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task-o-matic
AI-powered task management CLI for project initialization, PRD parsing, task breakdown, and execution with multi-provider AI support
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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AI-powered task management CLI for project initialization, PRD parsing, task breakdown, and execution with multi-provider AI support
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Product Manager agent. Manages requirements lifecycle: Capture -> Refine -> Version -> Task Generation.
Expert code review agent. Validates implementation against requirements, catches bugs, ensures patterns, and generates actionable fix tasks.
Autonomous agent loop for executing, validating, and completing tasks. Handles state transitions, subtask management, and review cycles.
| name | task-o-matic |
| description | AI-powered task management CLI for project initialization, PRD parsing, task breakdown, and execution with multi-provider AI support |
AI-powered task management CLI for single projects. Helps initialize projects, parse PRDs into tasks, break down work with AI, and execute tasks with automatic retry and verification.
Use when the user wants to:
The CLI binary is at: packages/cli/dist/cli/bin.js (after building)
Run with: node packages/cli/dist/cli/bin.js <command> [options]
Or if installed globally: task-o-matic <command> [options]
# New project workflow
1. Initialize project (task-o-matic init init)
2. Configure AI provider (task-o-matic config set-ai-provider)
3. Parse PRD into tasks (task-o-matic prd parse)
4. Split tasks into subtasks (task-o-matic tasks split --all)
5. Execute tasks (task-o-matic tasks execute-loop)
# Existing project workflow
1. Attach to existing project (task-o-matic init attach)
2. Configure AI provider (task-o-matic config set-ai-provider)
3. Generate PRD from codebase (task-o-matic prd generate --from-codebase)
4. Create tasks from PRD (task-o-matic prd parse)
5. Execute tasks (task-o-matic tasks execute-loop)
# Basic initialization
task-o-matic init init --project-name my-app --package-manager bun
# With specific AI provider
task-o-matic init init --project-name my-app --ai-provider openrouter --ai-model xiaomi/mimo-v2-flash:free
# With Better-T-Stack bootstrap
task-o-matic init init --project-name my-app --frontend next --backend convex --auth better-auth
Options:
--ai-provider <provider>: AI provider (openrouter/anthropic/openai/custom)--ai-model <model>: AI model--project-name <name>: Project name--package-manager <pm>: bun/npm/pnpm (default: npm)--frontend <frontends...>: Frontend framework(s) (default: next)--backend <backend>: Backend framework (default: convex)--auth <auth>: Authentication (default: better-auth)--directory <dir>: Working directory# Attach to existing project (auto-detect stack)
task-o-matic init attach
# With full project analysis
task-o-matic init attach --analyze
# Dry run (just show detection, don't create files)
task-o-matic init attach --dry-run
# Force re-detection (update cached stack.json)
task-o-matic init attach --redetect
Options:
--analyze: Run full project analysis (TODOs, features, structure)--dry-run: Just detect stack, don't create files--redetect: Force re-detection (overwrites cached stack.json)--ai-provider <provider>: AI provider (openrouter/anthropic/openai/custom)--ai-model <model>: AI model--context7-api-key <key>: Context7 API keyWhat it detects:
What it creates:
.task-o-matic/config.json - AI settings.task-o-matic/stack.json - Cached stack detection (used by AI context).task-o-matic/mcp.json - Context7 config.task-o-matic/tasks/ - Task storage.task-o-matic/prd/ - PRD storage.gitignore if git exists# Set provider and model
task-o-matic config set-ai-provider openrouter xiaomi/mimo-v2-flash:free
# Set with custom URL
task-o-matic config set-ai-provider custom https://api.example.com
# Check current config
task-o-matic config get-ai-config
# Analyze current project and generate PRD
task-o-matic prd generate
# With streaming output
task-o-matic prd generate --stream
# With custom output filename
task-o-matic prd generate --output my-project-prd.md
# Basic parse
task-o-matic prd parse --file prd.md
# With streaming output
task-o-matic prd parse --file prd.md --stream
# With reasoning tokens (for models that support it)
task-o-matic prd parse --file prd.md --ai-reasoning 4000 --stream
# Multiple models with combination
task-o-matic prd parse --file prd.md --ai openrouter:model1 openrouter:model2 --combine-ai openrouter:combine-model
Options:
--file <path>: Path to PRD file (required)--stream: Show streaming AI output--ai-reasoning <tokens>: Max reasoning tokens for supported models--ai <models...>: Multiple AI models--combine-ai <provider:model>: Model to combine results--tools: Enable filesystem tools for project analysistask-o-matic tasks list
# With filters
task-o-matic tasks list --status todo
task-o-matic tasks list --tag frontend
# Manual creation
task-o-matic tasks create --title "Add authentication" --content "Implement OAuth2 login"
