For engineering teams
AI scheduling for engineers who live in GitHub, Slack, and Notion
Nityasha is the AI day planner for engineering teams. It protects deep work, plans around standups and reviews, and connects to GitHub, Slack, and Notion, while a shared context file keeps every engineer's agents answering from the same understanding.
Key takeaways
- Hold long blocks for deep work instead of filling them with shallow tasks.
- Rebuild the day automatically when a review, a standup, or an incident lands.
- Pull work in flight from GitHub, plus the context around it in Slack and Notion.
- Read one shared profile over MCP in Claude Code, Codex, Cursor, and other agents.
- Let a new engineer's agent start from the team's context, not a six-week ramp.
Engineering work does not fit an hourly grid
A productive engineering day is a few long blocks, not a dozen thirty-minute slots. The expensive part of the work is holding a whole system in your head, and every interruption costs more than the time it takes. A scheduler that fills every gap with a small task is working against the job.
Engineering days are also the least predictable. An incident, a review, or an unblocking question can take the afternoon. A rigid plan breaks the first time that happens, and after it breaks twice nobody trusts it again.
- Deep work needs blocks measured in hours, not half-hours.
- Interruptions cost more than their clock time, so protecting focus is the point.
- Incidents and reviews are unschedulable and must reflow the day when they hit.
- A plan you stop trusting is worse than no plan at all.
Plan the day, protect the blocks
Nityasha plans each engineer's day from the meetings and tasks actually on it, then holds the longest available gaps for the highest-priority work. Shallow tasks fill the short gaps around them instead of eating the focus block.
When a standup moves, a review lands, or an incident pulls an engineer away, the plan reflows the remaining work into the real gaps that are left. The focus block is protected on purpose, not by luck.
Connect the tools the work already lives in
Nityasha plugs into the places engineering work leaves a trail, so the plan reflects what is really in progress and the profile carries the context that used to live in someone's head.
- GitHub: pull requests, reviews, and issue activity, so work in flight is visible to the plan.
- Slack: the handoffs and decisions that do not belong in a ticket.
- Notion: specs, docs, and the conventions a team writes down once and forgets.
- MCP agents: Claude Code, Codex, Cursor, and any other MCP client read the same profile.
One shared context for the whole team
Engineering teams lose time to the same question asked from different angles: how does auth work here, why did we drop that service, what is the rule on migrations. The answers live with whoever has been around longest, and everyone else pays to re-derive them.
The team plan puts that context in one shared file that every member's agent reads before it answers. Architecture decisions, conventions, and current priorities stop varying by which teammate's tool you asked, and a new engineer's agent starts from the same understanding as a senior one.
Fewer status meetings, more shipped work
When priorities and progress live in a file every agent reads, the recurring sync stops being a relay of facts. It becomes a review of decisions, and the daily standup can shrink or go.
Because the same context feeds both the plan and the agents, what an engineer is scheduled to do and what their AI tools assume they are doing stay in agreement. That is the whole value: one understanding of the work, used everywhere.
Frequently asked questions
The best fit is a scheduler that understands how engineering work actually happens: long uninterrupted blocks, unpredictable reviews and incidents, and tools like GitHub, Slack, and Notion. Nityasha plans around that reality, protects deep work, and keeps each engineer's plan current when an incident or review interrupts it.
Nityasha reads the fixed meetings on your calendar, holds the remaining long gaps for the highest-priority work, and refuses to fill them with low-value tasks. When a meeting moves into a focus block, it rebuilds the day rather than letting the block quietly disappear.
Yes. Nityasha connects to GitHub for pull requests and issue activity, Slack for the conversations and handoffs around work, and Notion for the docs and specs behind it. Because every connected agent reads the same personal context, the plan reflects what is actually in progress.
MCP (Model Context Protocol) is the open standard that lets an AI agent read context from a source. Nityasha exposes your profile over MCP, so Claude Code, Codex, Cursor, and other agents read the same context before they answer. That is what keeps one engineer's tooling consistent with the rest of the team.
Yes. Each engineer keeps a personal profile, and the team plan adds one shared context file every member's agents read. Priorities, architecture decisions, and conventions live in one place, so answers stop varying by which teammate's agent you asked.
No. You describe the work and its priority; Nityasha finds where it fits around meetings and rebuilds the plan when the day shifts. Agents can propose context updates as they learn, and you approve what stays, so the schedule and the context stay current without manual upkeep.
Keep reading
The scheduling engine behind the plan: automatic rescheduling without rigid blocks.
Keep a team on the same priorities without a recurring status meeting.
Try the live demo and see the planner with sample data, no signup.
How the popular AI calendar tools compare for engineering work.