ingest-sources

name	ingest-sources
description	Process multiple source documents with Extract-Then-Aggregate discipline. Use when user shares multiple transcripts, emails, or documents for batch processing.
argument-hint	["folder-path"]
effort-level	max

Ingest Sources

Process multiple source documents (transcripts, emails, documents) using Extract-Then-Aggregate discipline to ensure no entity with dedicated sources gets lost.

Trigger

• "Process these transcripts"
• "Here are my notes from [event]"
• Multiple files shared in sequence
• "Here's everything about [topic]"
• Folder path provided with multiple files
• /ingest-sources

Why This Skill Exists

When processing many sources, the failure mode is jumping to aggregation and missing entities that have dedicated sources but aren't prominent in high-traffic threads. A person with 2 transcripts dedicated to them can get lost if they're not mentioned often in emails.

The discipline: Inventory before processing, extraction before synthesis.

Input

User provides one of:

• Folder path containing multiple files
• List of file paths
• Multiple documents pasted in sequence
• Reference to previously shared content

The Five-Phase Workflow

Phase 1: Inventory

Before reading any content, create a manifest of all sources:

| # | Filename | Type | Date | Size | Likely Entities |
|---|----------|------|------|------|-----------------|
| 1 | call-with-sarah.md | transcript | 2026-01-15 | 4.2KB | Sarah Chen |
| 2 | jim-partnership-email.md | email | 2026-01-16 | 1.8KB | Jim Ferry |
| 3 | acme-contract.pdf | document | 2026-01-17 | 52KB | Acme Corp |
...

**Summary:**
- Total: 36 sources
- Date range: Jan 15 - Feb 1
- Types: 28 transcripts, 5 emails, 3 documents

Show inventory to user before proceeding. This prevents partial processing.

Phase 2: File-Then-Extract (Per Document)

CRITICAL: For each document, file it BEFORE extracting. This ensures provenance.

For each source in inventory:
    1. READ the full content
    2. CALL `claudia memory document store --project-dir "$PWD"` immediately (do not skip!)
    3. THEN extract entities/facts/commitments

Process each document systematically. Use IngestService (via local Ollama) when available, or extract directly.

Auto-detect source type:

• .md, .txt with participant names → meeting mode
• Email headers detected → email mode
• .pdf or formal structure → document mode
• Mixed content → general mode

Extraction schema per document:

Source #1: call-with-sarah.md
├── entities[]
│   ├── name: "Sarah Chen"
│   ├── type: person
│   ├── mention_count: 47
│   └── first_context: "Product lead at Acme Corp"
├── facts[]
│   ├── content: "Sarah prefers async communication"
│   ├── about: ["Sarah Chen"]
│   └── importance: 0.7
├── commitments[]
│   ├── content: "Send proposal by Friday"
│   ├── who: "user"
│   ├── to: "Sarah Chen"
│   └── deadline: "2026-02-07"
├── relationships[]
│   ├── source: "Sarah Chen"
│   ├── target: "Acme Corp"
│   └── relationship: "works_at"
└── dedicated_to: "Sarah Chen"  ← CRITICAL: This source is primarily ABOUT Sarah

Progress tracking:

Extracting: [========>   ] 28/36 (78%)

The dedicated_to field is essential. If a source is primarily about a specific entity (not just mentioning them), mark it. This prevents the "missing entity" problem.

Phase 3: Consolidation

After all extractions complete, merge by entity:

Canonicalize names:

• Check existing entity_aliases table for known aliases
• Fuzzy match "Sarah" vs "Sarah Chen" vs "S. Chen"
• Ask user to confirm ambiguous matches

Merge semantically identical facts:

• "Sarah prefers Slack" + "Sarah likes async comms" → single fact about communication preference
• Keep the more specific version

Track source counts:

Entity: Sarah Chen
├── Dedicated sources: 4 (#1, #5, #12, #18)
├── Total mentions: 12 sources
├── Facts extracted: 8
└── Commitments: 2

Phase 4: Verification

Before storing anything, verify completeness:

### Entity Coverage

| Entity | Dedicated Sources | Total Mentions | Sources |
|--------|-------------------|----------------|---------|
| Sarah Chen | 4 | 12 | #1, #5, #12, #18, ... |
| Jim Ferry | 2 | 6 | #2, #15, ... |
| Acme Corp | 3 | 8 | #3, #7, #22, ... |
| Project Alpha | 0 | 4 | #4, #8, #11, #19 |

### Dedicated Source Rule

**Any entity with 2+ dedicated sources MUST appear proportionally in the final output.**

If Jim Ferry has 2 transcripts dedicated to him but doesn't show up in the entity coverage summary, that's a verification failure. Stop and investigate.

