client-discovery
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npx mdskill add aAAaqwq/AGI-Super-Team/client-discoveryTransforms raw client requests into structured project scoping.
- Converts unstructured task lists into categorized estimates.
- Depends on CRM data and client workspace documents.
- Uses technical analysis to define build requirements.
- Outputs structured breakdowns with hours and priorities.
SKILL.md
.github/skills/client-discoveryView on GitHub ↗
--- name: client-discovery description: Analyze client automation/AI requests into structured scoping with hours, pricing, and priorities --- # Client Discovery > Take a client's raw list of requests and produce a structured scoping breakdown with categories, hours, pricing, dependencies, and recommended phases. ## When to use - Client sends a list of automation/AI tasks they want built - "analyze requests from [client]" - "scope this project" - "estimate hours for [client]" - "create proposal breakdown" - Before a discovery/scoping call — to come prepared with estimates ## Dependencies - Other skills: `query-leads` (CRM data), `client-workspace` (for shared docs) - External: none (this is an analysis skill, no scripts) ## How to execute ### Step 1: Gather inputs 1. **Client's raw request list** — from TG, email, call notes, or shared doc 2. **Client's tech stack** — CRM, ATS, tools they use (from CRM notes or questionnaire) 3. **Company context** — from CRM: size, industry, budget signals ### Step 2: For each request item, analyze For every item in the client's list, produce: | Field | Description | |-------|-------------| | **Name** | Short name (2-5 words) | | **Category** | `agent` / `automation` / `integration` / `knowledge-base` / `product` | | **What client wants** | Plain language — what outcome they expect | | **What needs to be built** | Technical: APIs, triggers, LLM prompts, data flows | | **Key questions** | What we need to clarify before building | | **Integrations** | Which tools/APIs: CRM, ATS, LinkedIn, Bluedot, etc. | | **Complexity** | `low` (prompt eng, 4-6h) / `medium` (integration, 6-10h) / `high` (multi-system, 10-15h) | | **Hours estimate** | Range: low-high | | **Dependencies** | Other items that should be built first | ### Step 3: Prioritize Group items into: 1. **Quick wins** (low complexity, high impact) — do first, show value fast 2. **High ROI** (medium complexity, core business impact) — second phase 3. **Strategic** (high complexity, long-term value) — third phase 4. **Can skip / already exists** — tools like NotebookLM that solve it out of the box ### Step 4: Check for off-the-shelf solutions Before estimating custom build hours, check if an existing tool already does it: - NotebookLM for knowledge bases - Zapier/Make for simple automations - Existing SaaS (Fireflies for transcription, Clay for signal tracking, etc.) Flag these as "buy vs build" decisions with the client. ### Step 5: Produce summary table ``` | # | Request | Hours | $ | Phase | Notes | |---|---------|-------|---|-------|-------| | 1 | Job posting AI | 4-6 | 400-600 | Quick win | Few-shot prompting | | 2 | CRM automation | 8-12 | 800-1200 | Phase 2 | Needs API access | ... | TOTAL | | 60-90 | $6K-9K | | | ``` ### Step 6: Generate discovery questions Based on gaps in the analysis, generate a pre-call questionnaire: - Questions about tech stack and data - Questions about priorities and budget - Questions about team and users Use `client-workspace` skill to create a shared Google Doc with these questions. ## Rate Card | Service | Rate | |---------|------| | Consulting / implementation | $100/hr, 15-min increments ($25 min) | | Quick win (4-6h) | $400-600 | | Medium project (6-12h) | $600-1200 | | Complex project (10-15h) | $1000-1500 | ## Output Format The analysis should be saved as: 1. **CRM activity** — summary in activities.csv 2. **Shared doc** — if questionnaire created, in client's Discovery folder 3. **Text summary** — shown to Ivan for review before the call ## Checklist - [ ] All client request items analyzed and categorized - [ ] Hours and pricing estimated for each item - [ ] Off-the-shelf alternatives checked - [ ] Items prioritized into phases - [ ] Discovery questions generated for unknowns - [ ] Summary table produced - [ ] CRM activity logged ## Examples ### Client J (2026-03-04) Client: Diana Prince, Client J (IT recruiting, 22 years experience) Stack: Recruitee (ATS), Streak (CRM), Bluedot (call recording) 11 automation requests → analyzed into 4 blocks: - Block 1: CRM & Sales (18-27h, $1.8-2.7K) - Block 2: Recruiting process (16-22h, $1.6-2.2K) - Block 3: Knowledge base (13-20h, $1.3-2K) — partly solved by NotebookLM - Block 4: Client-facing products (14-22h, $1.4-2.2K) Total: 61-91h, $X-YK ## Related skills - `client-workspace` — create shared docs for discovery - `call-prep` — prepare for the discovery/scoping call - `query-leads` — CRM data lookup
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