td-task-management

name	td-task-management
description	Task management for AI agents across context windows. Use when agents need to track work, log progress, hand off state, and maintain context across sessions. Includes workflows for single-issue focus, multi-issue work sessions, and structured handoffs. Essential for AI-assisted development where context windows reset between sessions.

td - Task Management for AI Agents

Overview

td is a minimalist CLI for tracking tasks and maintaining agent memory across context windows. When your AI session ends, td captures what was done, what remains, and what decisions were made—so the next session picks up exactly where the last one left off.

Core capability: Run td usage and get everything needed for the next action—current focus, pending reviews, open issues, recent decisions.

Quick Start

Session Start (Every Time)

td usage --new-session  # Auto-rotation + see current state

Output tells you:

• Active work sessions and recent decisions
• What issues are pending review (you can review these)
• Highest priority open issues
• Recent handoffs from previous sessions

Single-Issue Workflow

For focused work on one issue:

td start <issue-id>                    # Begin work
td log "OAuth callback implemented"    # Track progress
td log --decision "Using JWT tokens"   # Log decisions
td handoff <id> --done "..." --remaining "..."  # Capture state
td review <id>                         # Submit for review

Multi-Issue Workflow (Recommended for Agents)

For agents handling related issues:

td ws start "Auth implementation"      # Start work session
td ws tag td-a1b2 td-c3d4             # Associate issues (auto-starts them)
td ws tag --no-start td-e5f6          # Associate without starting
td ws log "Shared token storage"       # Log to all tagged issues
td ws handoff                          # Capture state, end session

Key Workflows

Workflow 1: Starting New Work

# 1. Check what to work on
td usage          # See current state
td next           # Highest priority open issue
td critical-path  # What unblocks most work

# 2. Start work
td start <id>

# 3. Begin logging
td log "Started implementation"

Workflow 2: Handing Off Work

This is critical for agent-to-agent handoffs:

td handoff <id> \
  --done "OAuth flow, token storage" \
  --remaining "Refresh token rotation, error handling" \
  --decision "Using JWT for stateless auth" \
  --uncertain "Should tokens expire on password change?"

Keys:

• --done - What's actually complete (be honest)
• --remaining - What's left (be specific)
• --decision - Why you chose approach X
• --uncertain - What you're unsure about

Next session will see all this context with td usage or td context <id>.

Workflow 3: Reviewing Code

# 1. See reviewable issues
td reviewable

# 2. Check details
td show <id>
td context <id>

# 3. Approve or reject
td approve <id>
# Or:
td reject <id> --reason "Missing error handling"

Important: You cannot approve work you implemented. Session isolation enforces this.

Workflow 4: Handling Blockers

# 1. Log the blocker
td log --blocker "Waiting on API spec from backend team"

# 2. Work on something else
td next              # Get another issue
td ws tag td-e5f6   # Add to work session

# 3. Come back to blocked issue later
td context td-a1b2  # Refresh context when blocker resolves

Commands by Category

Checking Status

• td usage - Current state, reviews, next steps
• td usage -q - Compact view (after first read)
• td current - What you're working on
• td ws current - Current work session state
• td next - Highest priority open
• td critical-path - What unblocks most work

Working on Issues

• td start <id> - Begin work
• td unstart <id> - Revert to open (undo accidental start)
• td log "msg" - Track progress
• td log --decision "..." - Log decision
• td log --blocker "..." - Log blocker
• td show <id> - View details
• td context <id> - Full context for resuming

Handing Off

• td handoff <id> --done "..." --remaining "..." - Single issue
• td ws handoff - Multi-issue work session

Reviews

• td review <id> - Submit for review
• td reviewable - Issues you can review
• td approve <id> - Approve (different session only)
• td reject <id> --reason "..." - Reject

Creating/Managing Issues

• td create "title" --type feature --priority P1 - Create
• td list - List all
• td list --status in_progress - Filter by status
• td block <id> - Mark as blocked
• td delete <id> - Delete

File Tracking

• td link <id> <files...> - Track files with issue
• td files <id> - Show file changes

Other

• td monitor - Live dashboard
• td session --new "name" - Force new session
• td undo - Undo last action

See quick_reference.md for full command listing.

Resources

quick_reference.md

Complete command reference organized by task type.

ai_agent_workflows.md

Detailed workflows for common AI agent scenarios:

• Single-issue focus
• Multi-issue work sessions
• Handling blockers
• Resuming work
• Code review process
• Tips for AI agents

Issue Lifecycle

open → in_progress → in_review → closed
         |              |
         v              | (reject)
     blocked -----------+

Key Principles

Session Isolation: Every terminal/context gets a unique session ID. The session that implements code cannot approve it. Different session must review. This forces actual handoffs and prevents "works on my context" bugs.

Structured Handoffs: Don't just say "here's what I did"—structure it with done/remaining/decisions/uncertain so next agent has clear context.

Minimal: Does one thing. Single binary, SQLite local storage (.todos/), no server, works with any AI tool.

For AI Agents

Always start conversation with:

td usage --new-session

This auto-rotates sessions and gives you current state. Then:

1. Single focused issue → Use single-issue workflow
2. Multiple related issues → Use td ws start for work sessions
3. Before context ends → Always td handoff or td ws handoff
4. Log decisions → Use --decision flag to explain reasoning
5. Log uncertainty → Use --uncertain flag to mark unknowns
6. Track files → Use td link so future sessions know what changed

See ai_agent_workflows.md for detailed examples.