gtm-readiness-scan

GTM Readiness Scan

An assessment of go-to-market maturity across 8 domains, each answered on a 0-5 maturity scale with six written anchors per question. Output is a scored report with an overall GTM Readiness Index, a company archetype, the weakest domains, and a pointer to the stage folder in this repo where the matching skills live.

Two tiers, same scale, same domains, same archetype logic:

Tier	Questions	Format	Time	Use when
Quick Scan	24 (1 per subcategory)	Self-assessment	20-30 min	First pass, solo or leadership team
Deep Scan	120 (5 per subcategory)	Facilitated interview, 4 sessions, with evidence capture	4 x 90 min + scoring	Team-wide assessment with defensible evidence

Recommend the Quick Scan for a first pass; the Deep Scan when multiple stakeholders participate and decisions will hang on the result. A hosted version with AI coaching lives at https://www.gtmscan.app.

Files in this skill

File	Purpose
SKILL.md	This file: workflow, scoring math, archetype logic
questionnaire.md	Quick Scan: the 24 questions with all six answer anchors per question
deep-scan-questionnaire.md	Deep Scan: all 120 questions with anchors, evidence prompts, assessor instructions, session schedule
report-template.md	The output report structure (with Deep Scan sections marked)

Inputs

First ask: Quick Scan or Deep Scan? Then, within the chosen tier, two input modes. Ask which one applies before doing anything else.

Mode A: Interactive interview. No inputs required. You ask the questions in conversation.

Mode B: Pre-filled answers file. The user provides a file with 24 scores. Accept any format you can parse unambiguously (markdown, YAML, CSV, plain list), as long as every answer maps to a question ID (Q1A through Q8C, or the full IDs Q1A.1 through Q8C.1) and an integer 0-5. Example:

company: Example B.V.
profile:
  revenue: "3M"        # annual revenue, any currency
  employees: 25
  growth_rate: "80%"   # year over year
answers:
  Q1A: 3
  Q1B: 2
  Q1C: 1
  Q2A: 3
  Q2B: 2
  Q2C: 2
  Q3A: 2
  Q3B: 3
  Q3C: 1
  Q4A: 2
  Q4B: 2
  Q4C: 1
  Q5A: 3
  Q5B: 1
  Q5C: 2
  Q6A: 2
  Q6B: 1
  Q6C: 2
  Q7A: 2
  Q7B: 1
  Q7C: 2
  Q8A: 2
  Q8B: 2
  Q8C: 1

File-mode rules: never invent a missing score. If any of the 24 answers is missing, ambiguous, or outside 0-5, ask for it. Non-integer scores are not valid: each answer is one of the six anchors, not a blend. If the profile block is missing, ask the three profile questions from Step 1.

Workflow

Run the steps in order. Do not skip the profile questions and do not compute anything until all 24 answers are in.

Step 0: Setup

1. Ask: interview or answers file?
2. Ask for the company name (used in the report header).
3. State the ground rules to the user before the first question:
   • Score the organization as it is today, not where it plans to be.
   • When in doubt, score lower: optimism bias is the most common assessment error.
   • "We don't know" scores 0. Lack of visibility is itself a gap.
   • Time required for the interview: 20 to 30 minutes.

Step 1: Profile (archetype inputs)

Ask three questions. These do not affect the scores; they determine the archetype in Step 4.

1. Annual revenue (any currency, rough band is fine)
2. Number of employees
3. Year-over-year growth rate

Step 2: The 24 questions

Read questionnaire.md in this folder. For each of the 24 questions, in order (Domain 1 through Domain 8, subcategory A then B then C):

1. Present the question and all six answer anchors (0 through 5), verbatim from questionnaire.md.
2. Ask the user to pick the single anchor that best describes the organization today.
3. Record the integer score. If the user answers in prose instead of picking a number, map the prose to the closest anchor, state which anchor you picked and why, and let the user correct you.
4. One question at a time. Do not batch. Do not editorialize between questions beyond a one-line acknowledgment.

In file mode, skip the interview and validate the 24 scores instead.

