arthai-marketplace

/client-discovery

Client discovery and AI readiness assessment.

Synopsis

/client-discovery <client-name> [-- brief]

When to use it

First substantive step with a new client — gathers business context, tech landscape, and pain points
Producing an AI maturity score and readiness level to anchor the rest of the engagement
Not for: deep competitive or industry research — that’s /market-research; or running the whole engagement lifecycle — that’s /consulting

Quickstart

/client-discovery acme-corp

What you’ll see: a structured intake interview (13 questions in three groups), followed by a current-state analysis, AI maturity scorecard, competitive research, and a set of discovery files written to the client’s directory.

Prerequisites: WebSearch access must be enabled in your Claude Code settings — step 4 (competitive landscape research) runs ~8 WebSearch queries and will fail if WebSearch is not permitted. Run the command from the project directory where consulting-toolkit/ exists (typically the claude-agents root).

Examples

/client-discovery acme-corp                                   # full interactive intake
/client-discovery acme-corp -- 50-person fintech, Postgres + AWS, wants churn prediction
                                                              # brief pre-answers questions; only gaps are asked

What it does

Intake interview — user-confirmation checkpoint: asks 13 questions via AskUserQuestion in three groups — Business Context (5), Current Technology (4), Pain Points & Goals (4). If you passed a -- brief, questions it already answers are skipped and the extracted answers confirmed with you. Declined questions are recorded as “Not disclosed”.
Current state analysis — business model summary, a Mermaid tech-stack diagram, a Data Readiness Score (1-5 across Volume, Quality, Accessibility, Governance, Integration), and a team capability matrix.
AI maturity assessment — scores Strategy, Data, Technology, People, and Process 1-5 against a rubric; composite score (5-25) maps to a readiness level (Not Ready, Early Stage, Developing, Advancing, AI-Native Ready), presented as an ASCII scorecard.
Competitive landscape research — runs ~8 WebSearch API queries (industry AI trends, competitor initiatives, case studies with ROI) and compiles a competitive intelligence brief (max 500 words). Requires WebSearch to be enabled — see Prerequisites above.
Writes the discovery files — profile plus four discovery documents (see below). If the client directory already exists, it warns you and asks whether to overwrite or create a versioned copy.

The whole process takes roughly 15-20 minutes with a cooperative client.

Output & artifacts

All files land in consulting-toolkit/clients/<client-name>/:

profile.md — company overview, business model summary, maturity score and readiness level
discovery/intake.md — all 13 questions with verbatim answers
discovery/current-state.md — business narrative, tech-stack diagram, data readiness scores, capability matrix
discovery/maturity-assessment.md — dimension scores with justification, composite score, scorecard, recommended focus areas
discovery/stakeholder-map.md — decision makers, influencers, champions, end users, blockers (templated with placeholders if not provided during intake)

Troubleshooting

Problem	Fix
Client can’t answer a question	Say so — it records “Not disclosed” and moves on; scores are justified from what was provided
Client directory already exists	The skill asks before touching it — choose overwrite or a versioned copy
Stakeholder map is mostly placeholders	Expected when stakeholder info wasn’t shared at intake — fill it in during follow-up conversations
Downstream skills say discovery data is missing	Confirm the discovery files above were written for the exact client name you’re passing them
Step 4 fails with a permission error	Ensure WebSearch is enabled/permitted in your Claude Code settings

/consulting — the engagement orchestrator; runs this as the discovery phase
/opportunity-map — the next step: turns discovery data into scored initiatives
/market-research — deeper, focused research beyond the discovery-phase scan