Keyword Research Harvest

Community

zhongzhx

keyword-research-harvest

Use when the user wants a reusable local workflow to search scholarly APIs for any topic keywords, build a candidate table, download accessible PDFs or HTML/XML full texts, run a second-pass HTML-to-PDF attempt, and deduplicate the downloaded files. Best for keyword-driven research-article harvesting that should be reproducible and saved into a project folder.

Overview

Publisher	zhongzhx
Repository	`literature-harvest`
Skill name	`keyword-research-harvest`
Stars	74
Forks	5
Bundled files	17
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
17 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by zhongzhx on GitHub. Read the source before you install it.

Installation

Install the Keyword Research Harvest AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/zhongzhx/literature-harvest/tree/main
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/zhongzhx/literature-harvest.git \
  .claude/skills/keyword-research-harvest

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Keyword Research Harvest in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Keyword Research Harvest on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Keyword Research Harvest is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Keyword Research Harvest

Use this skill when the user gives a topic or keyword set and wants a local literature-harvesting workflow, not a one-off manual search.

What this skill does

Bundles its own literature_harvest/scripts stack, including search_pubmed.py, search_europepmc.py, search_crossref.py, search_openalex.py, merge_and_deduplicate.py, download_fulltexts.py, and harvest_utils.py.
Searches PubMed/PMC, Europe PMC, Crossref, and OpenAlex through the bundled pipeline.
Builds a no-dedup candidate table first.
Downloads all legally accessible files to a new run folder.
Saves HTML/XML when PDF is not directly available.
Runs a second pass to chase PDF links from saved HTML pages.
Produces a deduplicated file folder and manifest after downloading.

Dependency model

This skill is self-contained. The target project does not need to already contain:

literature_harvest/
search_pubmed.py
download_fulltexts.py

The bundled copies live under:

literature_harvest/scripts/

Files in this skill

scripts/run_keyword_harvest_no_dedup.py Use this to launch a new broad keyword harvest run into a new run folder.
scripts/continue_download_and_dedup.py Use this to resume pending downloads, try HTML-to-PDF second pass, and build a deduplicated download set.
literature_harvest/scripts/ Bundled search and download dependencies copied from a working local harvest stack so the skill is portable.
references/config_template.json Copy and edit this for the topic-specific query set and filtering terms.
references/prompt_template.md Reusable prompt for another AI/agent.

Recommended workflow

Choose any output parent folder where the new harvest run folder should be created.
Copy references/config_template.json and edit it for the user's topic keywords.
Run scripts/run_keyword_harvest_no_dedup.py with:
- --output-root
- --config
- --run-name
If the job is large, rerun with the same --run-name and --skip-search to avoid repeating API search.
Run scripts/continue_download_and_dedup.py --run-root <run-folder> --retry-failed.
Report:
- candidate count
- downloaded file count
- true PDF count
- HTML/XML count
- remaining pending
- deduplicated keep count

Constraints

Do not silently drop failed downloads.
Keep metadata even when download fails.
Prefer original research when the config asks for it, but do not hard-code one domain such as Aspergillus.
Do not claim HTML/XML is PDF.
Treat second-pass cluster-like or support-like annotations as auxiliary only; downloading remains the primary task.

When to read references

Read references/config_template.json before preparing a new run config.
Read references/prompt_template.md when the user wants to hand this skill to another AI/agent.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Keyword Research Harvest AI skill do?

Why use Keyword Research Harvest on TypingMind?

Because you install it once and use it with any model. Keyword Research Harvest is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Keyword Research Harvest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhongzhx/literature-harvest/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Keyword Research Harvest?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Keyword Research Harvest?

As many as you like. As long as a model supports skills, you can use Keyword Research Harvest with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Keyword Research Harvest AI skill free?

Yes. It is published on GitHub by zhongzhx under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Keyword Research Harvest

What this skill does

Dependency model

Files in this skill

Recommended workflow

Constraints

When to read references

Bundled files

Frequently asked questions

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