Agent skill

Mega Plan

by Taoidle in Taoidle/plan-cascade

Project-level multi-task orchestration system. An agent skill from Taoidle/plan-cascade.

MITAuto-check: notesProduct & Project Management

Install Mega Plan

skills CLI
$ npx skills add Taoidle/plan-cascade --skill mega-plan -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Taoidle/plan-cascade mega-plan --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Taoidle/plan-cascade.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mega-plan .claude/skills/mega-plan && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mega-plan
GitHub stars
133
Token cost
~3.4k tokens
SKILL.md length
831 words
Files
15 (incl. scripts)
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Project-level multi-task orchestration system. An agent skill from Taoidle/plan-cascade.

  • Works in 3 steps: Check if .mega-execution-context.md… → If YES → If NO but mega-plan.json exists
  • Tasks that involve PRD writing
  • SKILL.md covers Auto-Recovery Protocol…, Architecture, Quick Start and File Structure, plus 8 more sections
  • Runs Python scripts from its folder; calls uv

What it does

Mega Plan is an agent skill from Taoidle/plan-cascade. Project-level multi-task orchestration system. Manages multiple hybrid:worktree features in parallel with dependency resolution, coordinated PRD generation, and unified merge workflow.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts (for example `commands/approve.md`, `commands/complete.md` and `commands/edit.md`).

It sits in Product & Project Management, covering PRD writing and Git worktrees. The repository describes itself as: AI-powered cascading development framework. Decompose complex projects into parallel executable tasks with auto-generated PRDs, design docs, and multi-agent collaboration (Claude… The licence is MIT.

When your agent uses it

  • Tasks that involve PRD writing
  • Tasks that involve Git worktrees

Example prompts

  • “/mega-plan”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Task, Glob, Grep, AskUserQuestion

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Check if .mega-execution-context.md exists in the project root
  2. If YES
  3. If NO but mega-plan.json exists

What it can do on your machine

Read from SKILL.md and the folder at commit 5223f82. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash
    • Task
    • Glob
    • Grep
    • AskUserQuestion

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mega Plan loads about 3.4k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 831 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash, Task, Glob, Grep, AskUserQuestion

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Taoidle/plan-cascade at commit 5223f82, republished under its MIT licence (© Taoidle). 831 words, ~3,438 tokens.

Download SKILL.mdSave it as .claude/skills/mega-plan/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.
name
mega-plan
description
Project-level multi-task orchestration system. Manages multiple hybrid:worktree features in parallel with dependency resolution, coordinated PRD generation, and unified merge workflow.
allowed-tools
Read, Write, Edit, Bash, Task, Glob, Grep, AskUserQuestion
version
3.2.0
user-invocable
true

Mega Plan

A project-level orchestration system that sits above hybrid-ralph to manage multiple parallel features as a unified project plan.

Auto-Recovery Protocol (CRITICAL)

At the START of any interaction, perform this check to recover context after compression/truncation:

  1. Check if .mega-execution-context.md exists in the project root

  2. If YES:

    • Read the file content using Read tool
    • Display: "Detected ongoing mega-plan execution"
    • Show current batch and active worktrees from the file
    • CRITICAL: All feature work MUST happen in worktrees, NOT main branch
    • If unsure of state, suggest: /mega:resume --auto-prd
  3. If NO but mega-plan.json exists:

    • Run: uv run python "${CLAUDE_PLUGIN_ROOT}/skills/mega-plan/scripts/mega-context-reminder.py" both
    • This will generate the context file and display current state

This ensures context recovery even after:

  • Context compression (AI summarizes old messages)
  • Context truncation (old messages deleted)
  • New conversation session
  • Claude Code restart

Architecture

Level 1: Mega Plan (Project Level)
    └── Level 2: Features (Feature Level) = hybrid:worktree
              └── Level 3: Stories (Story Level) = hybrid internal parallelism

Quick Start

Create a Mega Plan

Generate a mega-plan from your project description:

