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By aimi-so
Run autonomous engineering workflows with standalone agents that execute headless browser tasks, generate Rails/Ruby code, manage Git worktrees, review code for minimalism and security, validate database migrations, and orchestrate deployment checklists
npx claudepluginhub aimi-so/aimi-engineering-plugin --plugin aimi-engineeringExplore ideas through guided brainstorming with batched questions and codebase research
Enrich tasks.json stories with research insights
Execute all pending stories autonomously with wave-based parallelism
Execute the next pending story from tasks.json
Generate tasks.json directly from a feature description
Visually compares live UI implementation against Figma designs and provides detailed feedback on discrepancies. Use after writing or modifying HTML/CSS/React components to verify design fidelity.
Iteratively refines UI design through N screenshot-analyze-improve cycles. Use PROACTIVELY when design changes aren't coming together after 1-2 attempts, or when user requests iterative refinement.
Detects and fixes visual differences between a web implementation and its Figma design. Use iteratively when syncing implementation to match Figma specs.
Creates or updates README files following Ankane-style template for Ruby gems. Use when writing gem documentation with imperative voice, concise prose, and standard section ordering.
Researches and synthesizes external best practices, documentation, and examples for any technology or framework. Use when you need industry standards, community conventions, or implementation guidance.
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
Resolve all PR comments using parallel processing. Use when addressing PR review feedback, resolving review threads, or batch-fixing PR comments.
Execute a single story from the tasks file autonomously. This skill defines how Task-spawned agents execute individual stories. Used internally by /aimi:execute and /aimi:status commands.
Generate tasks.json directly from a feature description. Full pipeline: research, spec analysis, story decomposition, JSON output. Triggers on: plan feature, generate tasks, create task list, direct planning.
Browser automation using Vercel's agent-browser CLI. Use when you need to interact with web pages, fill forms, take screenshots, or scrape data. Alternative to Playwright MCP - uses Bash commands with ref-based element selection. Triggers on "browse website", "fill form", "click button", "take screenshot", "scrape page", "web automation".
Executes bash commands
Hook triggers when Bash tool is used
External network access
Connects to servers outside your machine
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Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Plan and autonomously build a software task end-to-end. Recons the codebase, applies preloaded memory, decomposes into the right number of phases, gets one confirmation, then prepares a single ready-to-paste /goal command — one paste between you and done — that drives execution to completion with built-in retry, fix-spec recovery, and per-phase memory writeback. Works on Claude Code and Codex.
Long Task Harness for AI agents - task/feature-driven development with external memory
Interactive feature development workflow from idea to implementation (Requirements → Design → Tasks → Execute)
Spec-driven development for big features. When features get too big, plan mode gets too vague—leading to hallucinations during implementation. ShipSpec replaces vague plans with structured PRDs, technical designs, and ordered tasks that keep Claude grounded.
AI-powered cascading development framework with design document system and multi-agent collaboration. Breaks down projects into Features (Mega Plan), Features into Stories (Hybrid Ralph), with auto-generated technical design docs, dependency-driven batch execution, Git Worktree isolation, and support for multiple AI agents (Codex, Amp, Aider, etc.).
Implementation planning, execution, and PR creation workflows with multi-agent collaboration
Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
No model invocation
Executes directly as bash, bypassing the AI model
No model invocation
Executes directly as bash, bypassing the AI model
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Autonomous task execution for Claude Code with structured JSON task management.
Transform implementation plans into executable user stories, then run them autonomously with full context isolation. Stories with independent dependencies execute in parallel via Team orchestration with git worktrees. Each story gets its own agent with automatic state tracking.
No external dependencies. This plugin is fully standalone.
# Add marketplace and install
claude /plugin marketplace add https://github.com/aimi-so/aimi-engineering-plugin
claude /plugin install aimi-engineering
Verify installation:
claude /plugin list
/aimi:status
Install using the built-in installer script. No external dependencies required.
git clone https://github.com/aimi-so/aimi-engineering-plugin
cd aimi-engineering-plugin
./install.sh --to opencode
This installs the plugin into OpenCode's global config directory (~/.config/opencode/) with full compatibility translation:
commands/aimi/plan.md) so they appear as /aimi:plan, /aimi:execute, etc.OPENCODE_CONFIG_DIR, and error messages point to the OpenCode installer~/.config/opencode/skills/ with SKILL.md and reference files preserved~/.config/opencode/agents/aimi-*.md with model fields preservedopencode.json for autonomous Bash execution~/.config/opencode/plugins/aimi-engineering/opencode.jsonAIMI_PLUGIN_DIR is set in your shell profileAfter installation, restart your shell or run:
export AIMI_PLUGIN_DIR="$HOME/.config/opencode/plugins/aimi-engineering"
disable-model-invocation is not supported in OpenCode. The installer prepends a side-effect warning to command bodies as a workaround, instructing the model not to invoke sub-agents autonomously.AskUserQuestion tool is not available in OpenCode. Commands that use it are rewritten to use natural conversation prompts instead (asking the user directly in the response).subagent_type (e.g., subagent_type: researcher) is not yet supported in OpenCode. Agents are installed as general-purpose with the agent prompt inlined into the Task tool invocation.To install into .opencode/ in your project directory instead of globally:
./install.sh --to opencode --project
./install.sh --uninstall --from opencode
./install.sh --to opencode --dry-run
The AIMI_PLUGIN_DIR environment variable points to the installed plugin directory so that commands can locate CLI scripts, skill references, and agent definitions. The installer sets this automatically. Do not set this variable manually unless you have a custom installation layout.
# 1. Brainstorm your feature
/aimi:brainstorm Add user authentication with email/password
# 2. Generate tasks directly
/aimi:plan Add user authentication
# 3. Execute all stories autonomously
/aimi:execute
# 4. Review the implementation
/aimi:review
| Command | Description | Usage |
|---|---|---|
/aimi:brainstorm | Explore ideas through guided brainstorming | /aimi:brainstorm [feature] |
/aimi:plan | Generate tasks.json directly from feature description | /aimi:plan [feature] |
/aimi:deepen | Enrich tasks.json stories with research insights | /aimi:deepen [tasks-path] |
/aimi:status | Show current task execution progress | /aimi:status |
/aimi:next | Execute the next pending story | /aimi:next |
/aimi:execute | Run all stories autonomously (parallel for v3, sequential for v2.2) | /aimi:execute |
/aimi:review | Multi-agent code review with findings synthesis | /aimi:review [PR or branch] |
/aimi:swarm | Execute multiple tasks.json files in parallel Docker sandboxes | /aimi:swarm [--file path] [--max N] |
/aimi:brainstormStandalone brainstorm workflow with codebase research and Ralph-style batched multiple-choice questions. Explores requirements and approaches interactively before committing to implementation.