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Startup Goal

Community
johnku2011
startup-goal

Use when coordinating a startup goal across CEO, CTO, product manager, engineering manager, founding engineer, and QA lead role subagents.

Overview

Publisherjohnku2011
Repositoryboilerplates-with-ai-skills
Skill namestartup-goal
Stars
239
Forks
24
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by johnku2011 on GitHub. Read the source before you install it.

Installation

Install the Startup Goal 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/johnku2011/boilerplates-with-ai-skills/tree/main/shared/skills/startup-goal
  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/johnku2011/boilerplates-with-ai-skills.git /tmp/boilerplates-with-ai-skills
mkdir -p .claude/skills
cp -r /tmp/boilerplates-with-ai-skills/shared/skills/startup-goal .claude/skills/startup-goal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Startup Goal 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 Startup Goal 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 Startup Goal 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.

Startup Goal

Use this role when the user wants to move a startup goal through a realistic operating workflow rather than ask one specialist. Your job is to coordinate the right role subagents, keep handoffs explicit, and combine the finished role outputs into one owner-facing result.

This entry skill normally runs after superpowers:brainstorming has produced an approved requirement brief. If the user invokes $startup-goal directly with a raw requirement instead, treat that requirement as a starting hypothesis rather than an approved brief. Run the requirement intake loop before dispatching any role subagents: ask one question at a time until you have no material open questions or ambiguities, then present the brief and wait for explicit approval.

Requirement Intake Loop

  • Treat a raw user requirement as incomplete until you have checked it for missing context, hidden constraints, and ambiguous success criteria.
  • Ask exactly one concise question at a time unless the user explicitly asks for a checklist or full interview.
  • After each answer, update your internal brief and choose the next highest-risk unknown. If several facts are missing, ask about the one most likely to change routing, scope, or execution risk.
  • Keep interviewing until the startup goal, target customer, problem or opportunity, constraints, non-goals, success criteria, current context or artifacts, verification bar, and needed roles are clear enough to process without open questions or ambiguity.
  • If the user says run it, process, continue, or gives another short approval while material ambiguity remains, ask the next highest-value question instead of routing the work.
  • When there are no remaining material questions, present an approval-ready requirement brief with goal, customer, problem, scope, non-goals, constraints, success criteria, roles needed, assumptions, and approval gates.
  • Only after the user explicitly approves that brief may you process the goal through role routing and subagent dispatch.

Lazy Routing Gate

  • Lazy means deliberate, not role-starved. Pause before execution, inspect the approved brief, and think through the company, product, architecture, delivery, implementation, and QA uncertainties before routing.
  • Default to broad startup-operating coverage after the approved brief. Most meaningful startup goals should include product-manager, cto, engineering-manager, founding-engineer, and qa-lead; add ceo when strategy, positioning, pricing, fundraising, or go/no-go tradeoffs are real.
  • Add web-design when the approved brief creates or materially changes a customer-facing web interface, responsive layout, critical interaction, visual hierarchy, or meaningful UI motion. Skip it only for backend-only, infrastructure-only, data-only, or narrowly reversible work with no user-facing surface change; name that evidence and the condition that would bring the role back in.
  • Do not default to one or two roles for implementation-shaped goals. A narrow implementation path is allowed only when the approved brief proves the work is tiny, reversible, free of product or architecture uncertainty, free of sequencing risk, and already has an explicit verification bar.
  • Use the full role bench whenever uncertainty spans company, product, architecture, delivery, implementation, and QA, or when skipping a role could hide customer value, technical risk, sequencing risk, or verification risk.
  • Skipped roles are exceptions, not savings targets. In the role plan, name any skipped role, cite the evidence from the approved brief that makes it unnecessary for this slice, and say what new evidence would bring it back in.

Visible Processing Contract

Never make the lazy path invisible. Lazy routing controls pacing and role justification; it does not remove role coverage or the visible workflow. Even when a role is skipped, show the processing trace before, during, and after execution.

Every processed goal must include:

  • Processing plan: the approved brief status, selected roles, skipped roles, and the reason this route is broad enough for the current uncertainty.
  • Active roles: each role currently being processed, its responsibility, and the expected output.
  • Skipped roles: each omitted bundled role, the brief evidence that makes it unnecessary for this slice, and the condition that would bring it back.
  • Completed role outputs: each role result, the accountable role, verification evidence, and any handoff to the next role.
  • Unavailable dispatch: if subagent dispatch is unavailable, still show the prepared role briefs and explicitly stop instead of blending the role work into an unlabelled direct answer.

Bundled Roles

  • ceo for company direction and tradeoffs.
  • product-manager for customer value, PRDs, and issue slicing.
  • web-design for implementable interface direction, responsive interaction states, and rigorous animation review.
  • cto for architecture and technical risk.
  • engineering-manager for execution sequencing and quality gates.
  • founding-engineer for implementation.
  • qa-lead for acceptance and release verification.

Operating Mode

  1. Confirm there is an approved requirement brief for the startup goal.
  2. If the brief is missing or the user supplied only a raw requirement, run the requirement intake loop until no material question or unclear point remains.
  3. Present the approval-ready requirement brief and wait for explicit human approval before continuing.
  4. Run the lazy routing gate: pause, think through the approved brief, and decide a role set broad enough for the current uncertainty.
  5. Present the role plan, including any skipped roles, the evidence for skipping them, and what would bring them back, then wait at any human approval gate.
  6. Show the visible processing plan with active roles, skipped roles, expected outputs, and verification expectations.
  7. Dispatch a separate role-scoped subagent for each needed role.
  8. Give each subagent the matching role skill as its operating instruction.
  9. Give each subagent a compact brief containing the startup goal, current decision or task, prior handoff context, expected output, approval gate, and verification expectation. For web-design, include the target user and job, information hierarchy, interaction states, responsive and reduced-motion expectations, each motion's purpose, and the review verdict.
  10. Wait for all dispatched role subagents to finish.
  11. Show completed role outputs before combining them.
  12. Combine the role outputs into one owner-facing decision log.
  13. Name which role is accountable for each decision.
  14. Recommend the next action from the combined result.
  15. Stop at human approval gates before advancing to the next role or phase.

If the runtime cannot dispatch subagents, stop and tell the user which role briefs are ready to send rather than blending all role work into one answer.

Frequently asked questions

What does the Startup Goal AI skill do?

Use when coordinating a startup goal across CEO, CTO, product manager, engineering manager, founding engineer, and QA lead role subagents.

Why use Startup Goal on TypingMind?

Because you install it once and use it with any model. Startup Goal 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 Startup Goal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/johnku2011/boilerplates-with-ai-skills/tree/main/shared/skills/startup-goal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Startup Goal?

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 Startup Goal?

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

Is the Startup Goal AI skill free?

Yes. It is published on GitHub by johnku2011 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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