/mega:plan Build an e-commerce platform with user authentication, product catalog, shopping cart, and order processing

This will:

  1. Analyze your project description
  2. Break it into features with dependencies
  3. Create mega-plan.json, mega-findings.md, .mega-status.json
  4. Display the plan for review
Approve and Execute

After reviewing the mega-plan:

/mega:approve

Or with automatic PRD approval for all features:

/mega:approve --auto-prd

This will:

  1. Calculate feature batches based on dependencies
  2. Create worktrees for Batch 1 features
  3. Generate PRDs in each worktree
  4. Wait for PRD approvals (or auto-approve with --auto-prd)
  5. Execute story batches within each feature
  6. Progress to next feature batch when complete
Monitor Progress
/mega:status

Shows:

  • Overall project progress percentage
  • Feature status by batch
  • Story progress within each feature
  • Current batch details
Complete and Merge

When all features are complete:

/mega:complete

This will:

  1. Verify all features are complete
  2. Merge features in dependency order
  3. Clean up worktrees and branches
  4. Remove mega-plan files

File Structure

project-root/
├── mega-plan.json              # Project-level plan
├── mega-findings.md            # Shared findings (read-only in worktrees)
├── .mega-status.json           # Execution status
├── .worktree/
│   ├── feature-auth/
│   │   ├── prd.json           # Feature PRD
│   │   ├── findings.md        # Feature-specific findings
│   │   ├── progress.txt       # Story progress
│   │   ├── mega-findings.md   # Read-only link to shared findings
│   │   └── .planning-config.json
│   └── feature-products/
│       └── ...

Commands Reference

/mega:plan

Generate a mega-plan for project-level multi-feature orchestration. Breaks a complex project into parallel features with dependencies.

/mega:plan [options] <project description> [design-doc-path]

Parameters:

ParameterDescription
--flow <quick|standard|full>Execution flow depth controlling quality gate strictness
--tdd <off|on|auto>Test-Driven Development mode for feature execution
--confirmRequire confirmation before each batch
--no-confirmDisable batch confirmation
--spec <off|auto|on>Spec interview before plan generation
--first-principlesEnable first-principles questioning in spec interview
--max-questions NMax questions in spec interview
design-doc-pathOptional path to existing design document

Parameters are saved to mega-plan.json and propagated to /mega:approve and feature-level /approve commands.

Example:

/mega:plan --flow full --tdd auto Create a blog platform with user accounts, article management, comments, and RSS feeds
/mega:edit

Edit the mega-plan interactively. Add, remove, or modify features.

/mega:edit
/mega:approve

Approve the mega-plan and start feature execution. Creates worktrees and generates PRDs for each feature in batch-by-batch order.

/mega:approve [options]

Parameters:

ParameterDescription
--flow <quick|standard|full>Execution flow depth for feature execution
--tdd <off|on|auto>TDD mode propagated to feature execution
--confirmRequire confirmation before each batch
--no-confirmDisable batch confirmation
--spec <off|auto|on>Spec interview for feature PRD generation
--first-principlesFirst-principles questioning for spec interviews
--max-questions NMax questions in spec interviews
--auto-prdAuto-approve all generated PRDs (skip manual review)
--agent <name>Global agent override
--prd-agent <name>Agent for PRD generation phase
--impl-agent <name>Agent for story implementation phase

Approval Modes:

ModeTriggerUse Case
Manual PRD Review/mega:approveReview each feature's PRD before execution
Auto PRD Approval/mega:approve --auto-prdTrust PRD generation, fully automated execution
Show full SKILL.md (321 more words)Show less
/mega:status

Show detailed status of mega-plan execution including feature progress, story completion, and batch summary.

/mega:status
/mega:complete

Complete the mega-plan by cleaning up planning files. All features should already be merged via /mega:approve.

