startup_skill

This skill orchestrates startup work by spawning specialized agents and prioritizing stage-appropriate actions for rapid learning or scaling.
  • Python

2.5k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill startup

  • _meta.json271 B
  • SKILL.md2.2 KB

Overview

This skill orchestrates startup work by spawning specialized agents and applying stage-appropriate priorities. It routes decisions to the right role, enforces resource-aware tradeoffs, and flags common startup traps. The skill is practical and focused on accelerating learning pre-product-market-fit and scaling efficiently post-PMF.

How this skill works

When asked for help, the skill spawns domain-specific agents (product, engineering, design, marketing, finance, recruiting, legal, sales) and runs them in parallel when needed. It first identifies the startup stage and applies different priorities: rapid validation for pre-PMF, efficient growth for post-PMF. Decisions are routed based on reversibility, cross-functionality, and ownership, with analysts spawned for irreversible choices and syntheses produced for cross-functional tradeoffs.

When to use it

  • You need a coordinated plan across product, engineering, design, and growth.
  • You’re unsure what to prioritize given limited founder time and budget.
  • You must validate product-market fit quickly and cheaply.
  • You’re preparing to scale after proving retention and demand.
  • You want to avoid common mistakes like hiring too early or automating prematurely.

Best practices

  • Always determine the startup stage first; never apply post-PMF tactics to pre-PMF problems.
  • Prioritize learning speed pre-PMF: focus on retention, engagement, and organic signals.
  • Favor reversible, fast decisions where possible; model irreversible ones with an analyst agent.
  • Consider founder time as a primary resource constraint alongside money.
  • Default to manual-first, scrappy solutions and automate only when processes are stable.

Example use cases

  • Run a coordinated launch: spawn product, design, engineering, and marketing agents to execute and synthesize tasks.
  • Validate a new feature: prioritize experiments that answer retention and user delight questions quickly.
  • Decide on hiring: evaluate whether the founder is overwhelmed and if the role should be filled now or deferred.
  • Prepare to scale after PMF: spawn growth and operations agents to design repeatable acquisition and onboarding funnels.
  • Evaluate a strategic partnership or contract: spawn legal, sales, and finance agents and run scenario models for irreversible commitments.

FAQ

Ask about user behavior, repeat usage, referral signals, and revenue patterns; if retention and organic demand are not proven, treat as pre-PMF.

What if multiple decisions are urgent?

Spawn parallel agents for each domain, prioritize reversible quick wins, and synthesize output to resolve cross-functional conflicts.

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startup skill by openclaw/skills | VeilStrat