Claude Code • Repeatable Multi-Model Planning

Claude Code has Plan Mode. PlanToCode adds the architect's blueprint.

Claude Code Plan Mode is Stage 5 execution with permission controls. PlanToCode provides Stages 1-4: spec capture (voice/text), 4-stage FileFinderWorkflow, multi-model implementation_plan across GPT-5.2/Claude Sonnet 4.5/Gemini, human review and merge. Feed comprehensive blueprints into Plan Mode.

Intelligence-Driven Development: PlanToCode is the architect (repeatable multi-model planning). Claude Code Plan Mode is the construction crew (permission-controlled execution). Combine for enterprise-grade governance.

Claude Code Plan Mode (Shift+Tab, read-only analysis; --permission-mode plan for execution) provides permission-controlled execution. But it lacks repeatable pre-planning: no multi-model synthesis, no human-guided merge, no systematic file discovery. PlanToCode fills this gap with Intelligence-Driven Development Stages 1-4: (1) Specification capture via text_improvement/task_refinement, (2) 4-stage FileFinderWorkflow (root selection, regex filter, AI relevance, extended path finder), (3) Multi-model implementation_plan across GPT-5.2/Claude Sonnet 4.5/Gemini, (4) Human review and implementation_plan_merge. Stage 5: Feed merged blueprint into Claude Code Plan Mode. All technical details verified against Anthropic's official documentation.

What PlanToCode gives you here

Architect vs implementor: Complementary roles

PlanToCode is the architect (Stages 1-4): capturing requirements from voice/meetings, surveying the codebase (FileFinderWorkflow), drafting blueprints from multiple perspectives (multi-model planning), reviewing and consolidating (merge). Claude Code Plan Mode is the implementor (Stage 5): executing the blueprint with permission controls. Both needed for enterprise governance.

No brittle rules—infers patterns from code

Stage 2c AI relevance assessment and Stage 2d extended path finder analyze your codebase to infer patterns (import conventions, folder structure, test co-location). Stage 3 implementation_plan adapts to your patterns—no manual config, no brittle linting rules. Claude Code Plan Mode executes within these inferred constraints.

Repeatable multi-model planning process

Stage 3: Run implementation_plan with GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro. Each model brings a different perspective. Stage 4: Review side-by-side, identify conflicts (one suggests class components, another uses hooks). Write merge instructions ("Prefer hooks, keep error handling from the other plan"). Run implementation_plan_merge. Repeatable process, reproducible results.

Plan Mode permissions + pre-planned scope

Claude Code Plan Mode: Shift+Tab for read-only analysis, --permission-mode plan for execution with approvals. PlanToCode Stages 1-4 define scope before Plan Mode runs. Stage 4 human review locks file list, edit types (add/modify/delete), dependencies. Plan Mode executes within pre-approved scope—no permission escalation, no scope creep.

Narrative: Generate → Copy → Execute

Complete workflow: Stage 1 (capture "Add caching layer to API" via text_improvement). Stage 2 (FileFinderWorkflow discovers routes/, middleware/, redis config). Stage 3 (generate GPT-5.2 plan: Redis client setup; Gemini plan: Redis cluster + fallback). Stage 4 (merge with "Use GPT-5.2 Redis client, add Gemini fallback"). Copy merged XML. Open Claude Code, paste blueprint, enable Plan Mode (Shift+Tab), review proposed changes, approve. Claude Code executes. PlanToCode terminal logs output.

Skyscraper blueprint analogy

PlanToCode is the architect: surveys site (file discovery), drafts blueprints (multi-model), reviews structural integrity (merge), stamps approval (Stage 4 review). Claude Code Plan Mode is the construction crew: follows blueprint, requests permission before each floor (Plan Mode approvals), builds exactly as specified. No architect = improvised construction = brittle foundation.

How it works with this tool

Stages 1-4: PlanToCode repeatable planning workflow

Stage 1: Capture specification from voice notes, meeting transcripts, or text descriptions using text_improvement (clarify vague requirements) and task_refinement (break down into sub-tasks). Stage 2: Run FileFinderWorkflow—4 sub-stages: (2a) root folder selection identifies scope boundaries, (2b) regex file filter matches patterns, (2c) AI relevance assessment scores each file, (2d) extended path finder discovers transitive dependencies. Stage 3: Generate multiple implementation plans using implementation_plan prompt across GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro—each brings a different perspective. Stage 4: Review plans side-by-side, identify conflicts/gaps, write merge instructions (plain English guidance: "Prefer this plan's error handling, use that plan's test coverage"), run implementation_plan_merge to produce single XML blueprint.

Stage 5: Feeding the blueprint into Claude Code Plan Mode

Open Claude Code CLI in your repo. Paste the merged implementation_plan XML from Stage 4 as context. The XML contains file-by-file edits (add/modify/delete operations), import statements, dependency order, rollback instructions, and constraints. Toggle Plan Mode with Shift+Tab (read-only analysis: see what Claude proposes without execution). Or start in Plan Mode via --permission-mode plan (execution with approvals: Claude requests permission for each file edit). Claude Code treats the merged blueprint as the execution specification.

Plan Mode permissions layer on pre-planned scope

Claude Code Plan Mode permissions: Shift+Tab (read-only analysis, no edits), --permission-mode plan (execution with per-file approvals), default mode (full access). PlanToCode Stage 4 human review pre-approves scope: which files to touch, which operations (add/modify/delete), which dependencies to include. Plan Mode permissions enforce boundaries on pre-approved scope. No permission escalation, no scope creep. Two-stage governance: human review (Stage 4) + permission controls (Stage 5).

