Agent skill

Repository Harvesting Guide

by wentorai in wentorai/research-plugins

Harvest metadata from open repositories using OAI-PMH protocol

MITAuto-check passedData & Analytics

Install Repository Harvesting Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill repository-harvesting-guide -a claude-code

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

GitHub CLI
$ gh skill install wentorai/research-plugins repository-harvesting-guide --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/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tools/scraping/repository-harvesting-guide .claude/skills/repository-harvesting-guide && 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
repository-harvesting-guide
GitHub stars
298
Used in
1 other repo
Token cost
~2.4k tokens
SKILL.md length
166 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Harvest metadata from open repositories using OAI-PMH protocol

  • Tasks that involve Web scraping
  • SKILL.md covers OAI-PMH Protocol Fundamentals, Building a Harvester in Python, Selective Harvesting and Data Quality and Deduplication, plus 1 more section
  • Reaches arxiv.org and openarchives.org
  • Tasks that involve Data cleaning

What it does

Repository Harvesting Guide is an agent skill from wentorai/research-plugins. Harvest metadata from open repositories using OAI-PMH protocol

Its SKILL.md is about 2.4k 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 Data & Analytics, covering Web scraping and Data cleaning. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Web scraping
  • Tasks that involve Data cleaning

Example prompts

  • “/repository-harvesting-guide”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • arxiv.org
    • openarchives.org
    • purl.org
    • export.arxiv.org
    • ncbi.nlm.nih.gov
    • oai.europeana.eu
    • api.archives-ouvertes.fr
    • dblp.org
    • citeseerx.ist.psu.edu
    • v2.sherpa.ac.uk

    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

Repository Harvesting Guide loads about 2.4k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 166 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

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 passed

The automated check found no risky patterns in SKILL.md.

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 wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 166 words, ~2,447 tokens.

Download SKILL.mdSave it as .claude/skills/repository-harvesting-guide/SKILL.md (or your agent's skills folder).
name
repository-harvesting-guide
description
Harvest metadata from open repositories using OAI-PMH protocol

Repository Harvesting Guide

A skill for harvesting metadata from open access repositories using the OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) protocol. Covers protocol fundamentals, building harvesters in Python, handling resumption tokens for large collections, metadata format parsing (Dublin Core, MARC, METS), selective harvesting by date and set, and integrating harvested data into research workflows.

OAI-PMH Protocol Fundamentals

What Is OAI-PMH

OAI-PMH is a standardized protocol that allows metadata to be harvested from repository systems. It is the backbone of library interoperability and is supported by virtually every institutional repository, preprint server, and digital library worldwide.

OAI-PMH Architecture:

Data Providers (repositories):
  - Expose metadata through a standardized HTTP interface
  - Must support Dublin Core as minimum metadata format
  - May support additional formats (MARC, MODS, DataCite, etc.)
  - Examples: arXiv, PubMed Central, DSpace repositories,
    EPrints, institutional repositories

Service Providers (harvesters):
  - Send HTTP requests to data providers
  - Collect, aggregate, and index metadata
  - Build search services, union catalogs, analytics
  - Examples: BASE (Bielefeld), CORE, OpenDOAR

Protocol Version: 2.0 (current, since 2002)
Transport: HTTP GET or POST
Response format: XML
Base URL example: https://arxiv.org/oai2
Six OAI-PMH Verbs
OAI-PMH defines exactly six request types (verbs):

1. Identify
   Purpose: Describe the repository
   URL: baseURL?verb=Identify
   Returns: repository name, admin email, earliest datestamp,
            granularity, compression support

2. ListMetadataFormats
   Purpose: List available metadata formats
   URL: baseURL?verb=ListMetadataFormats
   Returns: format prefixes (oai_dc, marc21, datacite, etc.)
   Optional: identifier parameter to check formats for one record

3. ListSets
   Purpose: List available sets (collections/categories)
   URL: baseURL?verb=ListSets
   Returns: set names and specs for selective harvesting
   Example sets: physics:hep-th, cs:AI, math:AG

4. ListIdentifiers
   Purpose: List record identifiers (headers only, no metadata)
   URL: baseURL?verb=ListIdentifiers&metadataPrefix=oai_dc
   Optional: from, until, set parameters
   Returns: identifiers, datestamps, set memberships

5. ListRecords
   Purpose: Harvest full metadata records
   URL: baseURL?verb=ListRecords&metadataPrefix=oai_dc
   Optional: from, until, set parameters
   Returns: complete metadata records in requested format

6. GetRecord
   Purpose: Retrieve a single record by identifier
   URL: baseURL?verb=GetRecord&identifier=oai:arxiv:2301.00001
         &metadataPrefix=oai_dc
   Returns: one complete metadata record

Building a Harvester in Python

Basic Harvester
python
import requests
import xml.etree.ElementTree as ET
import time

OAI_NS = "http://www.openarchives.org/OAI/2.0/"
DC_NS = "http://purl.org/dc/elements/1.1/"

def harvest_records(base_url, metadata_prefix="oai_dc",
                    from_date=None, until_date=None,
                    set_spec=None):
    """
    Harvest all records from an OAI-PMH endpoint.
    Handles resumption tokens for paginated results.

