Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add raine/skills --skill "consult-auto"
Install specific skill from multi-skill repository
# Description
Consult Gemini and Codex for high-level planning, synthesize into a detailed plan, implement, then get final review. No user interaction.
# SKILL.md
name: consult-auto
description: Consult Gemini and Codex for high-level planning, synthesize into a detailed plan, implement, then get final review. No user interaction.
Consult external LLMs for planning ideas, synthesize into a detailed plan, implement, and review.
Reviewer Configuration
Arguments: $ARGUMENTS
Check the arguments for flags:
Reviewer flags (mutually exclusive):
- --gemini β use only Gemini
- --codex β use only Codex
- --claude β use a Claude subagent (Task tool)
- No flag β use both Gemini and Codex in parallel (default)
Mode flags:
- --dry-run β plan only, skip implementation and final review (stop after Phase 3/3.5)
- --skip-final β skip the final review phase (Phase 5)
- --rounds N β after synthesizing the plan, run N-1 additional review-refine cycles (default: 1). Max: 3.
- --skip-explore β skip Phase 1 codebase exploration, go straight to Phase 2 with only the task description and any explicitly mentioned files
Strip all flags from arguments to get the task description.
Phase 1: Understand the Task (No Questions)
If --skip-explore: Skip the exploration step. Use only the task description and any files the user has explicitly mentioned. Proceed directly to Phase 2 with a minimal context summary.
- Explore the codebase - use Glob, Grep, Read to understand:
- Relevant files and their structure
- Existing patterns and conventions
-
Dependencies and interfaces
-
Make reasonable assumptions - do NOT ask clarifying questions
- Use best judgment based on codebase context
- Prefer simpler solutions when ambiguous
-
Follow existing patterns in the codebase
-
Prepare context summary - create a brief summary of:
- The task to be implemented
- Relevant files discovered
- Key patterns and conventions in the codebase
- Any constraints or considerations
Phase 2: Consult for High-Level Planning
Based on the reviewer flag from arguments, consult external LLMs for high-level planning ideas.
Planning prompt (include your context summary and relevant file contents):
I need to implement the following task:
[Task description]
Here's what I found in the codebase:
[Context summary - relevant files, patterns, conventions]
Provide a high-level implementation plan:
- What approach would you recommend?
- What files need to be created or modified?
- What are the key implementation steps?
- Any edge cases or concerns to address?
- Any architectural decisions to consider?
Be specific about file paths and implementation details. Focus on the approach, not boilerplate.
If --gemini: Gemini only
Call mcp__consult-llm__consult_llm with:
- model: "gemini"
- prompt: Planning prompt above
- files: Array of relevant source files discovered in Phase 1
If --codex: Codex only
Call mcp__consult-llm__consult_llm with:
- model: "openai"
- prompt: Planning prompt above
- files: Array of relevant source files discovered in Phase 1
If --claude: Claude subagent
Use the Task tool with subagent_type: "general-purpose" and a prompt like:
I need a high-level implementation plan for: [task description]
Explore the codebase and provide:
- Recommended approach
- Files to create or modify
- Key implementation steps
- Edge cases and concerns
- Architectural decisions
Be specific about file paths. Focus on the approach, not boilerplate.
If no flag (default): Both Gemini and Codex in parallel
Spawn BOTH as parallel subagents (Agent tool, subagent_type: "general-purpose", model: "sonnet"). Each subagent prompt must include the full planning prompt and file list so it can make the MCP call independently.
Gemini subagent β prompt must include:
- Call mcp__consult-llm__consult_llm with model: "gemini", prompt: the planning prompt, files: [array of relevant source files]
- Return the COMPLETE response including any [thread_id:xxx] prefix
Codex subagent β prompt must include:
- Call mcp__consult-llm__consult_llm with model: "openai", prompt: the planning prompt, files: [array of relevant source files]
- Return the COMPLETE response including any [thread_id:xxx] prefix
Maintaining conversation context with thread_id
After each MCP call, check the response for a [thread_id:xxx] prefix. Extract and store the thread ID per model (e.g., gemini_thread_id, codex_thread_id). Pass the corresponding thread_id in all subsequent calls to the same model (Phase 3.5 rounds, Phase 5) so the reviewer retains full context from the planning phase without resending all files.
Phase 3: Synthesize and Write Detailed Plan
After receiving planning ideas from the LLM(s):
- Compare approaches - if using both LLMs:
- Identify common recommendations (high confidence)
- Note where they differ
-
Evaluate trade-offs of conflicting suggestions
-
Synthesize the best approach using your judgment:
- Take the strongest ideas from each LLM
- Resolve conflicts by preferring simpler solutions
- Ensure consistency with codebase patterns
-
Fill in gaps neither LLM addressed
-
Write the detailed plan following this structure:
# [Feature Name] Implementation Plan
**Goal:** [One sentence describing what this builds]
**Approach:** [2-3 sentences about the synthesized approach]
**Sources:**
- Gemini suggested: [key idea taken]
- Codex suggested: [key idea taken]
- Agent decision: [any conflicts resolved]
**Assumptions:** [List any assumptions made without asking the user]
---
### Task 1: [Short description]
**Files:**
- Create: `exact/path/to/file.py`
- Modify: `exact/path/to/existing.py` (lines 123-145)
**Steps:**
1. [Specific action]
2. [Specific action]
**Code:**
```language
// Include actual code, not placeholders
```
---
Guidelines:
- Exact file paths - never "somewhere in src/"
- Complete code - show the actual code
- Small tasks - 2-5 minutes of work each
- DRY, YAGNI - only what's needed
- Credit sources - note which LLM influenced each major decision
Save the plan to history/<date>-plan-<feature-name>.md (e.g. history/2026-02-15-plan-user-auth.md).
