Borrowing it
Nothing to install: this file belongs to mck-s/minirag-pgvector-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mck-s/minirag-pgvector-mcp/main/.claude/skills/ingest/SKILL.mdgit clone --depth 1 https://github.com/mck-s/minirag-pgvector-mcpWrote 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.
[](https://agentmods.dev/skills/mck-s/minirag-pgvector-mcp/ingest)<a href="https://agentmods.dev/skills/mck-s/minirag-pgvector-mcp/ingest"><img src="https://agentmods.dev/badge/skills/mck-s/minirag-pgvector-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.
<a href="https://agentmods.dev/skills/mck-s/minirag-pgvector-mcp/ingest"><img src="https://agentmods.dev/badge/skills/mck-s/minirag-pgvector-mcp/ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.00594 |
| Opus 5 | $0.00028 | $0.00297 |
| Sonnet 5 | $0.00011 | $0.00119 |
| Haiku 4.5 | $0.00006 | $0.00059 |
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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Distribute raw documents into the vector store: parse → chunk → embed → upsert. The evolution of your manual "ingest skill" that moved raw meeting minutes into the right folder — now it also chunks and embeds.
When to invoke
- New raw documents need to go into the vector store.
- You want to (re-)ingest your existing
context/repo. - A source changed and needs re-embedding.
Inputs
path— file or directory.doctype(optional) — one of the known types. If omitted, infer from the containing folder name (mirrors your folder-as-category convention).
Procedure
- Read-only survey first. List the files to be ingested and the doctype each will get. Print the plan. Do not write yet.
- Confirm the doctype mapping looks right (especially for inferred ones).
- For each file:
- Parse text; for markdown, capture
heading_pathper section. - Chunk using the strategy for its
doctype(seedocs/architecture.md). - Embed chunks with the active embedder.
- Compute
source_hash. If the document exists with the same hash → skip (idempotent). If it exists with a different hash → replace its chunks. - Upsert
documents+chunkswith metadata (doctype,source,heading_path,embedder_id,ingested_at).
- Parse text; for markdown, capture
- Report: per file — chunks created / skipped / replaced. Aggregate by doctype.
Guardrails
- Never embed with a model whose dimension ≠ the pgvector column dimension. Assert first.
- Never duplicate a source. Idempotency is keyed on
source_hash. - Do not invent doctypes silently. If a folder name doesn't map to a known doctype, surface it and ask rather than defaulting.
- Large batches: embed in batches; don't hold everything in memory.
Verification
After ingest, run:
SELECT doctype, count(*) FROM chunks GROUP BY doctype;
Counts should match the plan printed in step 1. Re-running the same ingest must not change counts.
Failure modes to watch (self-check)
- Chunking mid-code-block or mid-table (breaks meaning) → check the doctype strategy.
- Silent embedder swap (dimension mismatch or mixed
embedder_id) → refuse and report. - Over-trusting that "it ran" = "it's correct" → always print the verification query, don't assume.
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.
- 9d ago First seen · 52 lines · 0 tokens per session scan A fe590c37f070
ingest is a skill published in the GitHub repository mck-s/minirag-pgvector-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 594 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.