raine

auto

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# Install this skill:
npx skills add raine/skills --skill "auto"

Install specific skill from multi-skill repository

# Description

Autonomously create a plan, consult Gemini and Codex for improvements, apply feedback, and implement. No user interaction - uses best judgment throughout.

# SKILL.md


name: auto
description: Autonomously create a plan, consult Gemini and Codex for improvements, apply feedback, and implement. No user interaction - uses best judgment throughout.


Autonomously plan and implement a feature with external LLM review.

Reviewer Configuration

Arguments: $ARGUMENTS

Check the arguments for flags:

Reviewer flags (mutually exclusive):
- --gemini → use only Gemini for reviews
- --codex → use only Codex for reviews
- --claude → use a Claude subagent (Task tool) for reviews
- --no-review → skip all review phases (3, 4, and 6)
- No flag → use both Gemini and Codex in parallel (default)

Mode flags:
- --dry-run → plan only, skip implementation and final review (stop after Phase 4)
- --skip-final → skip the final review phase (Phase 6)
- --rounds N → repeat the review-refine cycle N times (default: 1). Max: 3.

Strip all flags from arguments to get the task description.

Phase 1: Understand the Task (No Questions)

  1. Explore the codebase - use Glob, Grep, Read to understand:
  2. Relevant files and their structure
  3. Existing patterns and conventions
  4. Dependencies and interfaces

  5. Make reasonable assumptions - do NOT ask clarifying questions

  6. Use best judgment based on codebase context
  7. Prefer simpler solutions when ambiguous
  8. Follow existing patterns in the codebase

Phase 2: Write the Plan

Create a plan document following this structure:

# [Feature Name] Implementation Plan

**Goal:** [One sentence describing what this builds]

**Approach:** [2-3 sentences about the approach]

**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

Save the plan to history/<date>-plan-<feature-name>.md (e.g. history/2026-02-15-plan-user-auth.md).

Phase 3: Consult Reviewer(s)

If --no-review: Skip to Phase 5 (Implementation).

Based on the reviewer flag from arguments:

If --gemini: Gemini only

Call mcp__consult-llm__consult_llm with:
- model: "gemini"
- prompt: See review prompt below
- files: Array including the plan file and relevant source files

If --codex: Codex only

Call mcp__consult-llm__consult_llm with:
- model: "openai"
- prompt: See review prompt below
- files: Array including the plan file and relevant source files

If --claude: Claude subagent

Use the Task tool with subagent_type: "general-purpose" and a prompt like:

Review this implementation plan. The plan is in: [plan file path]

Consider:
- Are the tasks correctly ordered and sized?
- Are there any missing steps or edge cases?
- Are the file paths and code snippets accurate?
- Any architectural concerns or better approaches?
- Is the approach consistent with the existing codebase patterns?

Read the plan file and relevant source files, then provide specific, actionable feedback. Be concise.

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 and file list so it can make the MCP call independently.

Gemini subagent — prompt must include:
- Call mcp__consult-llm__consult_llm with model: "gemini", prompt: the review prompt, files: [array including the plan file and relevant source files]
- Return the COMPLETE response including any [thread_id:xxx] prefix

Codex subagent — prompt must include:
- Call mcp__consult-llm__consult_llm with model: "openai", prompt: the review prompt, files: [array including the plan file and relevant source files]
- Return the COMPLETE response including any [thread_id:xxx] prefix


Review prompt (for Gemini/Codex):

Review this implementation plan. Consider:
- Are the tasks correctly ordered and sized?
- Are there any missing steps or edge cases?
- Are the file paths and code snippets accurate?
- Any architectural concerns or better approaches?
- Is the approach consistent with the existing codebase patterns?

Provide specific, actionable feedback. Be concise.

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 4 rounds, Phase 6) so the reviewer retains full context from earlier reviews without resending the plan and files.

Phase 4: Apply Improvements

After receiving feedback from both LLMs:

  1. Synthesize feedback - identify common suggestions and valid concerns
  2. Apply improvements automatically - update the plan with:
  3. Bug fixes or edge cases both LLMs identified
  4. Architectural improvements that make sense
  5. Missing steps that are clearly needed
  6. Discard conflicting advice - if Gemini and Codex disagree, use your judgment
  7. Update the plan file with the improvements
  8. Do NOT ask the user - proceed with your best judgment

Multiple Rounds (--rounds N)

If --rounds N is specified and N > 1, repeat Phases 3-4 for additional rounds:

  • Round 2+: Send the refined plan back to reviewers with this prompt, passing the thread_id from the previous round. The reviewer already has context from prior rounds, so you don't need to resend files — just include the updated plan file:
    ```
    This is revision [N] of the implementation plan. Review the updated plan:
  • Were previous concerns addressed adequately?
  • Any new issues introduced by the changes?
  • Any remaining gaps or edge cases?
  • Is the plan ready for implementation?

Focus on what changed. Be concise.
`` - **Apply improvements** from each round before proceeding to the next - **Track changes** by round in the plan file under a "Revision History" section - **Stop early** if reviewers indicate no further changes needed - **Update thread_id** after each round — use the latestthread_id` from the response

After completing all rounds (or stopping early), proceed to Phase 5.

Phase 5: Implement

If --dry-run: Skip to Phase 7 (Summary) - report the plan without implementing.

Implement the plan without further interaction:

  1. Follow the plan exactly - implement each task in order
  2. Commit after each logical unit - keep commits small and focused
  3. If something is unclear - make a reasonable decision and note it in the commit message
  4. If a task fails - attempt to fix it before moving on
  5. 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"
- Commit improvements from consultations separately from original plan tasks

Phase 6: Final Review

If --no-review or --skip-final: Skip to Phase 7 (Summary).

After implementation is complete, review the changes using the same reviewer from Phase 3:

  1. Generate the diff - get the full diff of all changes made

  2. Consult the reviewer based on the flag:

  3. --gemini or no flag: Use mcp__consult-llm__consult_llm with model: "gemini"

  4. --codex: Use mcp__consult-llm__consult_llm with model: "openai"
  5. --claude: Use Task tool with subagent_type: "general-purpose"

Include git_diff (for mcp tool) or instruct the subagent to run git diff to see changes.

Pass the thread_id from Phase 3/4 so the reviewer has full context from the plan review. The reviewer already knows the plan — the diff shows how it was implemented.

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.
  1. Apply fixes automatically - if the review identifies real issues:
  2. Fix bugs and edge cases
  3. Improve error handling if clearly needed
  4. Commit each fix separately with clear messages (not bundled with implementation commits)

  5. Skip minor style suggestions - don't refactor for style alone

Phase 7: Summary

Present a final summary to the user:

## Summary

**Implemented:** [One sentence describing what was built]

**Plan improvements applied:**
- [Improvement from Gemini/Codex feedback]
- [Another improvement]

**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.