# With AI enhancement
task-o-matic tasks create --title "Add authentication" --ai-enhance --stream
# Split specific task
task-o-matic tasks split --task-id 1 --stream
# Split all tasks
task-o-matic tasks split --all --stream
# With filters
task-o-matic tasks split --all --status todo --stream
Options:
--task-id <id>: Specific task to split--all: Split all tasks without subtasks--status <status>: Filter by status (todo/in-progress/completed)--stream: Show streaming output--ai-reasoning <tokens>: Enable reasoning--dry: Preview without changes# Enhance specific task
task-o-matic tasks enhance --task-id 1 --stream
# Enhance all tasks
task-o-matic tasks enhance --all --stream
# With filter
task-o-matic tasks enhance --all --status todo --stream
Options:
--task-id <id>: Specific task to enhance--all: Enhance all tasks--status <status>: Filter by status--tag <tag>: Filter by tag--dry: Preview without changes--force: Skip confirmation# Set task status
task-o-matic tasks status --task-id 1 --status in-progress
# Available statuses: todo, in-progress, completed
# Show hierarchical task tree
task-o-matic tasks tree
# Get next task to work on
task-o-matic tasks get-next
# Execute all todo tasks
task-o-matic tasks execute-loop --status todo
# Execute specific tasks
task-o-matic tasks execute-loop --ids 1,2,3
# With specific tool
task-o-matic tasks execute-loop --tool claude
# With planning phase
task-o-matic tasks execute-loop --plan --review-plan
# With verification
task-o-matic tasks execute-loop --verify "bun run test" --verify "bun run type-check"
# Progressive model escalation
task-o-matic tasks execute-loop --try-models "gpt-4o-mini,gpt-4o,claude:sonnet-4"
Options:
--status <status>: Filter by status--tag <tag>: Filter by tag--ids <ids>: Comma-separated task IDs--tool <tool>: opencode/claude/gemini/codex (default: opencode)--max-retries <number>: Max retries per task (default: 3)--model <model>: Force specific model--verify <command>: Verification command (can be used multiple times)--plan: Generate implementation plan before execution--plan-model <model>: Model for planning--review-plan: Pause for human review of plan--review: Run AI review after execution--auto-commit: Auto-commit after each task--try-models <models>: Progressive models for retries--dry: Show what would executetask-o-matic prd create "Build a task management app with real-time updates"
# Generate questions, answer interactively, refine PRD
task-o-matic prd refine --file prd.md --output prd_refined.md --stream
# Questions mode: AI generates clarifying questions
task-o-matic prd question --file prd.md
task-o-matic prd combine --files prd1.md,prd2.md --output master.md
task-o-matic prd get-stack --file prd.md
The workflow command provides an all-in-one interactive experience:
# Full interactive workflow
task-o-matic workflow
# Skip specific steps
task-o-matic workflow --skip-bootstrap --skip-prd-question-refine
# Auto-accept all suggestions
task-o-matic workflow --auto-accept
# Use config file
task-o-matic workflow --config-file workflow-config.json
Workflow covers:
task-o-matic config set-ai-provider openrouter xiaomi/mimo-v2-flash:free
Popular free models:
xiaomi/mimo-v2-flash:freegoogle/gemma-3-27b-it:freemeta-llama/llama-3.3-8b-instruct:freetask-o-matic config set-ai-provider anthropic claude-3-5-sonnet-20241022
task-o-matic config set-ai-provider openai gpt-4o
task-o-matic config set-ai-provider custom https://api.example.com
Set in .env file or environment:
AI_PROVIDER=openrouter
AI_MODEL=xiaomi/mimo-v2-flash:free
OPENROUTER_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
OPENAI_API_KEY=your_key_here
After initialization:
.task-o-matic/
├── config.json # Project configuration
├── tasks.json # Tasks database
├── prd.md # PRD file
└── plans/ # Implementation plans
--stream for long-running AI operations to see progress--ai-reasoning with models that support it for better task breakdown--dry first for bulk operations to preview changes--verify during execution to run tests after each task--plan for complex tasks to get implementation plans first# 1. Initialize
task-o-matic init init --project-name my-app --package-manager bun
# 2. Configure AI (optional if set in .env)
task-o-matic config set-ai-provider openrouter xiaomi/mimo-v2-flash:free
# 3. Parse PRD
task-o-matic prd parse --file prd.md --stream --ai-reasoning 4000
# 4. Review tasks
task-o-matic tasks tree
# 5. Split all tasks
task-o-matic tasks split --all --stream --ai-reasoning 4000
# 6. Enhance tasks with documentation
task-o-matic tasks enhance --all --stream
# 7. Execute
task-o-matic tasks execute-loop --status todo --verify "bun run test" --plan
Use cheaper models first, escalate to better ones on failure:
task-o-matic tasks execute-loop \
--try-models "xiaomi/mimo-v2-flash:free,gpt-4o-mini,claude:sonnet-4" \
--max-retries 3
Generate implementation plans before execution:
task-o-matic tasks execute-loop \
--plan \
--plan-model gpt-4o \
--review-plan
Run multiple verification commands:
task-o-matic tasks execute-loop \
--verify "bun run type-check" \
--verify "bun run test" \
--verify "bun run lint"