Phase 3.5: Refine Plan (if --rounds N where N > 1)
If --rounds N is specified and N > 1, run N-1 additional review-refine cycles on the synthesized plan.
For each additional round:
- Send plan for review using the same reviewer(s) with this prompt, passing the
thread_idfrom the previous call. The reviewer already has context from the planning phase, so you don't need to resend files β just include the updated plan file:
```
This is revision [N] of the implementation plan. Review the plan: - Are the tasks correctly ordered and sized?
- Any missing steps or edge cases?
- Are the file paths and code snippets accurate?
- Any architectural concerns?
- Is this ready for implementation?
Focus on issues worth fixing. Be concise.
```
- Apply improvements - synthesize feedback and update the plan:
- Fix issues both reviewers identified
- Address valid concerns from either reviewer
-
Discard conflicting or minor suggestions
-
Track changes - add a "Revision History" section to the plan noting what changed each round
-
Stop early if reviewers indicate no further changes needed
-
Update thread_id after each round β use the latest
thread_idfrom the response
After completing all rounds (or stopping early), proceed to Phase 4.
Phase 4: Implement
If --dry-run: Skip to Phase 6 (Summary) - report the plan without implementing.
Implement the plan without further interaction:
- Follow the plan exactly - implement each task in order
- Commit after each logical unit - keep commits small and focused
- If something is unclear - make a reasonable decision and note it in the commit message
- If a task fails - attempt to fix it before moving on
- Only stop if there's a blocking error that cannot be resolved
Implementation rules:
- Work through tasks sequentially
- Test changes when possible
- Keep commits atomic and well-documented
- Use commit messages that explain the "why"
Phase 5: Final Review
If --skip-final: Skip to Phase 6 (Summary).
After implementation is complete, get a final review using the same reviewer(s) from Phase 2.
-
Generate the diff - get the full diff of all changes made
-
Consult the reviewer(s) based on the flag:
If --gemini: Gemini only
Call mcp__consult-llm__consult_llm with:
- model: "gemini"
- prompt: Final review prompt below
- git_diff: { "files": [list of changed files], "base_ref": "HEAD~N" } (where N is number of commits)
- thread_id: The gemini_thread_id from Phase 2/3.5 (reviewer already knows the plan)
If --codex: Codex only
Call mcp__consult-llm__consult_llm with:
- model: "openai"
- prompt: Final review prompt below
- git_diff: { "files": [list of changed files], "base_ref": "HEAD~N" }
- thread_id: The codex_thread_id from Phase 2/3.5
If --claude: Claude subagent
Use the Task tool with subagent_type: "general-purpose" and instruct it to run git diff to see changes.
If no flag (default): Both Gemini and Codex in parallel
Spawn BOTH as parallel subagents (Agent tool, subagent_type: "general-purpose", model: "sonnet"). Each subagent prompt must include the full review prompt, git_diff details, and thread_id.
Gemini subagent β prompt must include:
- Call mcp__consult-llm__consult_llm with model: "gemini", prompt: the final review prompt, git_diff: { "files": [list of changed files], "base_ref": "HEAD~N" }, thread_id: gemini_thread_id from Phase 2/3.5
- Return the COMPLETE response including any [thread_id:xxx] prefix
Codex subagent β prompt must include:
- Call mcp__consult-llm__consult_llm with model: "openai", prompt: the final review prompt, git_diff: { "files": [list of changed files], "base_ref": "HEAD~N" }, thread_id: codex_thread_id from Phase 2/3.5
- Return the COMPLETE response including any [thread_id:xxx] prefix
Final review prompt:
Review this implementation for bugs, issues, or improvements:
- Any obvious bugs or edge cases missed?
- Code quality issues (error handling, naming, structure)?
- Deviations from best practices?
- Security concerns?
Be concise. Only flag issues worth fixing.
- Apply fixes automatically - if the review identifies real issues:
- Fix bugs and edge cases
- Improve error handling if clearly needed
-
Commit each fix separately with clear messages
-
Skip minor style suggestions - don't refactor for style alone
Phase 6: Summary
Present a final summary to the user:
## Summary
**Implemented:** [One sentence describing what was built]
**Planning sources:**
- Gemini contributed: [key idea]
- Codex contributed: [key idea]
- Resolved conflicts: [if any]
**Post-implementation fixes:**
- [Fix applied after final review, if any]
**Commits:**
- `abc1234` - [commit message]
- `def5678` - [commit message]
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.