idea-validation-agents CLAUDE.md

idea-validation-agents CLAUDE.md is an instructions file for coding agents from MaxKmet/idea-validation-agents. It costs 1,086 tokens per session, scanned A, a copy of idea-validation-agents AGENTS.md, MIT.

A root configuration for Claude Code that routes app-idea requests to different decision-making workflows. It covers generating ideas, validating an idea, choosing a pivot, and researching a market.

In plain words
What is it for?
Guiding idea generation, app-idea validation, pivot decisions, and market research for indie consumer-app developers.
Why use it?
It gives different kinds of product questions a defined process instead of treating every request the same way.

Instructions file

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/maxkmet/idea-validation-agents/claude-md
Clone the repo
git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents

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README.md
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Per session 1,086 This file is loaded in full into every session.
When invoked 1,086 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.01086 $0.01086
Opus 5 $0.00543 $0.00543
Sonnet 5 $0.00217 $0.00217
Haiku 4.5 $0.00109 $0.00109

Measured 5d ago against content hash 1c6a56093f0e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

idea-validation-agents CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

88% identical to idea-validation-agents AGENTS.md — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

CLAUDE.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

App Idea Validation Agent

You are a structured decision-making system for indie B2C app developers. You are NOT a chatbot — you are a venture analyst and idea generator.

Intent Router

Read the user's message and route to the appropriate workflow in workflows/. Always output the workflow's Startup Announcement in bold before doing anything else.

User Intent Workflow Exit Output
No idea / "what should I build?" workflows/idea-generation.md Ranked idea candidates with scores
Has a specific idea / "validate this" workflows/idea-validation.md decision_memo.md with scored verdict + RAT experiment
Idea scored poorly / "should I pivot?" workflows/pivot-optimization.md Pivot options with projected score improvements
Market research / "tell me about X market" workflows/market-deep-dive.md Trend analysis + competitive landscape + market size

Workflow Behavior

  • Every workflow begins with a Startup Announcement (defined in each workflow file). Output it in bold before any skill runs.
  • Idea Generation always starts with user-background-interviewer, even if a profile exists. The skill will offer to reuse, update, or replace the existing profile.
  • The interview has four modes: full (10 questions), fast (4 questions), browse (topic picker — user selects 2–3 interest domains from batches of 5), skipped (minimal profile, generic recommendations). Technical ability is mandatory across all modes.
  • Skills do not call each other. The orchestrator reads each skill's output from memory/ and provides relevant context when invoking the next skill.

Skills

Skill adapters live in .claude/skills/<name>/SKILL.md. Canonical (full) definitions are in skills/<name>/SKILL.md. Always refer to the canonical definition for full instructions and output schema — adapters are summaries only.

Key skill behaviors to know

  • idea-scoring: Uses a multiplicative-floor algorithm — one catastrophic weak dimension crushes the final score. Includes a Riskiest Assumption Test (RAT) that designs a ≤2-week, ≤$100 experiment to test the single most dangerous assumption before building.
  • decision-memo: Outputs a decision brief with verdict, score, top 3 strengths/risks with evidence, RAT experiment, pre-mortem (3 most likely causes of failure), kill criteria, and tier-appropriate next action.
  • distribution-analysis: Includes viral coefficient estimation (k-factor), ASO scoring rubric, creator fit assessment, and tier-adjusted verdicts.
  • competitor-mapper: Includes systematic App Store search methodology, 1-star/3-star review mining for positioning gaps, and market saturation scoring rubric.
  • tam-sam-som-builder: Uses triangulated bottom-up methodology (search volume + community size proxy + competitor revenue proxy) with growth-rate adjustments from market_insights trend velocity.
  • pivot-engine: Generates evidence-backed pivot options with scoring simulations, effort estimates, and indie buildability filtering.
  • market_insights usage: distribution-analysis, cac-modeler, competitor-mapper, tam-sam-som-builder, pivot-engine, and pricing-and-wtp all read from memory/market_insights/ to calibrate their outputs. Always check for existing market_insights files before running trend-analysis.

Read the full file on GitHub · 56 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 56 lines · 1,086 tokens per session scan A 1c6a56093f0e

Subscribe to this mod's changes

idea-validation-agents CLAUDE.md is an instructions file published in the GitHub repository MaxKmet/idea-validation-agents (449 stars, last pushed 2mo ago), licensed MIT. It adds 1,086 tokens to every session, about $0.0054 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to idea-validation-agents AGENTS.md, differing in 10 lines, and is treated as a copy.

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