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