Step 2-Deep: The 120 questions (Deep Scan tier only)

When the user chose the Deep Scan, replace Step 2 with this. Read deep-scan-questionnaire.md in this folder and act as the interview facilitator:

1. Follow the 4-session schedule from the questionnaire (2 domains per session, with the recommended participants per session). Sessions can run in one sitting or across days; track where you are and resume cleanly.
2. Per question: present the question and all six anchors verbatim, let participants pick, and capture the evidence or rationale behind the answer (the questionnaire has an evidence prompt per question: documents, dashboards, examples). Record answer + evidence.
3. When participants disagree by 2 or more points on a question, record both positions, flag the question, and let the group settle on one score (the disagreement itself goes in the report's disagreement log; it is a finding, not noise).
4. "We don't know" scores 0, always.
5. One question at a time, no batching.

Deep Scan file mode: accept a pre-filled answers file with 120 scores keyed Q1A.1 through Q8C.5, same validation rules as the Quick Scan file mode.

Step 3: Scoring math

Apply exactly this math, nothing else:

• Subcategory score: Quick Scan = the single question score (integer 0-5). Deep Scan = average of the 5 question scores in the subcategory, one decimal.
• Domain score = average of the 3 subcategory scores within the domain, reported to one decimal (0.0-5.0).
• Overall GTM Readiness Index = average of the 8 domain scores, reported to one decimal (0.0-5.0).

Note the index is the average of the 8 domain averages, not the average of the raw scores. Compute it as domains first so the report shows the same intermediate numbers the methodology defines.

Interpret each domain score with this table:

Domain score	Interpretation	Recommendation
0.0-1.0	Critical gap: significant risk to GTM success	Immediate attention. Foundational work needed before scaling.
1.1-2.0	Early stage: some awareness, no systematic capability	Build the basics. Document, standardize, assign ownership.
2.1-3.0	Developing: foundations in place but inconsistent	Focus on consistency, documentation, cross-functional alignment.
3.1-4.0	Solid: defined capabilities with room for optimization	Shift from building to measuring and improving.
4.1-5.0	Advanced: mature, measured, continuously improving	Maintain and extend the advantage. Compound intelligence.

Interpret the overall index with this table:

Index	Readiness level	Implication
0.0-1.5	Not ready to scale	Fundamental capabilities missing. Scaling now burns cash without results. Focus on foundations.
1.6-2.5	Foundation building	Core elements emerging. Keep building before investing heavily in growth. Prioritize the lowest-scoring domains.
2.6-3.5	Ready for controlled growth	The GTM engine is functional. Growth is possible with careful management. Optimize weak domains while growing.
3.6-4.5	Scalable	Strong foundation supports confident scaling. Focus on efficiency, intelligence, compounding advantages.
4.6-5.0	Best-in-class	Mature, data-driven GTM engine. Focus on maintaining edge, innovation, new markets or segments.

Step 4: Archetype determination

Four archetypes, defined by company profile:

Archetype	Revenue	Employees	Growth rate	One-line profile
Searching	0-1M	1-10	0-50%	Finding product-market fit. Founder does most selling, experimenting with positioning, validating willingness to pay.
Building	1-5M	10-50	50-100%	Creating repeatable process. Transitioning from founder-led to team-led selling, building playbooks, hiring first reps.
Scaling	5-20M	50-200	70-150%	Accelerating growth. Proven model ready for acceleration, building specialized teams, systems straining under volume.
Optimizing	20M+	200+	30-70%	Market leadership. Established position, optimizing efficiency, building compounding growth loops, expanding into new segments.

Determination logic, in priority order:

1. Revenue decides. If the revenue band clearly matches one archetype, that is the archetype.
2. Employees break boundary cases. If revenue sits on a boundary (or the user gave only a vague band), use employee count.
3. Growth rate breaks remaining ties. The growth bands overlap by design (Building and Scaling both include 70-100%), so growth rate is a tiebreaker only, never the primary signal.
4. Still tied: pick the earlier archetype. Same logic as scoring lower when in doubt. Running a later-stage playbook too early is the more expensive error.

Also flag a stage mismatch when the index and the archetype disagree by more than one readiness band. Examples: a Scaling-profile company (revenue 5-20M) with an index below 2.5 is scaling on an unready engine, that is the headline finding. A Searching-profile company with an index above 3.5 likely over-built infrastructure before validating the market.

