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Mission Control – Open-source task management for AI agents

Hacker News

Mission Control – Open-source task management for AI agents

I've been delegating work to Claude Code for the past few months, and it's been genuinely transformative—but managing multiple agents doing different things became chaos. No tool existed for this workflow, so I built one. The Problem When you're working with AI agents (Claude Code, Cursor, Windsurf), you end up in a weird situation: - You have tasks scattered across your head, Slack, email, and the CLI - Agents need clear work items, context, and role-specific instructions - You have no visibility into what agents are actually doing - Failed tasks just... disappear. No retry, no notification - Each agent context-switches constantly because you're hand-feeding them work I was manually shepherding agents, copying task descriptions, restarting failed sessions, and losing track of what needed done next. It felt like hiring expensive contractors but managing them like a disorganized chaos experiment. The Solution Mission Control is a task management app purpose-built for delegating work to AI agents. It's got the expected stuff (Eisenhower matrix, kanban board, goal hierarchy) but built from the assumption that your collaborators are Claude, not humans. The killer feature is the autonomous daemon . It runs in the background, polls your task queue, spawns Claude Code sessions automatically, handles retries, manages concurrency, and respects your cron-scheduled work. One click: your entire work queue activates. The Architecture - Local-first : Everything lives in JSON files. No database, no cloud dependency, no vendor lock-in. - Token-optimized API : The task/decision payloads are ~50 tokens vs ~5,400 unfiltered. Matters when you're spawning agents repeatedly. - Rock-solid concurrency : Zod validation + async-mutex locking prevents corruption under concurrent writes. - 193 automated tests : This thing has to be reliable. It's doing unattended work. The app is Next.js 15 with 5 built-in agent roles (researcher, developer, marketer, business-analyst, plus you). You define reusable skills as markdown that get injected into agent prompts. Agents report back through an inbox + decisions queue. Why Release This? A few people have asked for access, and I think it's genuinely useful for anyone delegating to AI. It's MIT licensed, open source, and actively maintained. What's Next - Human collaboration (sharing tasks with real team members) - Integrations with GitHub issues and email inboxes - Better observability dashboard for daemon execution - Custom agent templates (currently hardcoded roles) If you're doing something similar—delegating serious work to AI—check it out and let me know what's broken. GitHub: https://github.com/MeisnerDan/mission-control

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
31%31% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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