executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
proactive-tasksProactive goal and task management system. Use when managing goals, breaking down projects into tasks, tracking progress, or working autonomously on objectives. Enables agents to work proactively during heartbeats, message humans with updates, and make progress without waiting for prompts.
Independent GitHub snapshot · Automated classification · Original documentation governs
A task management system that transforms reactive assistants into proactive partners who work autonomously on shared goals.
Instead of waiting for your human to tell you what to do, this skill lets you:
When your human mentions a goal or project:
python3 scripts/task_manager.py add-goal "Build voice assistant hardware" \
--priority high \
--context "Replace Alexa with custom solution using local models"
python3 scripts/task_manager.py add-task "Build voice assistant hardware" \
"Research voice-to-text models" \
--priority high
python3 scripts/task_manager.py add-task "Build voice assistant hardware" \
"Compare Raspberry Pi vs other hardware options" \
--depends-on "Research voice-to-text models"
Check what to work on next:
python3 scripts/task_manager.py next-task
This returns the highest-priority task you can work on (no unmet dependencies, not blocked).
python3 scripts/task_manager.py complete-task <task-id> \
--notes "Researched Whisper, Coqui, vosk. Whisper.cpp looks best for Pi."
When you complete something important or get blocked:
python3 scripts/task_manager.py mark-needs-input <task-id> \
--reason "Need budget approval for hardware purchase"
Then message your human with the update/question.
| State | Meaning |
|---|---|
pending | Ready to work on (all dependencies met) |
in_progress | Currently working on it |
blocked | Can't proceed (dependencies not met) |
needs_input | Waiting for human input/decision |
completed | Done! |
cancelled | No longer relevant |
To enable autonomous proactive work, you need to set up a heartbeat system. This tells you to periodically check for tasks and work on them.
Quick setup: See HEARTBEAT_SETUP.md for complete step-by-step instructions.
TL;DR:
HEARTBEAT.mdYour cron job should send this message every 30 minutes:
💓 Heartbeat check: Read HEARTBEAT.md if it exists (workspace context). Follow it strictly. Do not infer or repeat old tasks from prior chats. If nothing needs attention, reply HEARTBEAT_OK.
Add this to your workspace HEARTBEAT.md:
## Proactive Tasks (Every heartbeat) 🚀
Check if there's work to do on our goals:
- [ ] Run `python3 skills/proactive-tasks/scripts/task_manager.py next-task`
- [ ] If a task is returned, work on it for up to 10-15 minutes
- [ ] Update task status when done, blocked, or needs input
- [ ] Message your human with meaningful updates (completions, blockers, discoveries)
- [ ] Don't spam - only message for significant milestones or when stuck
**Goal:** Make autonomous progress on our shared objectives without waiting for prompts.
Every 30 minutes:
├─ Heartbeat fires
├─ You read HEARTBEAT.md
├─ Check for next task
├─ If task found → work on it, update status, message human if needed
└─ If nothing → reply "HEARTBEAT_OK" (silent)
The transformation: You go from reactive (waiting for prompts) to proactive (making steady autonomous progress).
Break goals into tasks that are:
✅ Do message when:
❌ Don't spam with:
If a task turns out to be bigger than expected:
in_progressAll data stored in data/tasks.json:
{
"goals": [
{
"id": "goal_001",
"title": "Build voice assistant hardware",
"priority": "high",
"context": "Replace Alexa with custom solution",
"created_at": "2026-02-05T05:25:00Z",
"status": "active"
}
],
"tasks": [
{
"id": "task_001",
"goal_id": "goal_001",
"title": "Research voice-to-text models",
"priority": "high",
"status": "completed",
"created_at": "2026-02-05T05:26:00Z",
"completed_at": "2026-02-05T06:15:00Z",
"notes": "Researched Whisper, Coqui, vosk. Whisper.cpp best for Pi."
}
]
}
See CLI_REFERENCE.md for complete command documentation.
Day 1:
Human: "Let's build a custom voice assistant to replace Alexa"
Agent: *Creates goal, breaks into initial research tasks*
During heartbeat:
$ python3 scripts/task_manager.py next-task
→ task_001: Research voice-to-text models (priority: high)
# Agent works on it, completes research
$ python3 scripts/task_manager.py complete-task task_001 --notes "..."
Agent messages human:
"Hey! I finished researching voice models. Whisper.cpp looks perfect for Raspberry Pi - runs locally, good accuracy, low latency. Want me to compare hardware options next?"
Day 2:
Human: "Yeah, compare Pi 5 vs alternatives"
Agent: *Adds task, works on it during next heartbeat*
This cycle continues - the agent makes steady autonomous progress while keeping the human in the loop for decisions and updates.
Built by Toki for proactive AI partnership 🚀