ingest

ingest is a command for Claude Code from Galbaz1/video-research-mcp. It costs 10 tokens per session (798 once invoked), scanned A, original, MIT.

A command for manually adding research notes, document analyses, concepts, relationships, video notes, or meeting notes to a Weaviate knowledge store. Weaviate is a database designed for searching stored information by meaning and content.

In plain words
What is it for?
Use it to choose the appropriate knowledge collection and add a finding, analysis, concept, relationship, video note, or call note after checking that the store is available.
Why use it?
It helps keep useful findings and concepts in a central searchable store instead of leaving them only in a conversation or document.

Command for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; names the AskUserQuestion tool.

Part of the gr plugin — 12 skills, 17 commands, 7 agents shipped together

Good fit Use it to choose the appropriate knowledge collection and add a finding, analysis, concept, relationship, video note, or call note after checking that the store is available.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/galbaz1/video-research-mcp/ingest
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/Galbaz1/video-research-mcp

Made for: Claude Code.

Or install gr, the plugin that ships this one along with the rest of its 12 skills, 17 commands, 7 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ingest

README.md
[![agentmods](https://agentmods.dev/badge/commands/galbaz1/video-research-mcp/ingest/github.svg)](https://agentmods.dev/commands/galbaz1/video-research-mcp/ingest)
Your own site
<a href="https://agentmods.dev/commands/galbaz1/video-research-mcp/ingest"><img src="https://agentmods.dev/badge/commands/galbaz1/video-research-mcp/ingest/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ingest

Your own site · 80×15
<a href="https://agentmods.dev/commands/galbaz1/video-research-mcp/ingest"><img src="https://agentmods.dev/badge/commands/galbaz1/video-research-mcp/ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 10 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 798 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00010 $0.00798
Opus 5 $0.00005 $0.00399
Sonnet 5 $0.00002 $0.00160
Haiku 4.5 $0.00001 $0.00080

Measured 10d ago against content hash b4b647d63332, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

ingest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/ingest.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ingest: $ARGUMENTS

Manually add entries to the Weaviate knowledge store.

Phase 0: Determine Intent

If $ARGUMENTS is empty, ask the user:

AskUserQuestion:
  questions:
    - question: "What do you want to add to the knowledge store?"
      header: "Ingest"
      multiSelect: false
      options:
        - label: "Add a finding or insight"
          description: "Store a research finding, insight, or fact with evidence tier"
        - label: "Add content analysis"
          description: "Store an analysis of a document, URL, or text"
        - label: "Add a concept"
          description: "Store a concept with its definition and relationships"
        - label: "Describe what to add"
          description: "Free-form description — I'll pick the right collection"

Phase 1: Pick Collection

Map the user's intent to the right collection. Call knowledge_stats() first to verify Weaviate is available.

Intent Collection
Finding/insight ResearchFindings
Content analysis ContentAnalyses
Video note VideoAnalyses
Concept ConceptKnowledge
Relationship RelationshipEdges
Meeting/call note CallNotes

If $ARGUMENTS includes a collection name, use it directly.

Always call knowledge_schema(collection="<name>") to get the exact property names and types. Do not guess property names — the schema is the source of truth.

Phase 2: Gather Properties

If the user provided text or a file path in $ARGUMENTS:

  1. Read the content
  2. Extract the relevant properties automatically
  3. Show the user what will be ingested and ask for confirmation

If no content provided, ask the user for the required properties interactively.

Phase 3: Ingest

Call knowledge_ingest(collection="<name>", properties={...}).

On success: report the object UUID and offer:

  • "Search for it: /gr:recall <topic>"
  • "Add another entry"
  • "Add related concepts or relationships"

On error:

  • Schema validation error: The error message lists allowed name:type pairs. Call knowledge_schema(collection="<name>") for full details, then retry with correct properties.
  • Weaviate not configured: "Set up Weaviate first: /skill weaviate-setup"

Read the full file on GitHub · 93 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 93 lines · 10 tokens per session scan A b4b647d63332

Subscribe to this mod's changes

ingest is a command published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 1mo ago), licensed MIT. It adds 10 tokens to every session and 798 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.