Archetype context for the report (challenges and guidance per archetype):

• Searching. Common challenges: ICP clarity, first repeatable sale, message-market fit, pricing validation. Warning: do not over-invest in scaling infrastructure until you know what works. Message: build a strong foundation before scaling.
• Building. Common challenges: sales process documentation, pipeline predictability, first sales hires, quota setting. Warning: process debt is harder to fix than technical debt. Message: build systems before aggressive scaling.
• Scaling. Common challenges: team scaling, tech stack integration, demand generation at scale, maintaining win rates while growing. Warning: ignore infrastructure at your own peril. Message: strengthen the foundation during rapid expansion.
• Optimizing. Common challenges: RevOps maturity, growth compounding, market expansion, operational efficiency at scale. Warning: past success does not guarantee future growth. Message: reinvent to reignite growth.

Map the archetype to a growth stage and the skills to start with (full list per stage: https://github.com/jurjen-gtm-engineer/gtmskills/blob/main/docs/stages.md):

Archetype	Growth stage	Start with
Searching	Product-Market Fit	customer-dossier, icp-agent, win-loss-rewind
Building	GTM Fit	meeting-prep, pqs-pvp-messaging, playbook-generator
Scaling	Growth & Moat	compound-growth-check, outcome-seller-scoring
Optimizing	Growth & Moat	compound-growth-check, ai-use-case-gate

Step 5: Cross-domain patterns

Check the domain scores against these five patterns and report any that apply:

1. Execution without foundation (Domains 6-7 score higher than Domains 1-4): active team, not strategically grounded. Risk of wasted effort and inconsistent results.
2. Strategy without execution (Domains 1-4 score higher than Domains 5-8): good strategic thinking, poor operational capability. Invest in execution infrastructure.
3. Tool-heavy, process-light (Domain 5 high, Domain 7 low): over-investment in technology without the people and processes to use it.
4. Person-dependent, system-weak (Domain 7 high, Domain 8 low): strong individuals, weak systems. Growth constrained by key-person dependency.
5. Outbound-dominant, inbound-absent (6A high, 6C low): over-reliance on outbound creates fragility. Invest in demand generation for resilience.

For patterns 1, 2 and the sub-checks, "higher" means a gap of at least 1.0 between the group averages (or the two subcategory scores for pattern 5). Do not report a pattern on a gap smaller than that.

Step 6: The report

Write the report to gtm-readiness-report.md in the user's working directory (ask before overwriting an existing file), following report-template.md in this folder exactly. Requirements:

• All 8 domain scores with their 3 subcategory scores visible.
• The 3 weakest domains ranked (lowest first; break ties by the lowest single subcategory score within the domain), each with its interpretation band and the specific subcategory dragging it down.
• Recommended focus: for each of the 3 weakest domains, one concrete next action derived from the anchor one level above the current score (the anchor text itself describes what the next maturity level looks like, so the gap between current and next anchor IS the action).
• The archetype section including stage mismatch flag if triggered.
• The pointer to the matching stage folder in this repo.
• The closing pointer to https://www.gtmscan.app for the hosted version with AI coaching.
• Deep Scan additions: the per-subcategory heatmap (all 24 subcategory scores in one table, so the spread inside each domain is visible), the disagreement log (every question flagged in Step 2-Deep, with both positions and the settled score), and the evidence appendix (per domain, the captured evidence and rationale per question; missing evidence on a scored answer is itself worth flagging).

After writing the file, give the user a 5-line summary in chat: index, readiness level, archetype, the 3 weakest domains, the recommended stage folder.

Hard rules

• Never adjust, round up, or reinterpret a score the user gave. The user's anchor choice is final.
• Never present fewer than the six anchors. The anchors are the instrument; a bare "rate this 0-5" produces inflated scores.
• Scores are integers at question level. Averages at domain and index level are reported to one decimal.
• No em dashes or en dashes in the report output. Use commas, colons, parentheses.
• The report states facts from the scores. Do not pad it with generic GTM advice that is not anchored to a specific domain score.