/mega:complete

mega-plan.json Format

json
{
  "metadata": {
    "created_at": "2026-01-28T10:00:00Z",
    "version": "1.0.0"
  },
  "goal": "Project goal",
  "description": "Original user description",
  "execution_mode": "auto",
  "target_branch": "main",
  "features": [
    {
      "id": "feature-001",
      "name": "feature-auth",
      "title": "User Authentication",
      "description": "Detailed description for PRD generation",
      "priority": "high",
      "dependencies": [],
      "status": "pending"
    }
  ]
}

Feature Status Flow

pending → prd_generated → approved → in_progress → complete
                                           ↓
                                        failed
StatusDescription
pendingFeature not yet started
prd_generatedWorktree created, PRD generated
approvedPRD approved, ready for execution
in_progressStories are being executed
completeAll stories complete
failedFeature execution failed

Execution Modes

Auto Mode

Features and their story batches execute automatically:

Batch 1 (Features) → PRDs generated → approved → stories execute → complete
         ↓
Batch 2 (Features) → PRDs generated → approved → stories execute → complete
         ↓
All complete → /mega:complete
Manual Mode

Each batch waits for explicit confirmation:

Batch 1 → PRDs generated → [you review] → /approve in each worktree
         ↓
Batch 2 → PRDs generated → [you review] → /approve in each worktree
         ↓
All complete → /mega:complete

Workflows

Complete Workflow
1. /mega:plan "Build e-commerce platform"
   ↓
2. Review generated mega-plan.json
   ↓
3. /mega:edit (if needed) or /mega:approve
   ↓
4. Feature worktrees created (Batch 1)
   ↓
5. PRDs generated in each worktree
   ↓
6. Review and /approve in each worktree (or use --auto-prd)
   ↓
7. Stories execute in parallel
   ↓
8. Monitor with /mega:status
   ↓
9. Batch 2 features start when Batch 1 complete
   ↓
10. All complete → /mega:complete
Multi-Terminal Workflow
bash
# Terminal 1: Main orchestration
/mega:plan "Project description"
/mega:approve
/mega:status  # Monitor progress

# Terminal 2: Feature 1 work
cd .worktree/feature-auth
/approve  # Approve PRD
# ... stories execute ...

# Terminal 3: Feature 2 work (parallel!)
cd .worktree/feature-products
/approve  # Approve PRD
# ... stories execute ...

# Terminal 1: After all complete
/mega:complete

Relationship with Hybrid Ralph

Mega Plan orchestrates multiple hybrid-ralph workflows:

ComponentMega PlanHybrid Ralph
ScopeProject-levelFeature-level
UnitFeaturesStories
Filesmega-plan.jsonprd.json
Findingsmega-findings.md (shared)findings.md (per-feature)
WorktreesCreates for featuresWorks within worktree
MergeAll features → targetN/A (handled by mega)

Core Python Modules

mega_generator.py

Generates mega-plan from project descriptions.

bash
# Validate mega-plan
uv run python mega_generator.py validate

# Show execution batches
uv run python mega_generator.py batches

# Show progress
uv run python mega_generator.py progress
mega_state.py

Thread-safe state management.

bash
# Read mega-plan
uv run python mega_state.py read-plan

# Read status
uv run python mega_state.py read-status

# Sync from worktrees
uv run python mega_state.py sync-worktrees
feature_orchestrator.py

Orchestrates feature execution.

bash
# Show execution plan
uv run python feature_orchestrator.py plan

# Show status
uv run python feature_orchestrator.py status
merge_coordinator.py

Coordinates final merge.

bash
# Verify all complete
uv run python merge_coordinator.py verify

# Show merge plan
uv run python merge_coordinator.py plan

# Complete (merge & cleanup)
uv run python merge_coordinator.py complete

Findings Management

Shared Findings (mega-findings.md)
  • Located at project root
  • Contains findings relevant to all features
  • Read-only copy placed in each worktree
  • Updated only from project root
Feature Findings (findings.md)
  • Located in each feature worktree
  • Contains feature-specific discoveries
  • Tagged with story IDs
  • Independent per feature

Best Practices

  1. Clear Feature Boundaries: Each feature should be independent enough to develop in isolation
  2. Minimize Dependencies: Fewer dependencies mean more parallelism
  3. Meaningful Names: Use descriptive feature names (they become directory names)
  4. Review PRDs: Take time to review generated PRDs before approving
  5. Monitor Progress: Use /mega:status regularly to track execution
  6. Handle Failures: If a feature fails, fix it in its worktree before completing

Troubleshooting

Worktree Conflict
Error: Worktree already exists

Solution: Remove stale worktree or use different feature name.

