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Agent skill

Create Retrieval Plugin

by NomaDamas in NomaDamas/AutoRAG-Research

Guide developers through creating a custom retrieval pipeline plugin for AutoRAG-Research.

Apache-2.0Auto-check: notesAI & LLM Engineering

Install Create Retrieval Plugin

skills CLI
$ npx skills add NomaDamas/AutoRAG-Research --skill create-retrieval-plugin -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NomaDamas/AutoRAG-Research create-retrieval-plugin --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NomaDamas/AutoRAG-Research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/create-retrieval-plugin .claude/skills/create-retrieval-plugin && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
create-retrieval-plugin
GitHub stars
149
Token cost
~728 tokens
SKILL.md length
211 words
Files
1
Skills in repo
7
Repo updated
First seen
Licence
Apache-2.0

At a glance

Guide developers through creating a custom retrieval pipeline plugin for AutoRAG-Research.

  • Works in 3 steps: Scaffold → Implement → Write tests and install
  • Building a new search/retrieval strategy (e.g.
  • SKILL.md covers Workflow, Key Files and Examples
  • Calls pip

What it does

Create Retrieval Plugin is an agent skill from NomaDamas/AutoRAG-Research. Guide developers through creating a custom retrieval pipeline plugin for AutoRAG-Research. Walks through scaffolding, implementing BaseRetrievalPipeline methods, writing YAML configs, testing, and installing. Use when building a new search/retrieval strategy (e.g., Elasticsearch, ColBERT, custom vector search).

Its SKILL.md is about 730 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Vector databases and Project scaffolding. It works with Elasticsearch. The repository describes itself as: Automate your RAG research. The licence is Apache-2.0.

When your agent uses it

  • Building a new search/retrieval strategy (e.g.
  • Custom vector search)

Example prompts

  • “/create-retrieval-plugin”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read, Write, Edit

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Scaffold
  2. Implement
  3. Write tests and install

What it can do on your machine

Read from SKILL.md and the folder at commit a473cf0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Create Retrieval Plugin loads about 728 tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 211 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~728

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Edit

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NomaDamas/AutoRAG-Research at commit a473cf0, republished under its Apache-2.0 licence (© NomaDamas). 211 words, ~728 tokens.

Download SKILL.mdSave it as .claude/skills/create-retrieval-plugin/SKILL.md (or your agent's skills folder).
name
create-retrieval-plugin
description
Guide developers through creating a custom retrieval pipeline plugin for AutoRAG-Research. Walks through scaffolding, implementing BaseRetrievalPipeline methods, writing YAML configs, testing, and installing. Use when building a new search/retrieval strategy (e.g., Elasticsearch, ColBERT, custom vector search).
allowed-tools
Bash, Read, Write, Edit

Create Retrieval Plugin

Workflow

1. Scaffold
bash
autorag-research plugin create my_search --type=retrieval

Read the generated pipeline.py, pyproject.toml, YAML config, and test file to understand the structure.

2. Implement

For the shared pipeline implementation and testing rules, read:

  • ai_instructions/pipeline_implementer.md
  • ai_instructions/pipeline_test_writer.md
  • ai_instructions/pipeline_architecture_mapper.md

Implement the two abstract methods in the pipeline class:

  • _retrieve_by_id(query_id, top_k) — retrieve using query ID (query exists in DB with stored embedding)
  • _retrieve_by_text(query_text, top_k) — retrieve using raw text (may need on-the-fly embedding)

Both must return list[dict[str, Any]] with doc_id (chunk ID) and score keys.

DO NOT add your own asyncio.gather, asyncio.Semaphore, or any concurrency control. The base pipeline's run() already handles parallel execution of all queries via run_with_concurrency_limit() (semaphore + gather), controlled by the max_concurrency config parameter. Your method is called once per single query — just implement the retrieval logic for that one query.

Custom parameters: Add fields to your config class and pass them via get_pipeline_kwargs() → accept them in the pipeline constructor. See bm25.py for a real example.