    Args:
        base_url: OAI-PMH base URL
        metadata_prefix: metadata format (default: oai_dc)
        from_date: selective harvest start (YYYY-MM-DD)
        until_date: selective harvest end (YYYY-MM-DD)
        set_spec: restrict to a specific set
    """
    params = {
        "verb": "ListRecords",
        "metadataPrefix": metadata_prefix,
    }

    if from_date:
        params["from"] = from_date
    if until_date:
        params["until"] = until_date
    if set_spec:
        params["set"] = set_spec

    all_records = []
    request_count = 0

    while True:
        response = requests.get(base_url, params=params, timeout=30)
        response.raise_for_status()
        request_count += 1

        root = ET.fromstring(response.content)

        # Parse records from this page
        records = root.findall(
            f".//{{{OAI_NS}}}record"
        )

        for record in records:
            parsed = parse_dublin_core(record)
            if parsed:
                all_records.append(parsed)

        # Check for resumption token
        token_elem = root.find(
            f".//{{{OAI_NS}}}resumptionToken"
        )

        if token_elem is not None and token_elem.text:
            params = {
                "verb": "ListRecords",
                "resumptionToken": token_elem.text,
            }
            # Polite delay between requests
            time.sleep(2)
        else:
            break

    print(f"Harvested {len(all_records)} records "
          f"in {request_count} requests")
    return all_records


def parse_dublin_core(record_element):
    """
    Parse a Dublin Core metadata record into a dictionary.
    """
    header = record_element.find(f"{{{OAI_NS}}}header")
    metadata = record_element.find(f"{{{OAI_NS}}}metadata")

    if header is None or metadata is None:
        return None

    # Check if record is deleted
    status = header.get("status", "")
    if status == "deleted":
        return None

    identifier = header.findtext(f"{{{OAI_NS}}}identifier", "")
    datestamp = header.findtext(f"{{{OAI_NS}}}datestamp", "")

    dc = metadata.find(f".//{{{DC_NS}}}../")

    result = {
        "oai_identifier": identifier,
        "datestamp": datestamp,
        "title": find_dc_text(metadata, "title"),
        "creator": find_dc_all(metadata, "creator"),
        "subject": find_dc_all(metadata, "subject"),
        "description": find_dc_text(metadata, "description"),
        "date": find_dc_text(metadata, "date"),
        "type": find_dc_text(metadata, "type"),
        "identifier": find_dc_all(metadata, "identifier"),
        "language": find_dc_text(metadata, "language"),
        "rights": find_dc_text(metadata, "rights"),
    }

    return result


def find_dc_text(metadata, element_name):
    """Find first Dublin Core element text."""
    elem = metadata.find(f".//{{{DC_NS}}}{element_name}")
    return elem.text if elem is not None else ""


def find_dc_all(metadata, element_name):
    """Find all values of a Dublin Core element."""
    elems = metadata.findall(f".//{{{DC_NS}}}{element_name}")
    return [e.text for e in elems if e.text]

Selective Harvesting

By Date Range
Incremental harvesting strategy:

First harvest: Get everything
  from_date = None (or repository's earliestDatestamp)
  until_date = today

Subsequent harvests: Get only new/modified records
  from_date = last_harvest_date
  until_date = today

Date granularity:
  - Day-level: YYYY-MM-DD (most common)
  - Second-level: YYYY-MM-DDThh:mm:ssZ (some repositories)
  - Check the Identify response for supported granularity

Important: OAI-PMH datestamps reflect the date the METADATA
was last modified, not the publication date. A record edited
yesterday to fix a typo will appear in a harvest with
from=yesterday, even if the paper was published in 2015.
By Set (Collection)
Common set structures by repository type:

arXiv:
  physics, physics:hep-th, cs, cs:AI, math, math:AG, etc.