Concrete narrative: Caching layer implementation

Stage 1: Voice note "Add Redis caching to API endpoints to reduce DB load." Text_improvement clarifies: "Cache GET requests with 5-minute TTL, invalidate on POST/PUT/DELETE." Stage 2: FileFinderWorkflow discovers api/routes/*.ts, middleware/cache.ts (doesn't exist yet—Stage 2d suggests creation), config/redis.ts, tests/integration/. Stage 3: GPT-5.2 suggests Redis client with basic TTL; Gemini suggests Redis cluster with async invalidation. Stage 4: Merge instruction "Use simple Redis client, add async invalidation for write-heavy endpoints." Merged blueprint ready. Stage 5: Open Claude Code, paste blueprint, enable Plan Mode (Shift+Tab), review 8 file edits, approve. Claude Code executes: creates cache.ts, updates routes, adds tests, updates redis config.

Multi-model blind spot prevention

Single-model Claude Code Plan Mode provides one perspective—may miss edge cases, legacy constraints, or alternative patterns. PlanToCode Stage 3 multi-model planning runs GPT-5.2, Claude Sonnet 4.5, and Gemini 3 Pro in parallel. Each model brings a different perspective and catches different things. Stage 4 merge consolidates all three perspectives. Claude Code Plan Mode executes the synthesized blueprint—comprehensive coverage, fewer blind spots.

Repeatable process for audits and compliance

Enterprise governance requirement: reproducible planning process with audit trail. PlanToCode Stages 1-4 provide this: (1) Specification is versioned (text_improvement/task_refinement outputs stored). (2) FileFinderWorkflow results are logged (which files discovered, relevance scores). (3) Each model's implementation_plan is saved (GPT-5.2 output, Gemini output). (4) Merge instructions and final merged blueprint are versioned. Stage 5 Claude Code Plan Mode execution uses the blueprint—logs every file edit, permission request, approval. Full audit trail from requirements to execution.

Quickstart

1
Install PlanToCode alongside Claude Code
Download PlanToCode and connect it to the same repository you use with Claude Code CLI.
2
Discover relevant files
Use file discovery to build a comprehensive list of files and dependencies for your task.
3
Generate and merge plans
Create plans from multiple AI models and merge them with custom instructions for a complete architectural view.
4
Use with Claude Code Plan Mode
Provide the merged plan and discovered files to Claude Code. Use Plan Mode to review the changes before execution.

Learn more:Plan Mode guide·Permission modes

Verified from official sources

Claude Code is an official AI coding CLI from Anthropic with built-in planning capabilities.

Official docs

Claude Code supports review workflows where users can see and approve proposed changes before execution.

Official docs

Claude Code provides settings for controlling AI behavior and approval workflows.

Official docs

Claude Code integrates with local development environments and respects user permissions.

Official docs

Plan Mode tutorial

Official docs

SDK permissions note

Official docs

Frequently Asked Questions

Everything you need to know about PlanToCode

Yes. PlanToCode provides a human-in-the-loop workflow where team leads and stakeholders can review generated implementation plans, edit details, request modifications, and approve changes before they are executed by coding agents or developers. This ensures corporate governance and prevents regressions.

Upload Microsoft Teams meeting recordings or screen captures to PlanToCode. Advanced multimodal models analyze both audio transcripts (including speaker identification) and visual content (shared screens, documents) to extract specification requirements. You review the extracted insights - decisions, action items, discussion points - and incorporate them into implementation plans.

Yes. Implementation plans break down changes on a file-by-file basis with exact repository paths corresponding to your project structure. This granular approach ensures you know exactly what will be modified before execution, providing complete visibility and control.

PlanToCode is a desktop planning workspace. You run the file discovery workflow, review implementation plans in a Monaco editor, adjust prompts, and then launch the terminal from the same session. Chat tools hand you a single reply; here you stage the work before anything runs.

Yes. The app assumes you already work in large repositories and are comfortable with terminals, dependency graphs, and architecture trade-offs. The software keeps context organized so you can apply your judgment faster.

The default configuration ships with Gemini 3 Pro, GPT-5.2, Claude 4.5 Sonnet, o3, Grok 4, DeepSeek R1, and Kimi K2. You can switch models per task from the settings panel before submitting a job.

Project state, terminal logs, and plan drafts are stored locally in SQLite. Only the prompt you approve plus the files you select are sent through your configured server proxy. The Rust server in this repo can be self-hosted when you need to keep traffic on your own infrastructure.

The terminal boots the same shell you use on the host machine and auto-detects claude, cursor, codex, and gemini binaries. You can still run any other tooling manually - the PTY session is a regular shell with voice dictation available when you need hands-free input.

Start the file finder workflow to generate search patterns, relevance scoring, and prioritized file lists. Each stage runs as its own background job so you can inspect results before applying the selections to your session.

Absolutely. Plans open in a Monaco editor with syntax highlighting, diff-friendly formatting, and clipboard helpers. You can tweak the prompt, copy individual steps, and rerun generation without leaving the workspace.

Select two or more plans, write the merge guidance, and submit. The merged result is saved alongside the originals so you can compare drafts, keep notes, or rerun the merge with different instructions.

Token estimates, prompt previews, terminal health monitoring, and persistent sessions reduce rework. You decide when to run commands and which model to trust, while the app handles orchestration details.

Describe the task (typing or voice) → run the file finder workflow → generate one or more implementation plans → edit or merge plans in the Monaco editor → launch the built-in terminal to execute the plan and monitor logs. Sessions and logs stay available the next time you open the app.

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Plan software changes before you code. Review scope, merge multi-model insights, and execute with full visibility.

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