startup-skill: Four Claude Skills That Run Startup Validation as a Structured Process
AI agent skills for startup validation, competitive intelligence, and planning
At a glance
- What is it?
- startup-skill packages market research, competitive battle cards, Dunford positioning, and pitch construction as installable agent skills for Claude Code and compatible agents. The value is the enforced process and the interview gate, not the research itself, which still depends on whatever sources the agent can reach.
- Who is it for?
- Adopt startup-skill if you already work inside Claude Code or a skill-compatible agent and want a repeatable structure for early validation, competitor battle cards, and pitch drafts, and if you can absorb the token cost of multi-agent research runs. Do not adopt it as a substitute for primary customer research; the customer interview gate exists precisely because the skill cannot replace it.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 77 days ago.
- What is it written in?
- GitHub does not report a main language for this repository.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What startup-skill actually installs into your agent
This is not an application you run. It is a set of four skill definitions that Claude Code and other skill-compatible agents load on demand: startup-design, startup-competitors, startup-positioning, and startup-pitch. The README frames the output as what a strategy consultant would deliver, listing market research, battle cards, positioning, financial projections, and a 30-day action plan. The claim that matters more is the second half of that sentence: if your idea should die, it will tell you. That is a positioning choice, and it shapes the design. The startup-design skill runs a pre-flight check and eight phases, and it gates progress on customer interviews rather than letting the model generate synthetic customer sentiment and move on. For a solo founder or a small team that has never written a positioning document, the structure is the product. You are buying a sequence of prompts with defined deliverables, not a data source.
The four skills and the division of labour between them
Each skill covers a distinct slice. startup-design is the full path: market research, customer discovery, competitive analysis, brand, product definition, financial projections, and validation experiments, described as 30+ structured deliverables across 8 phases plus a pre-flight check and a customer interview gate. It also has a fast track mode for a compressed go/no-go when you ask for quick validation. startup-competitors produces battle cards for each competitor, a pricing landscape, a feature matrix, and a strategic report, and the README says it profiles 5-8+ competitors across 3 research waves, drawing on reviews, forums, web data, and social or community source packets when you provide them. startup-positioning applies April Dunford's framework, and the README names the components as Dunford's 5+1, with outputs being a positioning doc, a competitive alternatives map, a market category analysis, and messaging implications. startup-pitch produces narratives in six formats (10-minute, 5-minute, 2-minute, 1-minute elevator, email, and a slide-by-slide deck outline) plus a scoring rubric, Q&A prep, and investor roleplay. The split is sensible: competitors and positioning share inputs but produce different artifacts, and pitch depends on both.
How a run is triggered and what the agent does with your prompt
There is no CLI to learn for normal use. You describe the problem in natural language and the skill triggers automatically. The README gives worked examples: asking whether a SaaS for real estate agents that automates follow-up emails is worth building routes to startup-design and runs the full process; asking who your competitors are in project management for creative agencies routes to startup-competitors; asking for a quick validation routes to startup-design in fast track mode; asking how to position against Asana and Monday routes to startup-positioning; and describing a pre-seed raise with customer metrics routes to startup-pitch. You can also invoke directly with slash commands: /startup:startup-design, /startup:startup-competitors, /startup:startup-positioning, /startup:startup-pitch. The mechanism behind the trigger is the agent matching your intent against each skill's description, which is why the README's examples are phrased as questions rather than commands. If a skill does not fire, the fallback is the explicit slash command.
Installation commands and the update path
The recommended route is the Claude Code plugin marketplace. Two commands: claude plugin marketplace add ferdinandobons/startup-skill, then claude plugin install startup@startup-skill. For the Claude.ai web app, you download .skill files from the Releases page and upload them under Settings, then Skills. Other hosts have their own routes. The CLI install is npx skills add ferdinandobons/startup-skill. SkillKit, which the README says works with Claude Code, Cursor, and Copilot among others, uses npx skillkit install ferdinandobons/startup-skill. You can also clone the repository and copy the four skill directories into .agents/skills/, or add it as a git submodule at .agents/startup-skill. Updating differs by route. The plugin updates with claude plugin update startup@startup-skill, and the README notes that if new changes do not appear, you uninstall, remove the marketplace, re-add it, and reinstall. The CLI and SkillKit routes overwrite on re-run. Submodules use git submodule update --remote. Claude.ai users re-download and re-upload. That is four distinct update procedures for one project, which is a maintenance surface worth counting before you pick a route.
