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chromadb-rag-workflow

chromadb-rag-workflow is a skill for Claude Code, Codex from woliveiras/geremmyas. It costs 52 tokens per session (314 once invoked), scanned A, original, MIT.

A workflow for storing documents in ChromaDB and finding relevant passages for an AI system. RAG, or retrieval-augmented generation, lets an AI look up source material before answering.

In plain words
What is it for?
Use it to design collections, document IDs and metadata, choose storage and embeddings, split documents into chunks, ingest data, build retrievers, test searches, and plan backups or re-embedding.
Why use it?
It helps keep document collections consistent, searchable, filterable, testable, and recoverable as source files or embedding models change.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 10repo 3mo ago A scan 52 tokens original MIT

Good fit Use it to design collections, document IDs and metadata, choose storage and embeddings, split documents into chunks, ingest data, build retrievers, test searches, and plan backups or re-embedding.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/woliveiras/geremmyas/chromadb-rag-workflow
View source ↗ woliveiras/geremmyas
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.

Any agent
npx skills add woliveiras/geremmyas --skill chromadb-rag-workflow
Clone the repo
git clone --depth 1 https://github.com/woliveiras/geremmyas

Made for: Claude Code, Codex.

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 chromadb-rag-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/woliveiras/geremmyas/chromadb-rag-workflow/github.svg)](https://agentmods.dev/skills/woliveiras/geremmyas/chromadb-rag-workflow)
Your own site
<a href="https://agentmods.dev/skills/woliveiras/geremmyas/chromadb-rag-workflow"><img src="https://agentmods.dev/badge/skills/woliveiras/geremmyas/chromadb-rag-workflow/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 chromadb-rag-workflow

Your own site · 80×15
<a href="https://agentmods.dev/skills/woliveiras/geremmyas/chromadb-rag-workflow"><img src="https://agentmods.dev/badge/skills/woliveiras/geremmyas/chromadb-rag-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 314 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 $0.00052 $0.00314
Opus 5.5 $0.00021 $0.00126
Sonnet 5.5 $0.00010 $0.00063
Haiku 4.5 $0.00005 $0.00031

Measured 2mo ago against content hash cdb8f95ea691, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

chromadb-rag-workflow 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 2mo 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.

content/skills/chromadb-rag-workflow/SKILL.md · 40 lines

What it actually says

ChromaDB RAG Workflow

Build Chroma-backed retrieval with stable collections, metadata, and evaluation.

Process

  1. Identify source documents, update cadence, tenancy/security boundaries, and retrieval success criteria.
  2. Choose PersistentClient or HttpClient based on deployment shape.
  3. Define collection naming, embedding function/model, document IDs, and metadata schema.
  4. Choose chunking and normalization before ingestion.
  5. Ingest with stable IDs and metadata needed for filters and debugging.
  6. Add retrieval tests for expected matches, metadata filters, no-match cases, and stale/deleted document behavior.
  7. Document backup, rebuild, and re-embedding strategy.
  8. Keep retrieval metrics or qualitative eval examples close to the feature.

Rules

  • Do not change embedding model for an existing collection without a re-embedding plan.
  • Do not delete/recreate persistent collections as normal cleanup.
  • Do not rely on vector similarity alone when tenant, document type, or freshness filters are known.
  • Keep source documents recoverable so vector data can be rebuilt.

Output

  • Collection and metadata design
  • Ingestion/retrieval flow
  • Persistence and backup plan
  • Retrieval evaluation checklist
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. 2mo ago First seen · 40 lines · 52 tokens per session scan A cdb8f95ea691

Subscribe to this mod's changes

chromadb-rag-workflow is a skill published in the GitHub repository woliveiras/geremmyas (10 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 314 once invoked, about $0.0002 per session on Opus 5.5. 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.

Data and AIRAG and vectors
Related

chromadb-rag-workflow compared with skills from other repositories

chromadb-rag-workflow next to 6 skills from other repositories
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9router-embeddingsdecolua/9routerSkill32k9d agoMITA66Claude Code, Codex
embedding-strategiesforyourhealth111-pixel/Vibe-SkillsSkill3.8k3d agoApache-2.0A37Claude Code, Codex
similarity-search-patternsforyourhealth111-pixel/Vibe-SkillsSkill3.8k3d agoApache-2.0A30Claude Code, Codex