| 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