### Gaps Detected

- Source #14: No entities extracted (may need manual review)
- Source #22: References "the investor" without name

### Completeness Check

Before proceeding:
- [ ] Every dedicated source entity appears in coverage
- [ ] No sources skipped or failed
- [ ] Ambiguous entity names resolved
- [ ] Gaps acknowledged or explained

User must confirm before proceeding to storage. This is the checkpoint that catches the "missing entity" problem.

Phase 5: Storage

After user confirms verification:

1. Verify all sources filed: Sources were already filed during Phase 2 (File-Then-Extract). Verify the file count matches:

Confirm: [N] sources filed to ~/.claudia/files/

If any sources weren't filed in Phase 2, file them now before proceeding.

Files are auto-routed to entity folders:

• people/sarah-chen/transcripts/...
• clients/acme-corp/documents/...
• projects/alpha/emails/...

2. Create/update entities:

claudia memory batch --project-dir "$PWD" <<'EOF'
[
  { "op": "entity", "name": "Sarah Chen", "type": "person", "description": "Product lead at Acme Corp" },
  { "op": "entity", "name": "Jim Ferry", "type": "person", "description": "Partnership contact" },
  { "op": "entity", "name": "Acme Corp", "type": "organization", "description": "Client company" }
]
EOF

3. Store facts and relationships:

claudia memory batch --project-dir "$PWD" <<'EOF'
[
  { "op": "remember", "content": "Sarah prefers async communication", "about": ["Sarah Chen"], "importance": 0.7 },
  { "op": "relate", "source": "Sarah Chen", "target": "Acme Corp", "relationship": "works_at", "strength": 0.9 }
]
EOF

4. Link provenance:

memory_sources table connects memories → source documents
entity_documents table connects documents → entities

This creates the chain: any fact can trace back to the exact document it came from.

Output Format

**📥 Multi-Source Ingestion: [Topic/Event]**

### Phase 1: Inventory Complete
[Summary table shown above]

Proceed with extraction? [y/n]

---

### Phase 2: Extraction Complete
- Sources processed: 36/36
- Entities found: 12
- Facts extracted: 87
- Commitments detected: 14
- Relationships mapped: 23

---

### Phase 3: Consolidation Complete
- Unique entities: 9 (after deduplication)
- Canonical names resolved: 4 aliases merged

---

### Phase 4: Verification

[Coverage table shown above]

**Dedicated Source Check:**
✓ Sarah Chen: 4 dedicated sources, appears in 12 total
✓ Jim Ferry: 2 dedicated sources, appears in 6 total
✓ Acme Corp: 3 dedicated sources, appears in 8 total

**Gaps:**
⚠ Source #14: No entities extracted

Ready to store? [y/n]

---

### Phase 5: Storage Complete

**Files stored:** 36
**Entities created/updated:** 9
**Memories stored:** 87
**Relationships created:** 23

All sources linked to entities. Provenance chain complete.

**Query examples:**
- "What do I know about Jim Ferry?" → will surface all 6 source memories
- "Show me Sarah's transcripts" → will list all 4 dedicated files
- "Where did I learn about Acme's timeline?" → will cite exact source

---

Judgment Points

Ask for confirmation on:

• Ambiguous entity matches (is "S. Chen" the same as "Sarah Chen"?)
• Sources with no extractable entities (manual review needed?)
• Importance scores for extracted facts
• Proceeding past verification phase
• Creating new entities vs linking to existing

Quality Checklist

• Inventory created before reading content
• Every source gets extraction record (none skipped)
• dedicated_to field populated for sources primarily about an entity
• Verification phase completed with user confirmation
• Dedicated source rule enforced (2+ dedicated = must appear proportionally)
• All sources filed via claudia memory document store
• Provenance chain complete (memories link to documents)
• No entity lost that had dedicated sources

Error Handling

If extraction fails for a source:

• Log the failure
• Continue with other sources
• Surface in verification phase
• Offer manual review option

If IngestService unavailable (no Ollama):

• Fall back to direct Claude extraction
• Slower but still systematic
• Same extraction schema applies

If verification fails:

• Do NOT proceed to storage
• Show which entities are missing
• Offer to re-extract specific sources
• User must explicitly override to continue

Extensibility

This workflow is schema-agnostic. Works for any source type:

Data Type	Detection	Extraction Mode
Meeting transcripts	.md, .txt with names	meeting
Email threads	Email headers	email
Documents/PDFs	.pdf, formal structure	document
Research notes	Mixed content	general
Slack exports	Message format	general
CRM exports	Structured records	general

Add new extraction modes to IngestService if needed, or use general mode which extracts: facts, entities, relationships, summary.

Tone

• Methodical: this is a systematic process
• Transparent: show progress at each phase
• Protective: catch errors before they become permanent
• Efficient: batch operations, clear status updates