Token consumption is the real cost of a full run
The README states plainly that these skills run multiple research agents and can consume a large number of tokens, and recommends Claude Max 5x for the best experience. It also gives a recovery instruction: if a session hits the limit, ask Claude to resume from where you left off and it will pick up the process. Read that as the honest cost model. The software is MIT-licensed and free, so the expense is not a licence fee, it is the model usage a multi-wave research process burns through. A startup-competitors run profiling five to eight or more competitors across three research waves is not a single prompt. If you are on a metered plan, the per-run cost is unpredictable from the documentation alone, and the README does not publish token estimates per skill or per phase. That is the single biggest gap in the material: you cannot budget a run before starting one. The resume instruction mitigates interruption but not cost.
Where the output is only as good as what the agent can reach
startup-competitors builds battle cards from reviews, forums, web data, and social or community source packets when provided. That last clause is doing real work. The skill can research what is publicly reachable, and you can hand it source packets to widen the base, but it has no privileged access to competitor pricing pages behind a login, private Slack communities, or sales call recordings. Battle cards built on thin public data will read confidently and may be wrong on pricing tiers and packaging details, which are exactly the fields a sales team relies on. The startup-design customer interview gate is the same admission from the other direction: the process is designed to stop and require real conversations rather than let the model invent customer quotes. Treat every generated deliverable as a draft that needs source verification. The 30-day action plan and financial projections are the most exposed here, because projections built on unverified market assumptions compound errors rather than surfacing them.
How it differs from a general research assistant or a strategy template
The obvious alternative is asking the same model the same questions without the skill installed, or working from a static strategy template. The difference is enforcement. A plain prompt gives you whatever the model produces in one pass with no gate, no phase structure, and no defined deliverable list. startup-design imposes a pre-flight check, eight phases, and a customer interview gate before it proceeds, and names 30+ deliverables so you can tell what is missing. startup-positioning does not ask the model to invent a positioning method; it applies April Dunford's framework with a stated component count. That is a real distinction in reproducibility: two people running the skill on the same idea get the same structure, though not necessarily the same conclusions. The trade-off is rigidity. If your situation does not fit an eight-phase design process, a fast track go/no-go, or a Dunford positioning exercise, the skill's structure becomes overhead, and a direct conversation with the model may be faster. The other alternative is hiring the consultant the README benchmarks against, which buys human judgement and accountability that a skill definition cannot supply.
Version cadence, licence, and what to check before adopting
The project is MIT-licensed, which permits commercial use, modification, and redistribution provided the licence and copyright notice are preserved. That is the standard permissive arrangement and carries no copyleft obligation on your own work; it also means no warranty, and the README makes no accuracy guarantees about generated research. The release cadence is visible in the material: v1.8.0 added deep-reasoning cues for new models, v1.9.0 introduced the validation-first flow and pitch deck outline, and v1.10.0 landed about a month after v1.9.0. Three minor releases in roughly five weeks suggests active iteration, which cuts both ways: improvements arrive quickly and so do changes to phase structure and deliverable lists, so pin a version if you build internal process around specific outputs. Contributions are open through pull requests and issues. Before adopting, confirm your host supports the install route you choose, since the plugin, Claude.ai upload, CLI, SkillKit, and submodule paths are not interchangeable, and check whether your plan can absorb a full multi-agent run without hitting session limits mid-process.
Editorial conclusion
Adopt startup-skill if you already work inside Claude Code or a skill-compatible agent and want a repeatable structure for early validation, competitor battle cards, and pitch drafts, and if you can absorb the token cost of multi-agent research runs. Do not adopt it as a substitute for primary customer research; the customer interview gate exists precisely because the skill cannot replace it. Before committing, verify three things: that your agent host supports the skills format you install, that a full startup-design run fits inside your plan's session limits, and that the competitor profiles it generates cite sources you can actually open.
Community notes