3. Write tests and install
bash
cd my_search_plugin
pip install -e .   # or: uv pip install -e .
cd .. && autorag-research plugin sync

Verify: ls configs/pipelines/retrieval/my_search.yaml

Key Files

PurposePath
Base config classautorag_research/config.py → BaseRetrievalPipelineConfig
Base pipeline classautorag_research/pipelines/retrieval/base.py → BaseRetrievalPipeline
Service layerautorag_research/orm/service/retrieval_pipeline.py → RetrievalPipelineService
Plugin entry point discoveryautorag_research/plugin_registry.py

Examples

Study these existing implementations for patterns:

  • autorag_research/pipelines/retrieval/bm25.py — BM25 retrieval (simple)
  • autorag_research/pipelines/retrieval/vector_search.py — Vector similarity search
  • autorag_research/pipelines/retrieval/hybrid.py — Hybrid (BM25 + vector)
  • autorag_research/pipelines/retrieval/hyde.py — HyDE (Hypothetical Document Embeddings)
  • YAML configs: configs/pipelines/retrieval/bm25.yaml, configs/pipelines/retrieval/vector_search.yaml

© NomaDamas, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/create-retrieval-plugin of NomaDamas/AutoRAG-Research.

Open the folder on GitHubat commit a473cf0

Compare with similar skills

Create Retrieval Plugin next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Create Retrieval Plugin compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Create Retrieval Plugin this skillNomaDamas/AutoRAG-Research149—~728Automated safety check: NotesApache-2.0
Amazon Opensearch Serviceaws/agent-toolkit-for-aws2.8k—~2.4kAutomated safety check: PassApache-2.0
Azure AI Search Python SDKmicrosoft/skills3.1k6 repos~4.4kAutomated safety check: PassMIT
RAG Implementationwshobson/agents40k10 repos~1.1kAutomated safety check: PassMIT
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence
Similarity Search Patternswshobson/agents40k11 repos~577Automated safety check: PassMIT

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Works with

Categories

Questions about Create Retrieval Plugin

What does Create Retrieval Plugin do?

Guide developers through creating a custom retrieval pipeline plugin for AutoRAG-Research. Create Retrieval Plugin is an agent skill from NomaDamas/AutoRAG-Research. Guide developers through creating a custom retrieval pipeline plugin for AutoRAG-Research.

When should I use Create Retrieval Plugin?

Create Retrieval Plugin fits situations like: building a new search/retrieval strategy (e.g; custom vector search).

How do I install Create Retrieval Plugin in Claude Code?

Run `npx skills add NomaDamas/AutoRAG-Research --skill create-retrieval-plugin -a claude-code`. Or copy the skill folder (.agents/skills/create-retrieval-plugin in NomaDamas/AutoRAG-Research) into .claude/skills/create-retrieval-plugin in your project. Claude Code loads it when a task matches its description.

How do I install Create Retrieval Plugin in Codex?

Run `npx skills add NomaDamas/AutoRAG-Research --skill create-retrieval-plugin -a codex`. Or copy the skill folder (.agents/skills/create-retrieval-plugin in NomaDamas/AutoRAG-Research) into .agents/skills/create-retrieval-plugin in your project. Codex loads it when a task matches its description.

Can I use Create Retrieval Plugin in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NomaDamas/AutoRAG-Research --skill create-retrieval-plugin -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-retrieval-plugin, .gemini/skills/create-retrieval-plugin, .github/skills/create-retrieval-plugin and .opencode/skills/create-retrieval-plugin in your project.

What does Create Retrieval Plugin need to run?

Going by SKILL.md and its folder, Create Retrieval Plugin needs the command-line tools its instructions call (pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read, Write, Edit.

Does Create Retrieval Plugin access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Create Retrieval Plugin safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Create Retrieval Plugin use?

Create Retrieval Plugin is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Create Retrieval Plugin use?

About 728 tokens (SKILL.md is roughly 2.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Create Retrieval Plugin?

Skills that share tags, products or a category with Create Retrieval Plugin: Amazon Opensearch Service (aws/agent-toolkit-for-aws, 2.8k stars), Azure AI Search Python SDK (microsoft/skills, 3.1k stars), RAG Implementation (wshobson/agents, 40k stars) and Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create Retrieval Plugin?

NomaDamas (a GitHub organization) maintains it in NomaDamas/AutoRAG-Research, which has 149 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on August 9, 2026.

Source: NomaDamas/AutoRAG-Research on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.