agentsclimarketplace

Business idea evaluator

Skill 2612evgenii-hue/business-idea-evaluator/business-idea-evaluator

Объективная оценка бизнес-идей через 18 субагентов и Business Reality Score. Agent Skill для Claude Code, Cursor и Codex.

Install
npx -y skills add 2612evgenii-hue/business-idea-evaluator --skill business-idea-evaluator

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Objectively evaluates business ideas through 18 independent subagents, evidence grading, probabilistic modeling, and Business Reality Score (BRS). Use when the user asks to evaluate, validate, score, or stress-test a business idea, startup concept, side project, SaaS idea, or monetization plan. Always use for business idea analysis — never evaluate from gut feeling in main context. Requires real subagent invocation, not simulated expert opinions.

SKILL.md

10.9 KB, as published. Nobody here has run it

Business Idea Evaluator

Professional analytical system. Not a motivational coach. Evaluate the idea, not the user's enthusiasm.

Non-negotiable rules

  1. Never evaluate before idea is confirmed — complete Phase 1 first.
  2. Never simulate subagents in main context — launch all 18 via Task/Agent tool.
  3. Never use simple arithmetic mean for final score — use scripts/calculate_brs.py.
  4. Never flatter — see references/forbidden-phrases.md.
  5. Never claim "no competitors" without expert 01 completing deep search.
  6. Mark evidence status on every material claim — see references/evidence-status.md.
  7. If web search unavailable, state it once and downgrade all market claims to hypotheses.

Pre-flight — verify subagents before Phase 2

Mandatory. Before launching any subagent, run from project root:

bash .agents/skills/business-idea-evaluator/scripts/verify_subagents.sh

If verification fails, install and re-verify:

bash .agents/skills/business-idea-evaluator/scripts/install-agents.sh

Do not proceed to Phase 2 until .cursor/agents/, .claude/agents/, .agents/agents/, and .codex/agents/ each contain all 18 subagents. First-time setup: bash .agents/skills/business-idea-evaluator/scripts/bootstrap.sh

Anti-simulation gate (Phases 2–3)

These actions are forbidden in the main agent context:

  • Writing expert or math JSON without a completed Task/Agent invocation for that agent
  • Summarizing what an expert "would say" instead of launching the subagent
  • Skipping failed agents without failed_agents entry and retry

Required: 12 + 6 = 18 separate Task/Agent invocations, each with subagent_type matching the agent name (e.g. biz-eval-01-analog-research). Launch all agents in a phase within one message (parallel).

After each phase, briefly confirm: «Запущено N/N субагентов, получено N/N JSON-ответов» before proceeding.

Phase 1 — Idea discovery (5 questions)

See references/discovery-protocol.md for slot map and examples.

Ask exactly one question per message. Wait for answer before next question.

#GoalFirst question depends on
1Who is the paying customerYour reading of user's initial idea
2Specific pain + frequencyAnswer to Q1
3Current workaround + cost of painAnswer to Q2
4Product format + what customer pays forAnswer to Q3
5Launch market + MVP constraintsAnswer to Q4

Rules for questions:

  • Max 2 sentences per question.
  • No "tell me more" or open-ended prompts.
  • Each question must extract one concrete business-model element.
  • Track answers internally; do not dump full Q&A into final report.

After Q5, output only:

Я понял идею так: [one dense paragraph covering: audience, pain, solution,
delivery format, monetization, launch market, differentiation, first MVP]

Then ask: «Подтверждаешь такую формулировку или нужно поправить?»

  • If user corrects → update paragraph, ask confirmation again.
  • Do not proceed to Phase 2 until explicit confirmation («да», «подтверждаю», «верно», etc.).

Save confirmed idea as IDEA_BRIEF for all subagents.

Phase 2 — Expert layer (12 subagents)

Launch all 12 in parallel (one message, 12 Task/Agent invocations). Read references/expert-evidence-package.md for output schema.

Agent fileRole
biz-eval-01-analog-researchDeep analog & substitute search
biz-eval-02-pain-demandReal pain & willingness to pay
biz-eval-03-audience-behaviorBuyer behavior & acquisition
biz-eval-04-market-countryCountry/market realities
biz-eval-05-trends-longevityTrend & 1–5y durability
biz-eval-06-monetizationRevenue model & unit economics hints
biz-eval-07-marketingChannels & trust barriers
biz-eval-08-implementationMVP & technical feasibility
biz-eval-09-legal-platformLegal/platform/ethical risks
biz-eval-10-competitive-moatCopyability & defensibility
biz-eval-11-launch-zero7/30/90 day launch plan
biz-eval-12-red-teamAttack the idea

Install agents if missing: run bash .agents/skills/business-idea-evaluator/scripts/install-agents.sh from project root.

