By izmailovilya
Launch slash-command-triggered teams of AI agents to research codebases, implement multi-file features via parallel coders, run browser/CI verifications, and pass code through security/logic/quality review gates with tech-lead oversight.
Specialized architect for COMPLEX feature teams. Operates in two modes: 1. DEBATE mode (Phase 1): critiques plan from their expertise, debates with other architects via SendMessage until consensus. 2. REVIEW mode (Phase 2+): reviews code in their domain, replacing generic reviewers with domain-specific expertise. Three personas: Frontend (UI/UX/components), Backend (API/DB/security), Systems (testing/CI/DX). <example> Context: Lead spawns architects and sends plan for debate lead: "DEBATE PLAN: Review this task list from your expertise. Debate with other architects. Send SPEC APPROVED when you agree." assistant: "As the Backend Architect, I see issues with the API task scoping. SendMessage to architect-frontend and architect-systems with my critique." <commentary> Architect debates from their domain perspective, sending critiques directly to other architects. </commentary> </example> <example> Context: Architects debate a design decision architect-backend: "The API needs separate endpoints for read and write." architect-frontend: "Separate endpoints means two loading states. Can we use one with query params?" architect-systems: "Single endpoint is harder to test independently. I prefer separate." <commentary> Architects debate directly with each other — organic, not through Lead. </commentary> </example> <example> Context: Architect transitions to reviewer mode lead: "SWITCH TO REVIEW MODE. You are now reviewing code from coders." assistant: "Acknowledged. Waiting for REVIEW requests from coders." <commentary> After debate, architects transition to reviewing code in their domain. </commentary> </example> <example type="negative"> Context: Architect goes off-topic during debate assistant: "Let me also redesign the auth system while we're at it..." <commentary> Architects stay focused on the plan at hand. No scope creep. </commentary> </example>
One-shot browser verifier that uses Chrome extension to verify UI: page loads, elements visible, interactions work, no console errors. Spawned by Lead during Phase 3 verification of team-feature for browser-based checks. <example> Context: Verify skill spawns browser-verifier with UI checks lead: "Verify these browser checks: - Page http://localhost:3000/settings loads without console errors - Settings form is visible and has a Save button - Save button is clickable" assistant: "I'll navigate to the page, check console for errors, verify elements exist, and test the interaction." <commentary> Browser verifier uses Chrome extension tools to navigate, inspect elements, and check console output. </commentary> </example> <example> Context: Chrome extension is not available assistant: "Chrome extension not available. Reporting all browser checks as SKIP(capability) — these move to Human Checks." <commentary> Chrome unavailable = SKIP(capability). The system CAN'T verify, not that checks don't apply. User must acknowledge. </commentary> </example> <example> Context: Dev server is not running (ECONNREFUSED) assistant: "Dev server not responding (ECONNREFUSED on localhost:3000). Reporting all checks as BROKEN — environment unreliable." <commentary> ECONNREFUSED = BROKEN, not SKIP. The environment is unreliable — fix it and re-run. This is distinct from Chrome missing (SKIP). </commentary> </example> <example type="negative"> Context: Browser verifier tries to fix a UI bug assistant: "The button is not visible, let me update the CSS..." <commentary> Browser verifier NEVER modifies code. It only observes and reports. </commentary> </example>
One-shot CI verifier that runs build, typecheck, lint, and test commands. Reports PASS/FAIL/BROKEN per command with evidence. Spawned by Lead during Phase 3 verification of team-feature to validate that CI pipeline passes. <example> Context: Verify skill spawns ci-verifier with build and test commands lead: "Run these CI checks: - `pnpm build` passes - `pnpm tsc --noEmit` no errors - `pnpm test` all pass" assistant: "I'll run each command in order, capture output, and report PASS/FAIL/BROKEN for each with evidence." <commentary> CI verifier runs ALL commands even if earlier ones fail — gives a complete picture of CI health. </commentary> </example> <example> Context: Build fails but tests pass assistant: "BUILD: FAIL — 3 TypeScript errors in src/components/Form.tsx. TYPECHECK: FAIL — same errors. LINT: PASS. TESTS: PASS (42/42)." <commentary> CI verifier continues through all commands and reports each independently. Doesn't stop at first failure. </commentary> </example> <example> Context: node_modules missing, commands not found assistant: "BUILD: BROKEN — `pnpm` command not found. All remaining checks: BROKEN — environment unreliable." <commentary> BROKEN is distinct from FAIL — it means the environment is unreliable, not that the code is wrong. BROKEN blocks completion with action: fix environment, re-run. </commentary> </example> <example type="negative"> Context: CI verifier tries to fix failing tests assistant: "Tests are failing, let me fix the imports..." <commentary> CI verifier NEVER fixes code. It only runs commands and reports results. Fixing is someone else's job. </commentary> </example>
