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Grok Bot+18 agentsGTM Market Check: ICP, Positioning Risks + Channel Tests
Unlike free market-research prompts: evidence-labelled ICP, positioning risks + 3 small channel tests. No invented TAM, ROI or stats.
$7
GTM Market Check: ICP, Positioning Risks + Channel Tests
Example session with this skill installed
offer: Enterprise Retrieval-Augmented Generation (RAG) platform
decision_question: Proceed to a paid POC or build an internal solution?
audience: CTO and Engineering Lead
evidence: Technical documentation, security whitepaper dated Oct 2023, and internal cost estimates for a 3-month build.
alternatives: Internal LangChain-based prototype.
- Read your context and instructions
- Compiled the gtm market check
One or two lines summarising what was done.
A due-diligence brief was generated comparing the enterprise RAG platform against an internal build, focusing on security evidence and engineering trade-offs.
GTM due-diligence brief: Enterprise RAG Platform
Decision question: Proceed to a paid POC or build an internal solution?
Audience / decision owner: CTO and Engineering Lead
Market scope: Enterprise internal knowledge management
Evidence boundary: Tech docs, Oct 2023 Security whitepaper, internal build estimates
Generated: 2024-05-20 · Draft for human verification — not investment advice or a forecast
1. Executive readout
- What appears supported: SOC2 Type II compliance [E2]; Out-of-the-box connectors for Jira/Confluence [E1].
- What is a hypothesis: Vendor claims of <200ms latency at scale remain unverified in supplied docs.
- Most decision-relevant risk: Provider lock-in due to proprietary vector index format.
- Recommended next step: Run a 1-week latency stress test on the vendor's sandbox.
- Confidence: medium
2. Offer and decision frame
- Job to be done: Queryable internal documentation for 500+ engineers.
- Primary user / buyer: Engineering teams.
- Stated promise: Zero-config RAG with enterprise-grade security.
- Workflow and human hand-offs: Automated indexing; manual citation verification by users.
- Decision options in scope: test | buy | build
3. Evidence ledger
| ID | Claim or observation | Source / date | Type | Confidence | Decision implication |
|---|---|---|---|---|---|
| E1 | Supports 15+ data connectors | Tech Docs | Reported | high | Reduces initial integration effort |
| E2 | SOC2 Type II Compliant | Whitepaper (Oct 23) | Observed | high | Meets baseline procurement safety |
| E3 | Internal build takes 3 months | Internal Est | Assumed | medium | Baseline for ROI comparison |
4. Market and alternative context
- Category / problem space: Enterprise Search / AI Knowledge Management
- Alternatives: Internal build (LangChain/Pinecone), manual wiki search.
| Criterion | Offer | Internal Prototype | Evidence / confidence |
|---|---|---|---|
| Time-to-value | 1 week (config) | 12 weeks (build) | High |
| Customizability | Low (Black box) | High (Full control) | Medium |
| Security | Verified SOC2 | Requires audit | High |
5. AI, operational, and commercial risk review
| Risk area | Evidence observed | Unknown / failure mode | Impact if true | Verification |
|---|---|---|---|---|
| Data / privacy | Encryption at rest | Data residency options | High | Review DPA |
| Lock-in | Proprietary index | Migration cost | Medium | Ask for export API |
6. Positioning and GTM hypotheses
Positioning hypothesis: For CTOs who need secure internal AI search, this platform is a turnkey RAG solution that eliminates build-time, unlike internal prototypes, because it provides pre-built security compliance.
- Disqualifiers: Teams requiring custom embedding models not supported by the vendor.
7. Priority open questions
| Priority | Question | Why it changes decision | Evidence needed |
|---|---|---|---|
| 1 | What is the cost per query at 10k queries/day? | Affects long-term OpEx vs build | Pricing sheet |
8. Validation plan
| Priority | Hypothesis | Small, reversible test | Signal to observe |
|---|---|---|---|
| 1 | Latency stays <500ms | Load 1000 docs to sandbox | Response time logs |
Next steps
- Request the latest Data Processing Agreement (DPA) from the vendor.
- Schedule a technical deep-dive on vector index portability.
- Baseline the current "time-to-answer" for developers using manual search.
Connects securely to your tools. The creator never sees your data.
About this skill
Validate first → Market Check; then plan → GTM One-Pager.
Unlike free "do market research on my startup" prompts that invent TAM figures, conversion rates and competitor facts, this skill turns the offer, decision question and evidence you paste into a decision brief: ICP hypotheses, positioning risks, messaging gaps and small channel tests. Every claim is labelled observed / reported / inferred / unknown, with a confidence level.
Sample output (excerpt: AI support-ticket triage agent for Shopify stores, Generated: 2026-10-16): Decision: test with 1 ICP for 3 weeks before building integrations (confidence: medium)
| ICP hypothesis | Evidence | Label | Confidence |
|---|---|---|---|
| Shopify stores, 2–10 support agents | 6 call notes mention ticket backlog | observed | high |
| Agencies managing 5+ stores | 1 forum thread, undated | reported | low |
| Enterprise retail | none supplied | unknown | low |
| Positioning risk | Why it matters | Next check |
|---|---|---|
| "Replaces your support team" | Not supported by demo; trust risk | Reframe as triage + draft replies |
| Overlaps native Shopify Inbox | Buyer may see it as free | Compare 3 named workflows side by side |
Channel tests (2 weeks each, owner + stop rule)
- Shopify app listing draft: 20 visits → 2 installs, else stop
- Founder outreach to 15 stores from call notes: 3 demos booked
- One comparison post vs. native inbox: measure replies, not likes
Open questions: data access scope, refund-ticket handling, privacy review owner.
Works in Claude Code, Cursor and Codex · evidence-first, no invented market size or ROI · v1.0, updated Oct 2026 · questions answered via Agensi messages · More Cygnix Labs tools: https://apify.com/cygnix_labs
The problem Founders launch on gut feel and find out too late that the ICP, positioning or channel was wrong. Give your agent what you know about an AI or SaaS offer and get a pre-launch market check: decision frame, evidence ledger, market and alternative context, AI/operational risk review, positioning hypotheses, open questions, and small validation tests. It is built for founders, product teams, GTM operators, buyers, and consultants who need a clear next step—not invented market metrics, investment advice, or outcome guarantees.
What it covers
- Market context, buyer/job, category, alternatives, and buying friction
- Evidence quality with observed / reported / inferred / unknown labels
- AI capability, evaluation, privacy, security, reliability, dependency, and adoption questions
- Positioning, proof gaps, objections, and GTM hypotheses
- Prioritised open questions and reversible validation tests
- A decision checkpoint that states what the evidence does and does not support
Honesty and boundaries
This is a process and template skill. It uses supplied evidence and labels uncertainty; it does not browse by default or invent customer counts, market size, accuracy, uptime, savings, ROI, revenue, or other metrics. It is not investment, financial, legal, security, privacy, or compliance advice, and it does not guarantee product performance or commercial outcomes.
How to use
- 1
Connect your agent to the Agensi MCP
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- 2
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- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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