when dispatched as a sub-task during feature-dev quality review, or when the user wants a critique of a specific file or function.
Create distinctive, production-grade frontend interfaces with high design quality and accessible markup. Use this skill when the user asks to build or beautify web components, pages, applications, landing pages, dashboards, artifacts, or React/HTML/CSS UI. Generates creative, polished code that avoids generic AI aesthetics, then self-checks it against an objective accessibility and quality rubric.
Guide the creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when the user wants to build an MCP server to integrate an external API or service, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).