One-shot explorer that scans a project and returns a condensed summary of stack, structure, patterns, and conventions. Spawned during planning phase of team-feature to give the Lead understanding of the codebase without filling their context with raw files. <example> Context: Lead needs to understand the project before planning a feature lead: "Explore this project for planning a 'user notifications' feature." assistant: "I'll scan the project structure, identify stack, find similar features, and return a condensed summary." <commentary> Codebase researcher explores broadly and returns structure — not raw file contents. </commentary> </example> <example type="negative"> Context: Lead wants full file contents of reference implementations lead: "Find the best example files and return their full code" assistant: "That's reference-researcher's job. I return summaries, not full file contents." <commentary> Codebase researcher returns CONDENSED summaries. Reference researcher returns FULL file contents. </commentary> </example>
Temporary implementation agent for feature teams. Receives a task with gold standard examples, implements matching patterns, runs self-checks, requests review directly from team reviewers via SendMessage, fixes feedback, and commits. Spawned per task, shut down after completion. <example> Context: Coder picks up a task and starts working lead: "You are coder-1. Claim task #3 from the task list and implement it." assistant: "I'll read the task, study gold standards, implement matching their patterns, self-check, then request review from reviewers directly." <commentary> Coder follows the full workflow: read task → study references → implement → self-check → request review from reviewers → fix → commit. </commentary> </example> <example> Context: Coder sends review request directly to reviewers assistant: "SendMessage to security-reviewer, logic-reviewer, quality-reviewer, tech-lead: REVIEW task #3. Files changed: src/server/routers/settings.ts" <commentary> Coder sends review requests directly to all team reviewers and tech-lead via SendMessage — Lead is NOT involved in the review loop. </commentary> </example> <example> Context: Gold standard pattern doesn't fit the specific case assistant: "ESCALATION: task #3. Gold standard uses useQuery but this needs real-time updates. Proposed: useSubscription. Need decision before proceeding." <commentary> Coder NEVER silently deviates from gold standard. Always escalates to Tech Lead with justification. </commentary> </example> <example> Context: Coder is stuck after 2 attempts assistant: "STUCK: task #3. Problem: the API returns paginated data but there's no cursor field in the response type." <commentary> Coder asks for help early — doesn't spin in circles. </commentary> </example> <example type="negative"> Context: Coder wants to refactor unrelated code assistant: "I notice the auth middleware could be cleaner, but that's outside my task scope. Implementing only what's assigned." <commentary> Coder stays focused on the assigned task. No scope creep, no "while I'm here" refactoring. </commentary> </example>
Analyze codebase and create/update .conventions/ directory with gold standards, anti-patterns, and checks. Use this skill when the user asks to 'set up conventions', 'create coding standards', 'extract patterns from the codebase', 'document how code should be written', 'create gold standards', 'establish code style', or wants to ensure consistent code quality across the project. Also use when starting work on a new codebase that lacks a .conventions/ directory — conventions are the foundation for all agent-teams code quality.
Conducts a short adaptive interview (2-6 questions) to understand intent, then launches /team-feature with a compiled brief. Use this skill when the user asks to 'interview before building', 'discuss feature before implementation', 'ask me questions first', 'let's talk about what to build', 'I have an idea but need to flesh it out', or when the user's feature request is vague or ambiguous and would benefit from clarification before launching a full agent team. Also use when the user explicitly wants to be involved in scoping before implementation begins.
Launch Agent Team for feature implementation with review gates (coders + specialized reviewers + tech lead). Use this skill whenever the user asks to 'build a feature', 'implement this', 'code this', 'add functionality', 'create a component/page/API', 'launch agent team', 'team feature', or gives any substantial implementation task that involves writing code across multiple files. Also use when the user describes a feature they want built — even if they don't explicitly say 'team' or 'agents'. This skill orchestrates parallel coders with security, logic, and quality reviewers through a structured pipeline. Prefer this over doing implementation yourself whenever the task touches 3+ files or involves both frontend and backend changes.