Cursor / Claude — Task invocation pattern

One message, 12 parallel Task calls. Example for expert 01:

subagent_type: biz-eval-01-analog-research
prompt: |
  IDEA_BRIEF:
  <confirmed paragraph>

  TASK: Execute your role per agent definition. Use web search if available.
  Return ONLY valid JSON matching the schema in
  .agents/skills/business-idea-evaluator/references/expert-evidence-package.md
  for agent_id "01". Include sources with URLs when found.

Repeat for biz-eval-02-pain-demandbiz-eval-12-red-team in the same message.

Codex — explicit spawn

Codex does not auto-spawn. Tell the user once, then run:

  1. «Spawn 12 expert agents in parallel» — agents in .codex/agents/*.toml
  2. After merge: «Spawn 6 math agents in parallel»

Prompt template for each expert

IDEA_BRIEF:
<paste confirmed paragraph>

TASK: Execute your role per agent definition. Use web search if available.
Return ONLY valid JSON matching the schema in references/expert-evidence-package.md
for your agent_id. Include sources with URLs when found.

Collect all 12 JSON outputs. Merge into Expert Evidence Package (single JSON file or structured object). Write to biz-eval-evidence.json in workspace temp if needed.

Gate: Do not start Phase 3 until all 12 expert JSONs are received. If one fails, retry once; if still failing, note agent_failed in package and continue with penalty.

Phase 3 — Math layer (6 subagents)

Launch all 6 in parallel after Expert Evidence Package is complete. Each math subagent receives the full Expert Evidence Package and works independently of the other math subagents — it does not re-research the market, it processes the package numerically (counts, ranges, penalties). Independence is enforced by giving each its own context (separate Task/Agent invocation), not by prompt wording.

AgentInputRole
biz-eval-13-evidence-statsFull packageEvidence & source quality indices
biz-eval-14-math-modelPackage + agent 13Formula design & variable weights
biz-eval-15-scenario-probabilityFull package4-scenario probability map
biz-eval-16-unit-economicsFull packageCAC/LTV/churn model
biz-eval-17-sensitivityFull packageTop sensitivity parameters
biz-eval-18-experimentsFull package + math outputsValidation experiments

Prompt template for math agents

EXPERT_EVIDENCE_PACKAGE:
<paste full JSON from Phase 2>

TASK: Execute your role. Return ONLY valid JSON per your agent schema.
Do not re-research market — process the package mathematically.

Phase 4 — Deterministic BRS calculation

  1. Merge expert scores + math agent outputs into biz-eval-input.json (see references/scoring-formula.md; structure defined in references/evidence-package.schema.json).
  2. Validate, then score:
python3 .agents/skills/business-idea-evaluator/scripts/validate_evidence_package.py biz-eval-input.json
python3 .agents/skills/business-idea-evaluator/scripts/calculate_brs.py biz-eval-input.json
  1. Use script output as authoritative Business Reality Score. Main agent explains results; does not override numbers.

The script returns: base/min/max BRS, confidence, evidence_index, source_quality_index, hypothetical flag, blocking_caps_applied, main_blocking_risk, main_growth_factor, main_uncertainty_factor, the nine factors, a monte_carlo block (BRS distribution from seeded simulations whose spread is driven by the evidence base), a computed probability_map (verdict-band probabilities), and input warnings. Tune runs with --simulations N --seed N.

A worked input/output pair lives in references/example-input.json and references/example-output.json (also examples/sample-input.json).

If script fails, fix JSON and retry. Do not invent score manually.

Phase 5 — Final report

Render using references/report-template.md.

Verdict must map to BRS:

  • ≥65 — test in narrow segment (not "launch fully")
  • 45–64 — change model or segment before spending
  • 25–44 — weak; only cheap experiments justified
  • <25 — do not launch in current form

Always separate: proven by sources | statistical | hypothesis | needs verification.

Subagent installation

Agents live in skill bundle at agents/. Full bootstrap (install + verify + tests):

bash .agents/skills/business-idea-evaluator/scripts/bootstrap.sh

Install only:

bash .agents/skills/business-idea-evaluator/scripts/install-agents.sh
bash .agents/skills/business-idea-evaluator/scripts/verify_subagents.sh

Targets: .cursor/agents/, .claude/agents/, .agents/agents/ get Markdown; .codex/agents/ gets native TOML via build_codex_agents.py. Symlinks: .cursor/skills/business-idea-evaluator and .claude/skills/business-idea-evaluator.agents/skills/business-idea-evaluator. User-level ~/.cursor/agents/, ~/.claude/agents/, ~/.codex/agents/ also receive copies.

Resume / partial runs

If user returns after discovery: skip Phase 1 if IDEA_BRIEF confirmed earlier in thread. If experts done but math pending: start Phase 3 only.

References

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.