Experts
npx machina-cli add skill ConaryLabs/Mira/experts --openclawname: experts description: This skill should be used when the user asks to "consult experts", "get expert opinion", "expert review", "code review", "architecture review", "security review", "review this code", "what would an architect say", or wants a multi-perspective analysis from AI specialists. Use this for analysis and opinions only — no code changes are made. Use /mira:full-cycle instead if the user wants issues to be automatically implemented. argument-hint: "[context or focus area]"
Expert Consultation
Requires: Claude Code Agent Teams feature.
Get expert opinions on code, architecture, security, or plans using a team of 4 AI specialists.
Arguments: $ARGUMENTS
Instructions
-
Parse arguments (optional):
--members nadia,jiro-> Only spawn these specific experts (by first name)- No arguments -> spawn all 4 experts
- Any other text -> use as the context/question for the experts
-
Determine context: The user's question, the code they want reviewed, or the plan they want analyzed. If no context is obvious, ask the user what they'd like experts to review.
-
Launch the team: Call the Mira
launchMCP tool to get agent specs:launch(team="expert-review-team", scope=user_context, members="nadia,jiro" or omit for all)The
membersparameter is only needed if the user passed--members. Thescopeparameter should describe the review focus. -
Create the team:
TeamCreate(team_name=result.data.suggested_team_id) -
Create and assign tasks: For each agent in
result.data.agents:TaskCreate(subject=agent.task_subject, description=agent.task_description) TaskUpdate(taskId=id, owner=agent.name, status="in_progress") -
Spawn experts: For each agent in
result.data.agents, use theTasktool:Task( subagent_type="general-purpose", name=agent.name, model=agent.model, team_name=result.data.suggested_team_id, prompt=agent.prompt + "\n\n## Context\n\n" + user_context, run_in_background=true )Spawn all experts in parallel (multiple Task calls in one message).
IMPORTANT: Do NOT use mode="bypassPermissions" -- these are read-only discovery agents. IMPORTANT: Always pass model="sonnet" to the Task tool. This ensures read-only agents use a cost-efficient model.
-
Wait for findings: Teammates will send their findings via SendMessage when complete. Wait for all to finish.
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Synthesize findings: Combine all expert findings into a unified report:
- Consensus: Points multiple experts agree on
- Key findings per expert: Top findings from each specialist
- Tensions: Where experts disagree -- present both sides with evidence
- Action items: Concrete next steps
IMPORTANT: Preserve genuine disagreements. Do NOT force consensus. Present conditional recommendations: "If your priority is X, then..." / "If your priority is Y, then..."
-
Cleanup: Send
shutdown_requestto each teammate, then callTeamDelete.
Examples
/mira:experts
-> Prompts for what to review, then spawns all 4 experts
/mira:experts --members nadia,sable
-> Only spawns Nadia (architect) and Sable (security)
/mira:experts Review the authentication flow in src/auth/
-> All 4 experts review the auth code
/mira:experts Is this migration plan safe?
-> All 4 experts analyze the plan in context
Expert Roles
| Name | Role | Focus |
|---|---|---|
| Nadia | Systems Architect | Design patterns, API design, coupling, scalability |
| Jiro | Code Quality Reviewer | Bugs, type safety, error handling, race conditions |
| Sable | Security Analyst | SQL injection, auth bypass, input validation, secrets |
| Lena | Scope and Risk Analyst | Missing requirements, edge cases, incomplete error paths |
Source
git clone https://github.com/ConaryLabs/Mira/blob/main/plugin/skills/experts/SKILL.mdView on GitHub Overview
Get expert opinions on code, architecture, security, or plans from a four-person team of AI specialists. It delivers multi-perspective analysis without making code changes, ideal for architectural reviews, security assessments, or high-level feedback. If you want issues automatically implemented, use /mira:full-cycle.
How This Skill Works
The system uses your context to form a four-member expert team (Nadia, Jiro, Sable, Lena) and assigns the review focus. It launches read-only agents (model=sonnet) to analyze the requested area, collects findings from each expert, and then synthesizes a unified report that highlights consensus, key findings per expert, tensions, and concrete next steps.
When to Use It
- Request expert opinions on code quality, architecture, or security considerations for a project
- Perform a code or architecture review with multiple perspectives (code quality, security, risk) without changing the code
- Evaluate a migration plan or system design for safety, scalability, and edge cases
- Obtain a multi-disciplinary assessment of critical plans or requirements before implementation
- Need a structured synthesis of expert viewpoints with actionable next steps
Quick Start
- Step 1: Provide the focus area or context to review (e.g., 'review authentication flow in src/auth/')
- Step 2: Run /mira:experts [--members nadia,jiro] to spawn the expert team and set scope
- Step 3: Review the synthesized findings report with consensus, per-expert notes, tensions, and action items
Best Practices
- Provide a clear focus area and context to guide expert analysis
- Specify the scope (e.g., a module, flow, or design) and any constraints
- Indicate whether you want all four experts or a subset via --members
- Ask for both consensus and per-expert findings, including disagreements
- Clarify that this is analysis-only and implement changes with a separate process if needed
Example Use Cases
- Review the authentication flow in src/auth/ for security and reliability concerns
- Is this migration plan safe for production deployment and rollback scenarios?
- Code review of the payment processing module for error handling and edge cases
- Architecture review of the API gateway to assess scalability and coupling
- Security review of secrets handling and configuration management