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SKILL verified Apache-2.0 Self-run

Knowledge Batch Ingest

skill-pssah4-vault-operator-knowledge-batch-ingest · by pssah4

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.

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Install

$ agentstack add skill-pssah4-vault-operator-knowledge-batch-ingest

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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no reviews yet
3mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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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
  1. 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
  1. Create stub notes for genuinely new entities (same rules as knowledge-ingest: no dangling wikilinks)
  2. 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.

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

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Versions

  • v0.1.0 Imported from the upstream source.