DSpace repositories:
  com_12345_1 (community), col_12345_2 (collection)
  Hierarchical: department -> collection

PubMed Central:
  By journal: pmc-journal-name
  By funder: pmc-funder-name

Strategy:
  1. Call ListSets to see available sets
  2. Identify sets relevant to your research topic
  3. Harvest only those sets to reduce data volume
  4. Store the set membership for each record

Data Quality and Deduplication

Common Quality Issues
Quality problems in harvested metadata:

1. Duplicate records:
   - Same paper in multiple repositories
   - Same paper in multiple sets within one repository
   - Solution: Deduplicate by DOI, then by title similarity

2. Incomplete metadata:
   - Missing abstracts (very common)
   - Missing author identifiers
   - Missing dates or using inconsistent date formats
   - Solution: Enrich with Crossref or OpenAlex lookups

3. Encoding issues:
   - Non-UTF-8 characters in older repositories
   - HTML entities in text fields
   - Solution: Normalize encoding, strip HTML tags

4. Inconsistent formats:
   - Dates as "2023", "2023-01", "2023-01-15", "January 2023"
   - Author names as "Smith, John" vs "John Smith" vs "J. Smith"
   - Solution: Parse and normalize to canonical formats

Notable OAI-PMH Endpoints

Major repositories with OAI-PMH support:

arXiv:          https://export.arxiv.org/oai2
PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/oai/oai.cgi
Europeana:      https://oai.europeana.eu/oai
HAL (France):   https://api.archives-ouvertes.fr/oai/hal
DBLP:           https://dblp.org/oai
CiteSeerX:      https://citeseerx.ist.psu.edu/oai2

To find more endpoints:
  - OpenDOAR directory: https://v2.sherpa.ac.uk/opendoar/
  - ROAR (Registry of Open Access Repositories)
  - BASE (Bielefeld Academic Search Engine) source list

OAI-PMH harvesting remains the most reliable method for building comprehensive metadata collections from open repositories. While newer APIs like ResourceSync and Signposting offer richer functionality, OAI-PMH's universal adoption and simplicity make it the practical choice for most academic metadata collection tasks.

© wentorai, MIT. 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 skills/tools/scraping/repository-harvesting-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Repository Harvesting Guide 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.

Repository Harvesting Guide compared with similar skills
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Cashclaw Data Scraperertugrulakben/cashclaw303—~3kAutomated safety check: PassMIT
Data Cleaningericrisco/rsc-harness180—~3.6kAutomated safety check: PassMIT
Glue DiagnosticsKilo-Org/kilo-marketplace190—~2kAutomated safety check: PassMIT
Minerals Web Ingestlamm-mit/scienceclaw246—~512Automated safety check: PassApache-2.0

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Questions about Repository Harvesting Guide

What does Repository Harvesting Guide do?

Harvest metadata from open repositories using OAI-PMH protocol. Repository Harvesting Guide is an agent skill from wentorai/research-plugins.

When should I use Repository Harvesting Guide?

Repository Harvesting Guide fits situations like: tasks that involve Web scraping; tasks that involve Data cleaning.

How do I install Repository Harvesting Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill repository-harvesting-guide -a claude-code`. Or copy the skill folder (skills/tools/scraping/repository-harvesting-guide in wentorai/research-plugins) into .claude/skills/repository-harvesting-guide in your project. Claude Code loads it when a task matches its description.

How do I install Repository Harvesting Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill repository-harvesting-guide -a codex`. Or copy the skill folder (skills/tools/scraping/repository-harvesting-guide in wentorai/research-plugins) into .agents/skills/repository-harvesting-guide in your project. Codex loads it when a task matches its description.

Can I use Repository Harvesting Guide 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 wentorai/research-plugins --skill repository-harvesting-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/repository-harvesting-guide, .gemini/skills/repository-harvesting-guide, .github/skills/repository-harvesting-guide and .opencode/skills/repository-harvesting-guide in your project.

What does Repository Harvesting Guide need to run?

SKILL.md names no scripts, command-line tools or credentials: Repository Harvesting Guide is instructions for the agent only. Our summary lists: Python 3.

Does Repository Harvesting Guide access the network?

SKILL.md names 10 domains. In commands or code: arxiv.org, openarchives.org, purl.org, export.arxiv.org, ncbi.nlm.nih.gov, oai.europeana.eu, api.archives-ouvertes.fr, dblp.org, citeseerx.ist.psu.edu and v2.sherpa.ac.uk; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Repository Harvesting Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Repository Harvesting Guide use?

Repository Harvesting Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Repository Harvesting Guide use?

About 2.4k tokens (SKILL.md is roughly 9.8k 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 Repository Harvesting Guide?

Skills that share tags, products or a category with Repository Harvesting Guide: Authoritative Data Harvester (yushui2022/MathModel-Skill, 454 stars), Cashclaw Data Scraper (ertugrulakben/cashclaw, 303 stars), Data Cleaning (ericrisco/rsc-harness, 180 stars) and Glue Diagnostics (Kilo-Org/kilo-marketplace, 190 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Repository Harvesting Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

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