pull-query

name	pull-query
description	Search PULL analyses by criteria to find evidence for segments. Use when searching for demand signals, conversion patterns, or buyer evidence.
argument-hint	[query — e.g., 'scaling triggers', 'score > 15', 'would close']

Query PULL analyses to find evidence for segments, messaging, or pattern recognition.

Process

Step 1: Load Index

Read demand/pull-index.json.

If missing or empty: "No PULL analyses indexed yet. Paste a sales call transcript and I'll run a PULL analysis to start building the evidence layer."

Step 2: Parse Query

Accept natural language or structured filters:

Natural language:

• "Show me all demand calls"
• "Find calls where they mentioned scaling"
• "Who would close?"
• "Calls with score above 15"

Structured filters:

classification = demand
primary_trigger = scaling_team
pull_score >= 15
buyer_type = vp_engineering
would_close = yes
features_resonated contains integration

Multiple conditions use AND logic.

Step 3: Execute and Display

Summary view (default):

## Results: {count} analyses matching "{query}"

| Company | Prospect | Score | Trigger | Would Close |
|---------|----------|-------|---------|-------------|
| {co} | {name} | {X}/20 | {trigger} | {yes/no} |

Avg Score: {avg}/20

Detailed view (if few results or requested): Include full analysis metadata per result.

Step 4: Aggregation Queries

Support "group by" for pattern analysis:

## By Trigger

| Trigger | Count | Avg Score | Demand % |
|---------|-------|-----------|----------|
| {trigger} | {n} | {avg} | {pct} |

Step 5: Suggest Usage

After displaying results:

• If user is building a segment: "These analyses support a segment hypothesis. Want to create a segment definition?"
• If user is developing messaging: "These buyer quotes can ground your messaging angles."
• If finding patterns: "The dominant trigger is {X} — this could be your primary scenario."

Common Patterns

• Segment validation: "Do we have evidence for targeting {persona}?" → filter by buyer_type
• Messaging grounding: "What language do buyers use about {topic}?" → read full analyses for quotes
• Win pattern: "What do fast buyers have in common?" → filter would_close = yes, look for patterns
• Trigger discovery: "Group by trigger" → find which triggers correlate with demand