Uses power tools
Uses Bash, Write, or Edit tools
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A collection of plugins for Claude Code.
Add this marketplace to Claude Code:
/plugin marketplace add izmailovilya/ilia-izmailov-plugins
Then install any plugin:
/plugin install <plugin-name>@ilia-izmailov-plugins
Important: Restart Claude Code after installing plugins to load them.
Launch a team of AI agents to implement features with built-in code review gates.
Requires: Enable
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMSin settings.json or environment. See setup →
/plugin install agent-teams@ilia-izmailov-plugins
Usage:
/interviewed-team-feature "Add user settings page"
/team-feature docs/plan.md --coders=2
/conventions
The main workflow is /interviewed-team-feature — a short adaptive interview (2-6 questions) to understand your intent, then automatic launch of the full implementation pipeline. Spawns researchers, coders, and specialized reviewers (security, logic, quality) with automatic team scaling based on complexity (SIMPLE/MEDIUM/COMPLEX).
Expert evaluation arena — real experts independently assess options with cross-enrichment for any domain.
Requires: Enable
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMSin settings.json or environment. See setup →
/plugin install expert-arena@ilia-izmailov-plugins
Usage:
/expert-arena "Should we use microservices or monolith?"
/expert-arena "Best pricing strategy for a developer tool?"
Selects 3-5 real experts with opposing viewpoints, gathers context via researchers, launches independent evaluations with cross-enrichment, and produces an action-oriented report: verdict first, action plan second, detailed analysis for those who want to dig deeper.
Deep parallel codebase research — causal understanding, not just coverage.
Requires: Enable
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMSin settings.json or environment.
/plugin install team-research@ilia-izmailov-plugins
Usage:
/team-research "How does authentication work in this project?"
/team-research "Full architecture review"
Spawns a scout to map the landscape, then 2-7 investigators explore independent angles in parallel, followed by an adversarial challenger who stress-tests the findings. Produces a research report with causal understanding, source confidence tags, and cross-cutting insights.
Scout open-source repos for patterns and ideas to improve your own product.
/plugin install repo-scout@ilia-izmailov-plugins
Usage:
/repo-scout https://github.com/anomalyco/opencode
/repo-scout https://github.com/vercel/ai "how they handle streaming"
Two-phase approach: first understands YOUR project (2 scouts), then explores the external repo with your context (2 scouts), then adversarial challenge (2 challengers verify patterns are real and worth adopting). Only recommendations that survive the challenge make it into the final report.
Interactive feature audit for vibe-coded projects. Finds dead code, unused features, and experiments through conversation.
/plugin install vibe-audit@ilia-izmailov-plugins
Usage:
/vibe-audit # Full codebase scan
/vibe-audit features # src/features/ deep audit
/vibe-audit server # src/server/ routers & services
/vibe-audit ui # src/design-system/ components
/vibe-audit stores # src/stores/ Zustand state
Scans your codebase for suspicious areas (orphan routes, dead UI, stale code), asks if you need them, and safely removes what you don't — with git backup.
MIT
Scout open-source repos for patterns and ideas to improve your own product
Interactive feature audit for vibe-coded projects. Finds dead code, unused features, and experiments through conversation with the developer.
Expert evaluation arena — real experts independently assess options, cross-enrich, and produce a decision map
Launch a team of AI agents for deep parallel codebase research — causal understanding, not just coverage
npx claudepluginhub izmailovilya/ilia-izmailov-plugins --plugin agent-teamsLaunch a team of AI agents for deep parallel codebase research — causal understanding, not just coverage
Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
Task-focused agents for test, review, debug, docs, CI, security, refactoring, research, performance, and search-replace — with teammate and subagent role guidance
AI team workflow harness for Claude Code — specialist agents, shared skills, slash commands, hooks, and staged delegation pipeline.
Interactive skill that analyzes a task, proposes an agent team composition, and creates the team after user confirmation
Complete project development toolkit: 23 agents, 23 slash commands, 29 lifecycle hooks, and 69 reusable skills for Claude Code workflows