context-building

name

context-building

description

Build and maintain a global company context file that all other GTM skills read from. Captures product info, voice rules, ICP, win cases, proof library, campaign history, hypotheses, and DNC lists. Supports four modes: create (new context), update (append to existing), call recording capture (extract signals from transcripts), and feedback loop (import campaign results). Triggers on: "company context", "update context", "build context", "ICP", "win cases", "campaign history", "call recording", "feedback loop", "DNC list".

Company Context Builder

One global context file per company. Every other GTM skill reads from this file for voice, value prop, ICP, win cases, proof points, and campaign learnings.

Context File Location

claude-code-gtm/context/{company}_context.md

Single file per company, not per-campaign. All skills reference this path.

Modes

Mode 1: Create

Use when no context file exists yet. Walk the user through each section.

Step 1: Check if claude-code-gtm/context/{company}_context.md exists.

Step 2: If not, ask the user for each section (one at a time or in bulk):

Section	What to ask	Example
What We Do	Product one-liner, core value prop, email-safe value prop, key lingo, key numbers	Product description + quantifiable claims
ICP	Customer profiles, company sizes, roles, geographies	Target profiles with size ranges and regions
Win Cases	Past customers, why they bought, what worked	Concrete outcomes with metrics
Proof Library	Pre-written PS sentences for emails, mapped to audience and hypothesis	Ready-to-paste proof points
Campaign History	Past campaigns: vertical, list size, reply rate, learnings	(empty on first run)
Active Hypotheses	Current working hypotheses about what resonates	Pain points validated by campaign data

Step 3: Write the file using the schema from references/context-schema.md.

Key sections to get right:

What We Do — must include:

• Product one-liner
• Core value prop (internal version, can use any language)
• Email-safe value prop (outreach-friendly version of the value prop)
• Key numbers (quantifiable claims — database size, speed benchmarks, coverage stats)
• Key lingo (internal terms and definitions)

Proof Library — must include:

• Full PS sentences ready to paste into emails
• Each mapped to: best audience, best hypothesis, source win case
• Every proof point must trace back to a real win case
• Write the sentence as it would appear in the email (including "PS.")

Mode 2: Update

Use when context file exists and user wants to add or modify a section.

Step 1: Read existing context file.

Step 2: Ask what to update. Common updates:

• Add a new win case
• Add a campaign result
• Update ICP based on new learnings
• Add domains to DNC
• Revise or add hypotheses
• Add or update proof points in the Proof Library
• Update voice rules
• Update key numbers (e.g., database size grew)

Step 3: Append to the relevant section. Never overwrite existing entries — add new rows to tables, new bullets to lists.

Mode 3: Call Recording Capture

Use when the user pastes a call transcript or meeting notes.

Step 1: Read the transcript.

Step 2: Extract and categorize signals:

• ICP signals — who was on the call, their role, company size, what they care about
• Win case data — what resonated, what they said about their current workflow, pain points confirmed
• Proof point candidates — specific results or quotes that could become Proof Library entries
• DNC signals — any companies or domains mentioned as off-limits
• Hypothesis validation — which existing hypotheses were confirmed or refuted
• Voice feedback — any reaction to tone, language, or positioning that should update Voice rules

Step 3: Present extracted signals to the user for confirmation.

Step 4: Update the context file with confirmed signals.

Mode 4: Feedback Loop

Use when importing campaign results from your email sequencer (e.g. Instantly) or manual tracking.

Step 1: Read campaign results (CSV, pasted data, or email sequencer export e.g. Instantly).

Step 2: Extract metrics:

• Campaign name, vertical, list size
• Open rate, reply rate, positive reply rate
• Top-performing hypotheses (which P1 angles got replies)
• Patterns in positive vs negative replies

Step 3: Add a new row to the ## Campaign History table.

Step 4: Update ## Active Hypotheses based on results:

• Promote hypotheses with high reply rates to Validated
• Demote hypotheses that didn't resonate to Retired
• Note any new hypotheses suggested by reply patterns

Step 5: Update ## Proof Library if campaign results surfaced new proof points:

• New win cases → write new PS sentences
• Existing proof points that didn't resonate → add notes or remove

Cross-Skill References

This context file is consumed by:

• hypothesis-building — reads ICP, Win Cases, and product value prop to generate pain hypotheses
• email-prompt-building — reads Voice, What We Do, Proof Library, and Active Hypotheses to build prompt templates
• email-generation — reads the prompt template (which was built from this file)
• list-building — reads ICP and Win Cases for seed companies
• market-research — reads ICP and hypotheses for research scope
• enrichment-design — reads hypotheses for segmentation column design
• list-segmentation — reads hypotheses for tiering logic
• email-response-simulation — reads Voice rules to constrain rewrites
• campaign-sending — reads DNC list for exclusions

Reference

See references/context-schema.md for the full file schema with all sections and field definitions.