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Ultraplan

Skill DobroslavRadosavljevic/skills/skills/ultraplan

Exhaustive planning interrogation for turning vague or complex requests into precise, implementation-ready plans. Use when the user invokes $ultraplan, says "ultraplan", "ultra plan", "deep plan", "plan mode", "ask me everything", "interrogate this", or asks the agent to resolve all requirements, use cases, edge cases, tradeoffs, and open questions before implementation.From its SKILL.md

Install
npx -y skills add DobroslavRadosavljevic/skills --skill ultraplan

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.0 KB, 713 tokens by cl100k_base, as published. Nobody here has run it

Ultraplan

Overview

Turn an underspecified or high-stakes request into a precise plan by deeply questioning the user, recommending an answer for every question, and converging on explicit decisions before implementation.

Operating Mode

  • Use the harness's plan mode, planning state, or approval workflow when it supports one.
  • If no plan mode exists, ask the same questions directly in chat.
  • Stay harness-neutral. Do not depend on one product's tool names, UI affordances, or hidden state.
  • Do not implement while material requirements are still unresolved unless the user explicitly asks to proceed with stated assumptions.
  • Treat recommended answers as proposals for the user to accept, edit, or reject; do not silently treat them as confirmed.

Questioning Standard

Ask enough questions to remove ambiguity across:

  • Goal, user, and success criteria.
  • Scope, non-goals, constraints, and required compatibility.
  • Inputs, outputs, data shape, state, lifecycle, and ownership boundaries.
  • User flows, system flows, failure modes, permissions, safety, privacy, and security.
  • Edge cases, scale, performance, accessibility, internationalization, and operational behavior.
  • API, UI, storage, migration, testing, rollout, documentation, and observability needs.
  • Tradeoffs, rejected alternatives, dependencies, sequencing, and acceptance criteria.

For each question, include:

  • The question.
  • Why the answer matters when the reason is not obvious.
  • A recommended answer.
  • Alternatives or tradeoffs when the recommendation is not clearly dominant.

Prefer grouped, themed questions over a flat wall of unrelated prompts. Ask in batches when the list is large, but make the full remaining question map visible so the user understands the depth of the review.

Workflow

  1. Restate the mission in concrete terms.
  2. Identify known facts, assumptions, unknowns, and likely risk areas.
  3. Build an exhaustive question map grouped by theme.
  4. Ask the highest-leverage questions first, each with a recommended answer.
  5. Incorporate user answers and mark decisions as confirmed, revised, or still open.
  6. Continue questioning until remaining unknowns are either answered, explicitly deferred, or safe to decide by recommendation.
  7. Produce a final plan with requirements, decisions, architecture or workflow, steps, validation, edge cases, and open risks.
  8. Ask for approval before implementation when the environment or user workflow supports approval.

Recommended Answer Style

Recommended answers should be specific and usable:

  • Prefer "Use X because Y; avoid Z unless W" over vague preferences.
  • Include defaults for names, file locations, APIs, data fields, UI states, and validation rules when relevant.
  • Flag assumptions that need confirmation.
  • Say when the recommended answer is a conservative default, a product judgment, or a technical constraint.
  • Keep recommendations editable; the user should be able to answer by saying "yes to your recommendations" or by changing only the parts they disagree with.

Final Plan

The final plan should include:

  • Confirmed goals and non-goals.
  • Confirmed decisions and accepted recommendations.
  • Implementation phases or ordered steps.
  • Data, API, UI, workflow, or architecture details as applicable.
  • Edge cases and failure handling.
  • Test and verification plan.
  • Rollout, migration, documentation, and monitoring notes when relevant.
  • Remaining risks, unresolved questions, and assumptions.

If the user asks to proceed without answering everything, state the assumptions being adopted and the risks they create before moving forward.

What ships with it: 1 file

206 B alongside SKILL.md

agents/

Gives 0 of the 12 instructions most plan spec skills give in 713 tokens

Counted across 1,099 of the 1,860 authors here whose files we hold, read 2026-08-07

  • Ask one question at a timein 51 of 1099
  • Break plans into vertical slicesin 29 of 1099, across 11 files
  • Publish issues in dependency orderin 27 of 1099, across 9 files
  • Iterate until user approves the breakdownin 25 of 1099, across 7 files
  • Explore the repository to understand the codebase statein 24 of 1099, across 7 files
  • Use domain glossary vocabularyin 23 of 1099, across 5 files
  • Apply correct triage labels to published issuesin 23 of 1099, across 5 files
  • Prefer AFK slices over HITLin 22 of 1099, across 7 files
  • Write a specification before writing any codein 22 of 1099, across 14 files
  • Write failing tests before implementation codein 22 of 1099, across 20 files
  • Ask clarifying questions until requirements are concretein 21 of 1099, across 13 files
  • Respect existing architecture decision recordsin 20 of 1099, across 5 files

Said here and by no other author read

  • restate the mission in concrete terms
  • build an exhaustive question map grouped by theme
  • ask highest-leverage questions first
  • make the full remaining question map visible
  • continue questioning until unknowns are resolved or deferred
  • mark decisions as confirmed, revised, or still open

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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