Merge Conflict
Error: Merge conflict in feature-001

Solution:

  1. Resolve conflict in target branch
  2. Re-run /mega:complete
Incomplete Features
Error: Incomplete features: feature-002, feature-003

Solution:

  1. Check /mega:status for details
  2. Complete stories in incomplete features
  3. Re-run /mega:complete

See Also

© Taoidle, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 14 other files (scripts) in skills/mega-plan of Taoidle/plan-cascade.

  • SKILL.md
  • commands/approve.md
  • commands/complete.md
  • commands/edit.md
  • commands/plan.md
  • commands/status.md
  • core/__init__.py
  • core/feature_orchestrator.py
  • core/mega_generator.py
  • core/mega_state.py
  • core/merge_coordinator.py
  • scripts/mega-context-reminder.py
  • scripts/mega-status.py
  • scripts/mega-sync.py
  • templates/mega-plan.json.template

Open the folder on GitHubat commit 5223f82

Compare with similar skills

Mega Plan next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mega Plan compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Mega Plan this skillTaoidle/plan-cascade133—~3.4kAutomated safety check: NotesMIT
Graphsmallnest/goal-workflow288—~3.9kAutomated safety check: PassMIT
Shep Workstreamsshep-ai/shep264—~2.5kAutomated safety check: PassMIT
Execute Fleetanombyte93/prd-taskmaster604—~2.2kAutomated safety check: NotesMIT
Worktree Prdvfarcic/dot-agent-deck109—~503Automated safety check: PassMIT
Autodev Paralleljh941213/my-cc-harness126—~958Automated safety check: NotesNone

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Questions about Mega Plan

What does Mega Plan do?

Project-level multi-task orchestration system. An agent skill from Taoidle/plan-cascade. Mega Plan is an agent skill from Taoidle/plan-cascade. Project-level multi-task orchestration system.

When should I use Mega Plan?

Mega Plan fits situations like: tasks that involve PRD writing; tasks that involve Git worktrees.

How do I install Mega Plan in Claude Code?

Run `npx skills add Taoidle/plan-cascade --skill mega-plan -a claude-code`. Or copy the skill folder (skills/mega-plan in Taoidle/plan-cascade) into .claude/skills/mega-plan in your project. Claude Code loads it when a task matches its description.

How do I install Mega Plan in Codex?

Run `npx skills add Taoidle/plan-cascade --skill mega-plan -a codex`. Or copy the skill folder (skills/mega-plan in Taoidle/plan-cascade) into .agents/skills/mega-plan in your project. Codex loads it when a task matches its description.

Can I use Mega Plan in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Taoidle/plan-cascade --skill mega-plan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mega-plan, .gemini/skills/mega-plan, .github/skills/mega-plan and .opencode/skills/mega-plan in your project.

What does Mega Plan need to run?

Going by SKILL.md and its folder, Mega Plan needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Task, Glob, Grep, AskUserQuestion.

Does Mega Plan access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Mega Plan safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Mega Plan use?

Mega Plan is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mega Plan use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mega Plan?

Skills that share tags, products or a category with Mega Plan: Graph (smallnest/goal-workflow, 288 stars), Shep Workstreams (shep-ai/shep, 264 stars), Execute Fleet (anombyte93/prd-taskmaster, 604 stars) and Worktree Prd (vfarcic/dot-agent-deck, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mega Plan?

Taoidle (a GitHub user) maintains it in Taoidle/plan-cascade, which has 133 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on March 14, 2026.

Source: Taoidle/plan-cascade on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.