design-the-mva
Skill1 starsChanged 5 months agoApache-2.0View on GitHub
Use when a founder is about to build the first version and is piling features instead of proving one loop - "what's my MVP", "what should I build first", "scope this down", "let's add X too", "the demo works, ship it", "which feature first", "this is our biggest use case so start there", or they are scoping the highest-value highest-risk job because it impresses. Produces a Minimum Viable Agent: the one Goldilocks task (high-volume, guideline-based, low-risk), the closed loop it proves, the single high-risk hypothesis, and a bounded self-healing fallback. Skill before: a feature pile that demos. After: one experiment that can come back no.
In this repository27 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .cursor/rules/ai-native-os.mdcCursor rule
- agentic-build-loopSkill
- ai-assisted-salesSkill
- apply-ai-native-modelsSkill
- architect-before-codeSkill
- capture-learningSkill
- cognitive-architecture-reviewSkill
- compliance-readinessSkill
- customer-discovery-that-doesnt-lieSkill
- design-a-loopSkill
- eval-and-safety-harnessSkill
- frame-the-hypothesisSkill
- gateway-agent-opsSkill
- geo-contentSkill
- hypothesis-miningSkill
- map-the-terrainSkill
- measure-ai-native-pmfSkill
- moat-strategySkill
- pay-down-agentic-debtSkill
- red-team-the-agentSkill
- secure-the-connectorsSkill
- self-healing-fallbacksSkill
- share-of-model-auditSkill
- start-hereSkill
- write-the-claude-mdSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 34
Run measure-ai-native-pmf next, once the loop is live and the hypothesis has a clean yes or no.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Design the MVA
The cheapest thing in your company is now a feature. You describe one in a sentence and it works by lunch, which is exactly the trap dressed as a gift. So founders pile up panels and integrations and call it a product, while the one question that decides whether the company lives goes untested: will a real person let a machine own the job the whole thing depends on. Ship the smallest agent that proves that loop, not the feature pile that flatters you.
The method
Full method, the Goldilocks scoring filters, a worked MVA scope, anti-patterns, and a copyable
template: references/mva-method.md.
Step 1 - Find the Goldilocks task
Pick one task that is high-volume, guideline-based, and low-risk, all three at once. High-volume, so the loop runs often enough to save real time and throw off real signal. Guideline-based, so there is a knowable right answer a human can check at a glance. Low-risk, so a wrong answer is an annoyance and not a harm. Your highest-value task is usually your highest-risk one. Write that on the roadmap for later. Earn trust on the boring task next to it first.
Step 2 - Define the Minimum Viable Agent
The Minimum Viable Agent is the smallest agent that proves the intelligence loop closes: task in, agent acts, outcome out, human accepts or corrects, the system records the choice. The deliverable is not a screen. It is a working loop around one task, and it can be thin on the surface: a text box, an email reply, a row someone approves. The intelligence is the product.
Step 3 - Name the one high-risk hypothesis it tests
State the trust bet so it can come back no: users in role R, facing task T, will accept the agent's output without redoing it at least M percent of the time. One hypothesis, not a multi-feature dashboard. If a proposed build does not map to a bet that can fail, it is scope creep, however reasonable it sounds.
Step 4 - Add the self-healing fallback
Decide in one bounded sentence what the agent does when it is unsure or the model fails: retry a set number of times, switch to a backup, then drop to a fixed non-AI path and escalate to a named human. A pilot user who hits a dead end twice goes back to doing it by hand. You lose them on reliability, not capability.
Output
- The MVA spec: the one Goldilocks task, the loop it proves, the single high-risk hypothesis, and the bounded fallback.
- Run
measure-ai-native-pmfnext, once the loop is live and the hypothesis has a clean yes or no.
Constraints
- One task, not ten. The unit of work is an experiment, not a feature.
- Pick the Goldilocks zone, not the hardest problem. The first wrong answer on a high-risk job detonates the trust your whole roadmap needs.
- The MVA must prove the loop or it is just a feature. Run the Remove-the-AI test: take the model out, and if the loop still works, you scoped a feature.
- Stay theme-agnostic; the founder supplies the domain, you supply the rigour.
Dictionary
Minimum Viable Agent · the Goldilocks zone · self-healing fallback · the Remove-the-AI test
Copy-paste version
For non-coders: paste into any chatbot or no-code builder (Claude.ai, ChatGPT, Lovable, Bolt, v0).
Act as my AI-native product coach. My idea: [ONE_LINER]. Who it's for: [ICP]. Domain: [DOMAIN].
Help me write my Minimum Viable Agent in one sentence, in this exact shape:
"My first agent does [the single task] for [ICP], proving that [the loop], and when it fails it [the fallback]."
Rules:
1. The task must pass all three Goldilocks tests: high-volume (runs often), guideline-based (a
knowable right answer a human checks at a glance), and low-risk (a wrong answer is an annoyance,
not a harm). If my chosen task is my highest-value but highest-risk one, push back and tell me to
pick the safe job next to it instead.
2. The loop must prove a real human hands a real job to the machine and keeps the result. If removing
the AI leaves it still working, tell me I scoped a feature, not an agent.
3. The fallback must be bounded: retry a set number of times, then drop to a fixed answer and a named
human. No infinite loops.
Don't flatter me. End with the ONE next action to build it.Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

