Feature development workflow with exploration, architecture, implementation, and review phases. Use for implementing new features or significant changes.
Executes a structured seven-phase workflow for implementing features from discovery through implementation to final review.
/plugin marketplace add sequenzia/agent-alchemy/plugin install agent-alchemy-dev-tools@agent-alchemyThis skill is limited to using the following tools:
references/adr-template.mdreferences/changelog-entry-template.mdExecute a structured 7-phase feature development workflow. This workflow guides you through understanding, exploring, designing, implementing, and reviewing a feature.
CRITICAL: Complete ALL 7 phases. The workflow is not complete until Phase 7: Summary is finished. After completing each phase, immediately proceed to the next phase without waiting for user prompts.
Execute these phases in order, completing ALL of them:
Goal: Understand what the user wants to build.
Analyze the feature description from $ARGUMENTS:
Summarize your understanding to the user. Use AskUserQuestion to confirm if your understanding is correct before proceeding.
Goal: Understand the relevant parts of the codebase.
Run deep-analysis workflow:
${CLAUDE_PLUGIN_ROOT}/../core-tools/skills/deep-analysis/SKILL.md and follow its workflowPresent the synthesized analysis to the user.
Goal: Resolve any ambiguities before designing.
Review the feature requirements and exploration findings.
Identify underspecified aspects:
Ask clarifying questions: Use AskUserQuestion to get answers for critical unknowns. Only ask questions that would significantly impact the implementation.
If no clarifying questions are needed, inform the user and proceed.
Goal: Design the implementation approach.
Load skills for this phase:
${CLAUDE_PLUGIN_ROOT}/skills/architecture-patterns/SKILL.md and apply its guidance${CLAUDE_PLUGIN_ROOT}/../core-tools/skills/language-patterns/SKILL.md and apply its guidance${CLAUDE_PLUGIN_ROOT}/../core-tools/skills/technical-diagrams/SKILL.md and apply its styling rules for any Mermaid diagrams in architecture proposalsLaunch code-architect agents:
Launch 2-3 code-architect agents (Opus) with different approaches:
Agent 1: Design a minimal, focused approach prioritizing simplicity
Agent 2: Design a flexible, extensible approach prioritizing future changes
Agent 3: Design an approach optimized for the project's existing patterns (if applicable)
Use the Task tool with subagent_type: "agent-alchemy-core-tools:code-architect":
Feature: [feature description]
Design approach: [specific approach for this agent]
Based on the codebase exploration:
[Summary of relevant files and patterns]
Design an implementation that:
- Lists files to create/modify
- Describes the changes needed in each file
- Explains the data flow
- Identifies risks and mitigations
Return a detailed implementation blueprint.
Present approaches:
User chooses approach: Use AskUserQuestion to let the user select an approach or request modifications.
Generate ADR artifact:
${CLAUDE_PLUGIN_ROOT}/skills/feature-dev/references/adr-template.mdinternal/docs/adr/internal/docs/adr/NNNN-[feature-slug].md (create internal/docs/adr/ if needed)Goal: Build the feature.
Require explicit approval: Ask the user: "Ready to begin implementation of [feature] using [chosen approach]?" Wait for confirmation before proceeding.
Read all relevant files: Before making any changes, read the complete content of every file you'll modify.
Implement the feature:
Test if applicable:
IMPORTANT: Proceed immediately to Phase 6. Do NOT stop here. Do NOT wait for user input. Implementation is complete, but the workflow requires Quality Review and Summary phases. Continue directly to Phase 6 now.
Goal: Review the implementation for issues.
Load skills for this phase:
${CLAUDE_PLUGIN_ROOT}/skills/code-quality/SKILL.md and apply its guidanceLaunch code-reviewer agents:
Launch 3 code-reviewer agents (Opus) with different focuses:
Agent 1: Review for correctness and edge cases
Agent 2: Review for security and error handling
Agent 3: Review for maintainability and code quality
Use the Task tool with subagent_type: "code-reviewer":
Review focus: [specific focus for this agent]
Files to review:
[List of files modified/created]
Review the implementation and report:
- Issues found with confidence scores (0-100)
- Suggestions for improvement
- Positive observations
Only report issues with confidence >= 80.
Aggregate findings:
Present findings: Show the user:
User decides: Use AskUserQuestion:
If fixing: make the changes and re-review if needed.
IMPORTANT: Proceed immediately to Phase 7. Do NOT stop here. The workflow requires a Summary phase to document accomplishments and update the CHANGELOG. Continue directly to Phase 7 now.
Goal: Document and celebrate accomplishments.
Summarize accomplishments: Present to the user:
Update CHANGELOG.md:
${CLAUDE_PLUGIN_ROOT}/skills/feature-dev/references/changelog-entry-template.mdchangelog-format skill for Keep a Changelog guidelines[Unreleased] section with:
CHANGELOG.md doesn't exist, create it with proper header[Unreleased]Final message: Congratulate the user and offer next steps:
If any phase fails:
Exploration and synthesis agent coordination is handled by the deep-analysis skill in Phase 2, which uses Agent Teams with hub-and-spoke coordination. Deep-analysis performs reconnaissance, composes a team plan (auto-approved when invoked by another skill), assembles the team, and manages the exploration/synthesis lifecycle. See that skill for team setup, approval flow, agent model tiers, and failure handling details.
When launching other parallel agents (code-architect from core-tools, code-reviewer):
When calling Task tool for agents:
subagent_type: "agent-alchemy-core-tools:code-architect" (cross-plugin, from core-tools)subagent_type: "code-reviewer" for review agents (same plugin)Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.
Search, retrieve, and install Agent Skills from the prompts.chat registry using MCP tools. Use when the user asks to find skills, browse skill catalogs, install a skill for Claude, or extend Claude's capabilities with reusable AI agent components.
Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.