# Knowledge Batch Ingest

> Integrate an entire folder of notes and documents at once. Groups files thematically, presents proposals per group, and processes after user confirmation. Uses entity cache across files for token efficiency.

- **Type:** Skill
- **Install:** `agentstack add skill-pssah4-vault-operator-knowledge-batch-ingest`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [pssah4](https://agentstack.voostack.com/s/pssah4)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [pssah4](https://github.com/pssah4)
- **Source:** https://github.com/pssah4/vault-operator/tree/main/bundled-skills/knowledge-batch-ingest
- **Website:** https://pssah4.github.io/vault-operator/

## Install

```sh
agentstack add skill-pssah4-vault-operator-knowledge-batch-ingest
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Batch Ingest

Integrate multiple notes and documents from a folder in one workflow.
Token-efficient: shares entity lookups across files (search once, link many).

FEATURE-2005, EPIC-020.

## Step 1: ASK THE USER

Ask: "How would you like to handle this batch?"

- **Group review (recommended):** I analyze all files, group them by topic, and show you each group for confirmation before making changes.
- **Individual review:** I process each file like a single ingest and ask you before each one.
- **Quick mode:** I analyze all files, show you a summary, and proceed after one confirmation. Best for simple notes with clear topics.

Wait for the user's answer.

## Step 2: SCAN (deterministic, no LLM cost)

Use `list_files` on the target folder. For each file:

1. Check file type (markdown, PDF, DOCX, XLSX, PPTX)
2. For markdown: read frontmatter with `read_file` (first 20 lines only)
3. Check which properties are already set (Themen, Konzepte, Zusammenfassung, etc.)
4. Detect duplicates: if a note with the same title already exists in the vault

Report to the user:
```
Found {N} files in {folder}:
- {X} markdown notes ({Y} with complete frontmatter, {Z} need integration)
- {A} PDFs
- {B} Office documents
- {C} already integrated (skipping)
- {D} potential duplicates (will flag)

Estimated cost: ~${N * 0.10}-${N * 0.15} (entity cache reduces redundant searches)
```

## Step 3: ANALYZE AND GROUP

For each file that needs integration:

1. Read the content (markdown: `read_file`, documents: `read_document`)
2. Identify entities (topics, concepts, persons)
3. **Entity cache:** Before calling `semantic_search`, check the cache:
   - Cache hit: Reuse the previous search result (0 tokens)
   - Cache miss: Run `semantic_search`, store result in cache
4. Group files by their primary topic/cluster

Present groups to the user (in group-review mode):
```
Group 1: "AI Ethics" (5 files)
  - ethics-of-ai.md -> Themen: [[KI]], [[Ethik]]
  - responsible-ai.pdf -> New source note, Themen: [[KI]]
  - fairness-metrics.md -> Konzepte: [[Fairness]]
  ...

Group 2: "Project Management" (3 files)
  - sprint-retro-march.md -> Themen: [[Projektmanagement]]
  ...

Shall I proceed with Group 1?
```

## Step 4: PROCESS (after confirmation)

For each confirmed group:

1. Create a checkpoint (for undo)
2. For each file in the group:
   - **PDF/Office:** Use `ingest_document` (attaches full text automatically)
   - **Markdown:** Use `update_frontmatter` for properties, `write_file` only if body needs wikilinks
3. Create stub notes for genuinely new entities (same rules as knowledge-ingest: no dangling wikilinks)
4. Report progress after each file

After the group is done:
```
Group 1 complete: 5 files integrated, 2 stub notes created.
Proceed to Group 2, or undo Group 1?
```

## Step 5: SUMMARY

After all groups are processed:
```
Batch ingest complete:
- {N} files integrated
- {M} stub notes created
- {K} entities reused from cache (saved ~{K * 0.02} tokens)
- {G} groups, each with a checkpoint for undo

Next: Run vault health check to verify graph integrity?
```

## Entity Cache Rules

- In-memory only, not persisted between sessions
- Key: normalized entity name (lowercase, trimmed)
- Value: { path: string, exists: boolean } from semantic_search result
- Invalidate entry when a stub note is created for that entity
- Max 1000 entries (unlikely to exceed in one batch)

## Critical Rules

1. **NEVER modify existing notes without user confirmation** -- linking is thinking
2. **ALWAYS present proposals before making changes** -- no autonomous batch processing
3. **ONE checkpoint per group** -- each group independently reversible
4. **Prefer existing entities** -- the entity cache enforces this across the batch
5. **No AI slop** -- if unsure about an entity match, ask rather than guess
6. **Report cost** -- show estimated vs. actual token cost at the end

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [pssah4](https://github.com/pssah4)
- **Source:** [pssah4/vault-operator](https://github.com/pssah4/vault-operator)
- **License:** Apache-2.0
- **Homepage:** https://pssah4.github.io/vault-operator/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-pssah4-vault-operator-knowledge-batch-ingest
- Seller: https://agentstack.voostack.com/s/pssah4
- Browse the marketplace: https://agentstack.voostack.com/browse

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