Fresh drop from r/PromptEngineering: u/cipher-cmd open-sourced Baithak, a Claude Code skill that debates startup ideas with a five-advisor council. It’s built for Indian startups, and it’s the first idea-validator I’ve seen that doesn’t act like your biggest fan.
The problem it goes after is what the author calls hallucinated optimism. Ask a generic model about your idea and it will find a billion-dollar angle in almost anything. A dog-walking app, a pickle subscription box, a B2B tool for an industry you made up five minutes ago. Every one of them comes back with a confident market size and a cheerful “this has real potential.” If you’ve ever pasted a half-baked idea into a chat window and felt great about it afterward, you’ve met this problem. The feeling is the bug.
The twist is that the LLM doesn’t get to decide the important stuff. Before any persona speaks, an orchestrator called the Chair runs hard checks:
- Customer segment: India 1 (English-fluent), India 2 (regional, aspirational), India 3, or Global. Mix them without distinct pricing and the idea gets flagged. A ₹999 plan that works for a Bengaluru professional can be dead on arrival for a shop owner in a Tier 3 town, and the Chair makes you say which one you’re really selling to.
- Friction gates: UPI collection failure rates, CA gatekeeper resistance, compliance hurdles. These are the boring real-world walls that kill ideas long before the pitch deck matters. If your product needs a chartered accountant to say yes, that’s a distribution problem, and it gets named up front.
- Legal gate: the model is never asked “is this legal?” Instead it looks up a local index of 24 dated, sourced Indian law files and returns WHITE, GRAY, or BLACK. BLACK is an unconditional veto. No persona can talk it down, no clever framing can sneak past it, and the dates on the files tell you how fresh the answer is.
The second twist is the quotes. The five advisors are modeled on Nithin Kamath, Kunal Shah, Sridhar Vembu, Girish Mathrubootham, and Deepinder Goyal. Each one is backed by a corpus of real interviews and public letters, roughly 65k to 125k words per person. A Python script checks every quote in the debate against those raw sources. If a quote was invented, the run fails. Not a warning, not a soft score. The whole run fails. That’s the part I keep coming back to, because most “expert panel” prompts happily put words in famous mouths and nobody ever checks.
Here’s the mini-workflow:
- 1️⃣ Chair pre-check: segment, friction, and law status come first, before any opinions. If you get a BLACK here, you can stop and save yourself the tokens.
- 2️⃣ Round 1: each advisor gives an independent critique through their own lens. Sridhar looks at frugal depth outside the metros, and Girish looks at global pricing leverage. Because they critique independently, you don’t get one voice steering the rest.
- 3️⃣ Round 2: rebuttals. Advisors have to challenge each other’s assumptions and can revise their positions. Watch for who changes their mind, because that tells you which objection actually landed.
- 4️⃣ Verdict: a weighted consensus score, the single strongest objection, and a 7-day test costing ₹0 to ₹5,000 that you run before writing any code. Think a landing page, a WhatsApp broadcast, or ten phone calls to real buyers.
Pro tips you can steal even if you never touch Indian startups:
- Put the yes/no facts in files and code, and keep the model for judgment. A lookup beats a vibe. If you can write the rule down with a source and a date, it doesn’t belong in a prompt.
- Validate citations with a script, not with another prompt. Asking a model to check a model is how errors sneak back in. Plain string matching against raw text is dull, fast, and hard to argue with.
- Force a rebuttal round. Agreement that survives a challenge means more than agreement in round one. You can bolt this onto any multi-agent setup in about one extra prompt.
- Make the output end in a cheap test, not a score. A number feels like an answer, but a 7-day experiment is something you can actually go do this week.
One honest caveat, and a commenter raised it too. A verbatim match proves the words exist in the source. It doesn’t prove the quote was used in the right context. Someone asked whether the check catches a real quote used out of context, and that question is still open. A founder saying “never take funding” about one specific stage could get stretched into advice for a totally different one, and a string match would wave it through. Another commenter made a fair point that the law lookup is the keeper, but whether an idea is worth doing is still a human call. I agree. The council is there to give you better objections to chew on, and the decision stays with you.
There’s a live demo and the MIT-licensed repo, both linked in the original post. Fork it, swap the law files and the advisor corpora for your own market, and see what breaks. Maybe it’s UK tax rules with founders you actually read, or US health regulations with operators you trust. The architecture carries over, only the sources change. 🏴☠ Then tell me which gate you’d add first.
Frequently Asked Questions
Q: Does the council’s verdict decide whether I should build the idea?
No, and the gates are not meant to make that call for you. They catch hard blockers like a BLACK legal status or a customer segment that doesn’t price cleanly, which a chatty model tends to skip past. Whether the idea is worth your time is still a human decision, so use the verdict as a stress test for your assumptions and then check them with real customer conversations.
Q: Does the quote check catch real quotes that are used out of context?
The post describes the check as matching each quote against the raw source corpus, so it reliably flags invented quotes. It doesn’t judge whether a real quote is being used in the right context. If that matters for your work, read the paragraph around each quote the council relies on in the source file before you trust the argument.
Q: Is the legal check worth keeping even if I skip the rest of the council?
One commenter said that’s the part they’d keep, and it makes sense. A lookup against dated, sourced law files returns the same WHITE, GRAY, or BLACK status every time, while a model guessing at legality can drift. A WHITE result only means the indexed sources don’t flag the idea, so take anything near GRAY to a lawyer before you commit money.
I built a 5-agent AI council that debates startup ideas with deterministic law checks & verbatim source citations
by u/cipher-cmd in PromptEngineering