approve
Approve the current PRD and begin parallel story execution. Analyzes dependencies, creates execution batches, launches background Task agents for each story,…
Generate a mega-plan for project-level multi-feature orchestration. Breaks a complex project into parallel features with dependencies. Usage: /plan-cascade:mega-plan [--flow <quick|standard|full>] [--tdd <off|on|auto>] [--confirm] [--no-confirm] [--spec <off|auto|on>]
> /plugin marketplace add Taoidle/plan-cascade > /plugin install plan-cascade@plan-cascade
How it fires
How this command gets triggered: by you, by Claude, or both.
/mega-planContext preview
What this command does when you run it.
Generate a mega-plan for project-level multi-feature orchestration. Breaks a complex project into parallel features with dependencies. Usage: /plan-cascade:mega-plan [--flow <quick|standard|full>] [--tdd <off|on|auto>] [--confirm] [--no-confirm] [--spec <off|auto|on>]
description: "Generate a mega-plan for project-level multi-feature orchestration. Breaks a complex project into parallel features with dependencies. Usage: /plan-cascade:mega-plan [--flow <quick|standard|full>] [--tdd <off|on|auto>] [--confirm] [--no-confirm] [--spec <off|auto|on>] [--first-principles] [--max-questions N] <project description> [design-doc-path]"
You are creating a **Mega Plan** - a project-level plan that orchestrates multiple features in parallel.
This command accepts flow control parameters that propagate to all feature executions:
Parameters flow through three stages in mega-plan execution:
1. **Command-line flags to THIS command** (highest priority)
2. **Command-line flags to `/plan-cascade:mega-approve`**
3. **PRD-level overrides** (per feature, if needed)
4. **Default values** (lowest priority)
**Parameter Propagation Chain:**
# Step 1: Create mega-plan with parameters
/plan-cascade:mega-plan --flow full --tdd on --spec auto "Build e-commerce"
# → Saves to mega-plan.json:
# flow_config: {level: "full", propagate_to_features: true}
# tdd_config: {mode: "on", propagate_to_features: true}
# spec_config: {mode: "auto", ...}
# Step 2: Execute with saved parameters
/plan-cascade:mega-approve
# → Reads from mega-plan.json
# → For each feature: creates PRD with inherited flow/tdd settings
# → Sub-agents execute stories with these settings
# Step 3: Execute with override
/plan-cascade:mega-approve --flow standard
# → Uses flow="standard" (overrides mega-plan.json)
# → All features get flow="standard", tdd="on" (from mega-plan.json)**Note:** Spec interview parameters (--spec, --first-principles, --max-questions) are used by the orchestrator in `mega-approve` Step 6.0, NOT propagated to feature agents.
Override the execution flow depth for all feature approve phases.
| Flow | Gate Mode | AI Verification | Code Review | Test Enforcement | |------|-----------|-----------------|-------------|------------------| | `quick` | soft | disabled | no | no | | `standard` | soft | enabled | no | no | | `full` | **hard** | enabled | **required** | **required** |
Control Test-Driven Development mode for all feature story executions.
| Mode | Description | |------|-------------| | `off` | TDD disabled | | `on` | TDD enabled with prompts and compliance checks | | `auto` | Automatically decide based on risk assessment (default) |
Require confirmation before each **feature batch** execution.
**Batch-Level Confirmation**: In mega-plan execution, confirmation happens at the batch level (before launching parallel sub-agents), not inside individual feature executions. This allows human oversight while preserving parallelism.
Disable batch-level confirmation, even when using FULL flow.
Record spec interview configuration for later execution in `mega-approve` (interviews must run in the orchestrator, never inside per-feature subagents).
Enable first-principles questions (only when spec interview runs).
Soft cap for interview length (recorded in `.state/spec-interview.json` per feature).
**CRITICAL**: If this is your first time using Plan Cascade, run `/plan-cascade:init` first to set up the environment.
# Quick check - if this fails, run /plan-cascade:init
uv run python -c "print('Environment OK')" 2>/dev/null || echo "Warning: Run /plan-cascade:init to set up environment"Plan Cascade supports two path storage modes for runtime files:
Runtime files are stored in a user directory:
File locations in new mode:
All files in project root:
To check which mode is active:
uv run python -c "from plan_cascade.state.path_resolver import PathResolver; from pathlib import Path; r=PathResolver(Path.cwd()); print('Mode:', 'legacy' if r.is_legacy_mode() else 'new'); print('Mega plan:', r.get_mega_plan_path())"Level 1: Mega Plan (This level - Project)
└── Level 2: Features (hybrid:worktree tasks)
└── Level 3: Stories (hybrid-ralph internal parallelism)**IMPORTANT**: Before creating any planning files, ensure the project's `.gitignore` is configured to ignore Plan Cascade temporary files:
# Check and update .gitignore for Plan Cascade entries uv run python -c "from plan_cascade.utils.gitignore import ensure_gitignore; from pathlib import Path;
AI-Powered Cascading Development Framework Transform complex projects into parallel executable tasks with intelligent decomposition and multi-provider execution Why Plan Cascade? • Product Editions • Quick Start • Architecture
Repo: Taoidle/plan-cascade
Approve the current PRD and begin parallel story execution. Analyzes dependencies, creates execution batches, launches background Task agents for each story,…
AI auto strategy executor. Analyzes task and automatically selects and executes the best strategy: direct execution, hybrid-auto PRD generation,…
Check and update .gitignore to exclude Plan Cascade temporary files. Ensures planning files won't be accidentally committed to version control.
Complete a worktree task. Verifies all phases are complete, commits code changes (excluding planning files), merges to target branch, and removes worktree. Can…
Show execution status dashboard. Usage: /plan-cascade:dashboard [--verbose|-v] [--json]
Generate a technical design document. Auto-detects level: project-level from mega-plan.json, or feature-level from prd.json. Provides architectural context for…