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Os Whats Next

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kharmanskyi
os-whats-next

ALWAYS invoke this skill when the user asks what to do next, what is left, or what is blocked - "what's next", "what now", "what should we work on", "anything I can do" - in any language. This skill picks the next piece of work; when the user asks HOW to do a thing or says they do not understand what to do, that is os-step-by-step. Reads the last report, local changes, open pull requests and always the backlog. First finishes what is finished: verified-ready pull requests merge in the same pass. Then sorts the rest into doable-alone and needs-you, ending with one recommended next task in plain words - what it closes or unblocks. Never invents tasks.

Overview

Publisherkharmanskyi
Repositoryopen-steps
Skill nameos-whats-next
Stars
1.2K
Forks
106
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kharmanskyi on GitHub. Read the source before you install it.

Installation

Install the Os Whats Next AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kharmanskyi/open-steps/tree/main/skills/os-whats-next
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kharmanskyi/open-steps.git /tmp/open-steps
mkdir -p .claude/skills
cp -r /tmp/open-steps/skills/os-whats-next .claude/skills/os-whats-next
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Os Whats Next in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Os Whats Next on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Os Whats Next is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

os-whats-next

Answer "what do we do now" with the next move, not a map. The user does not need the dependency graph - they need what got finished, what to take next, and why, in plain words. This skill decides; os-step-by-step walks the user through their part; os-done-or-not reports what came of it.

Language

Write in the language the user speaks in this session, detected from the conversation. Commands, file names and identifiers stay English.

When to use

Triggers: the description above, plus a session just ended wanting a next move.

Step 1 - read the state, quickest first

Stop as soon as you can answer.

  1. The last report - ~/.claude/open-steps/reports/<project>/latest.md: a handover written for exactly this moment.

  2. Local state - uncommitted changes, unpushed commits, current branch.

  3. Open pull requests - one call: gh pr list --json number,title,mergeStateStatus,reviewDecision,isDraft.

  4. The backlog - always. The issue tracker when one is already connected (never authenticate or install one), otherwise task files in the repo: BIG-PICTURE.md, PLAN.md, TODO.md, docs/plan*. Next work comes from the backlog, not from imagination. No backlog anywhere → say so.

    Where os-big-picture keeps that file, only the "What is next" section is the backlog. The feature table above it is an inventory, and a row that has not changed in months is a finished feature, not a task. Reading work out of it is inventing work, which rule 4 below forbids. A row under "Worth retiring" is a real candidate, but it is a decision to put to the user, never a task to start.

    That file carries its own age: a measured date at the top and a date beside every stage. Never take a number out of it. Take only the "What is next" list, the "Worth retiring" rows, and each Stage with the date beside it. Before you put a "Worth retiring" row to the user, measure it again. From the project root, run bash <this skill's folder>/../os-big-picture/scripts/census.sh .. The . is the project, not the skill folder. If the script is missing or does not run, say so and call that row "not checked". When the stage dates are months behind the newest commit, say how old they are. Dates that stopped moving mean the reports stopped, not that the work did.

Say which sources you did not read: an unread source is not an empty source.

Step 2 - finish what is finished

A pull request with green checks and an approval is not a decision - it is unfinished business. Verify it through os-check-work's accept rules and merge it in this same pass. Two things stop the merge: a failed claim, and a task instruction that merges happen on command only - an orchestrator may own the merge. Report it as done, never as a question.

Step 3 - sort what remains into two lists

ListBelongs there when
I can do this aloneeverything needed is at hand: no decision, no secret, no approval, no device
Needs youa decision, an approval, a secret, a purchase, or a device only the user has

Blocked work gets no section of its own. Fold it into the reasoning, in plain words - "X waits on an outside check; I watch it" - the user trusts the recommendation, not the graph.

The shape - ten lines, like every report in this pack

<Lead: one sentence on where things stand - including what this pass merged.>

**I can do alone:** <up to three items, five words of why each>
**Needs you:** <up to three items, one line each - or drop the list>

**Next I take: <the one task> - <plain words: what it closes or unblocks>.**
<One line: what was not checked.>

When a quick small win and a big item are both real candidates, offer the choice through your tool's question picker where it has one (in Claude Code, AskUserQuestion). Give two to four options, the recommended one first and marked. Where there is no picker, write the same question and options as plain text, the recommended one first and marked. On the pick, prepare the launch: a prompt complete enough to paste or a command complete enough to run, and one line saying what comes out. Never run it yourself.

How many at once

Before offering to start several, prove they will not collide - all three:

CheckThey collide when
Same filesboth touch the same files, module, or migration sequence
Same shared resourceone working copy, branch, database, container project, port
One feeds the otherthe second needs the first one's output

Any check failing → one at a time, saying which failed. All passing → say so. Never claim parallel safety you did not verify - "I did not check" is honest; a collision discovered mid-run is not. Where the project isolates parallel work - a working copy per task, separate container projects or ports - name that as the precondition instead of assuming it.

Hard rules

  1. Three items per list, maximum - more → say how many were left out and on what basis you chose.
  2. Every item names its source - the report, a pull request, a backlog entry, a failing check. Your own idea is marked a suggestion, and lists are never padded: two real items beat five with filler.
  3. One recommendation, always - even when offering the small-versus-big choice, one option carries the mark and one line of plain-words reasoning.
  4. Finish, then prepare - never start. Merging a verified-ready pull request is finishing. New work is prepared as a ready-to-run launch and waits for the pick.
  5. Say what you did not check - especially the backlog. Silence reads as "nothing there".
  6. Plain words - no engineering identifiers except where they name an action.

Known gotchas

  • Deferred-until-Monday is not a task on Saturday: do not re-propose it early.
  • A stale tracker is worse than none - say when you read it. The same is true of the map, and it hides it better: its measured columns refresh themselves while the stages behind them age.
  • A draft pull request is unfinished work, not a merge to put to the user. If the session that opened it has ended, the draft goes in the I-can-do-alone list. If another session still works on it, leave it to that session and say so.
  • A quiet feature in the map is not a task. It is quiet because it is finished; the skill that wrote it already checked that something still uses it.
  • A needs-you pick goes to os-step-by-step, never explained inline.
  • "Ready to merge" is still a claim: the verify step is what makes it true - skipping it to move faster is how wrong work lands.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Os Whats Next AI skill do?

ALWAYS invoke this skill when the user asks what to do next, what is left, or what is blocked - "what's next", "what now", "what should we work on", "anything I can do" - in any language. This skill picks the next piece of work; when the user asks HOW to do a thing or says they do not understand what to do, that is os-step-by-step. Reads the last report, local changes, open pull requests and always the backlog. First finishes what is finished: verified-ready pull requests merge in the same pass. Then sorts the rest into doable-alone and needs-you, ending with one recommended next task in pl...

Why use Os Whats Next on TypingMind?

Because you install it once and use it with any model. Os Whats Next is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Os Whats Next in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kharmanskyi/open-steps/tree/main/skills/os-whats-next. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Os Whats Next?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Os Whats Next?

As many as you like. As long as a model supports skills, you can use Os Whats Next with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Os Whats Next AI skill free?

Yes. It is published on GitHub by kharmanskyi under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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