map-connections

name	map-connections
description	Scan context files to extract entities and relationships into the memory system. Triggers on "who knows who?", "network graph", "map my connections", "extract relationships". See also: `brain` for graph visualization once relationships are extracted.

Map Connections

Scan context files to extract entities, relationships, and build a connection graph. This command populates the memory system with structured relationship data from markdown files.

Usage

• /map-connections -- Full scan of people/, projects/, context/
• /map-connections --incremental -- Only scan files modified since last run
• /map-connections [file-path] -- Scan a specific file

Trigger Words

Use this command when the user says:

• "map my connections", "build my network", "scan for relationships"
• "analyze my people files", "who knows who"
• "populate the graph", "extract entities from files"

Workflow

1. Gather Files

Scan these directories for markdown files:

• people/ - Relationship files
• projects/ - Project documentation
• context/ - User context files

For incremental mode, check file modification times against the last run timestamp (stored in context/.map-connections-last-run).

Read each .md file in people/, projects/, context/
Track: filename, content, modification time

2. Extract Entities from Each File

For each file, extract:

Entity Name: From filename or first heading

• people/sarah-chen.md -> "Sarah Chen" (type: person)
• projects/website-redesign.md -> "Website Redesign" (type: project)
• First # Heading in file overrides filename-based name

Mentioned Entities: Scan file content for:

• People patterns: Names in "works with [Name]", "client of [Name]", mentions of capitalized names
• Organizations: Company names, "works at [Org]", "employed by [Org]"
• Projects: "working on [Project]", project file references

Attributes (Phase 2): Look for structured data:

• Geography: "based in [City]", "from [City]", city/state mentions
• Role: "CEO of", "founder of", titles in file
• Industry: Keywords like "real estate", "finance", "tech"
• Communities: "member of [Group]", known groups (YPO, EO)

3. Extract Relationships

Identify explicit and implicit relationships. For each relationship, set origin_type honestly based on how you know it. The system automatically caps strength based on origin, so always use strength: 1.0 and let the guards enforce the ceiling.

Extracted Relationships (origin_type: "extracted", ceiling: 0.8) Explicitly stated in the file:

• "works with [Name]" -> works_with
• "client of [Name]" -> client_of
• "reports to [Name]" -> reports_to
• "invested in [Project]" -> invested_in
• "manages [Name]" -> manages
• "partner at [Org]" -> partner_at
• "advisor to [Name/Org]" -> advisor_to

Inferred Relationships (origin_type: "inferred", ceiling: 0.5) Co-mentioned or contextually implied:

• Two people mentioned in the same file -> mentioned_with
• People in the same project file -> collaborates_on
• Same city + same industry -> likely_connected
• Same organization -> colleagues
• Same community group -> community_connection

4. Deduplicate and Resolve

Before creating entities:

1. Normalize names to canonical form (lowercase, no titles)
2. Check if entity already exists in memory via claudia memory entities search --project-dir "$PWD"
3. Merge new information with existing entity data
4. Track which entities are new vs updated

5. Store in Memory

Use claudia memory batch for efficiency:

claudia memory batch --project-dir "$PWD" <<'EOF'
[
  {"op": "entity", "name": "Sarah Chen", "type": "person", "description": "CEO at Acme Corp"},
  {"op": "entity", "name": "Acme Corp", "type": "organization"},
  {"op": "relate", "source": "Sarah Chen", "target": "Acme Corp", "relationship": "works_at", "strength": 1.0, "origin_type": "extracted"},
  {"op": "relate", "source": "Sarah Chen", "target": "Tom Miller", "relationship": "works_with", "strength": 1.0, "origin_type": "inferred"}
]
EOF

For relationship origin_type:

• Explicitly stated in the file ("Sarah is CEO of Acme"): origin_type: "extracted"
• Co-mentioned or contextually implied: origin_type: "inferred"
• User told you directly: origin_type: "user_stated"

The system automatically caps strength based on origin. You don't need to manually calibrate. Just be honest about how you know, and always use strength: 1.0.

When re-encountering existing relationships, the system strengthens them incrementally (scaled by origin). Repeated evidence builds trust organically.

6. Report Results

Output format:

## Connection Map Results

**Scan completed:** [timestamp]
**Files processed:** [count]

### New Entities ([count])

| Name | Type | Source |
|------|------|--------|
| Sarah Chen | person | people/sarah-chen.md |
| Acme Corp | organization | people/sarah-chen.md |
| Website Redesign | project | projects/website-redesign.md |

### New Relationships ([count])

| Source | Relationship | Target | Origin |
|--------|--------------|--------|--------|
| Sarah Chen | works_at | Acme Corp | extracted |
| Sarah Chen | collaborates_on | Website Redesign | extracted |
| Sarah Chen | mentioned_with | Tom Miller | inferred |

### Inferred Connections ([count])

| Entity A | Entity B | Reason | Origin |
|----------|----------|--------|--------|
| Sarah Chen | Jane Doe | Same city (Palm Beach) + industry (real estate) | inferred |

### Updated Relationships ([count])

| Relationship | Change |
|--------------|--------|
| Sarah Chen -> client_of -> Beta Inc | strengthened (re-encountered, extracted) |

### Summary

- **People:** [count] total ([new] new)
- **Organizations:** [count] total ([new] new)
- **Projects:** [count] total ([new] new)
- **Relationships:** [count] total ([new] new)

---

Relationship Type Reference

Type	Description	Example
works_with	Professional collaboration	"Sarah works with Tom on sales"
works_at	Employment relationship	"Sarah is CEO at Acme"
client_of	Client relationship	"Acme is a client of ours"
reports_to	Reporting hierarchy	"Tom reports to Sarah"
manages	Management relationship	"Sarah manages the team"
invested_in	Investment relationship	"Fund invested in Acme"
partner_at	Partnership	"Sarah is partner at Firm"
advisor_to	Advisory relationship	"Sarah advises Startup"
knows	General acquaintance	Default for co-mentions
collaborates_on	Project collaboration	People in same project file
colleagues	Same organization	Inferred from org membership
community_connection	Shared community	Same group membership
likely_connected	Attribute-based inference	Same city + industry

Edge Cases

Ambiguous names:

• "Sarah" could match multiple Sarahs
• Use file context to disambiguate
• If uncertain, don't create relationship

Self-references:

• Don't create relationships where source = target
• Filter these during processing

Duplicate relationships:

• Check for existing relationship before creating
• Update strength if new confidence is higher

Empty files:

• Skip files with no extractable content
• Report as "skipped: [reason]"

Notes

• This command is idempotent: running multiple times won't create duplicates
• Incremental mode is faster but may miss cross-file relationships
• For best results, run full scan periodically, incremental scan daily
• Save last run timestamp to context/.map-connections-last-run