Tac2017 Adr

Community

QSong-github

tac2017-adr

Query TAC 2017 ADR annotated drug labels for adverse drug reactions. Use whenever the user asks about ADRs extracted from FDA drug labels, MedDRA-normalized adverse reactions, or wants to look up a drug name, ADR string, or MedDRA code in the TAC 2017 ADR corpus.

Overview

Publisher	QSong-github
Repository	`DrugClaw`
Skill name	`tac2017-adr`
Stars	118
Forks	3
Bundled files	4
Links	Skill source Repository Homepage

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.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by QSong-github on GitHub. Read the source before you install it.

Installation

Install the Tac2017 Adr 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/QSong-github/DrugClaw/tree/main/skills/drug_nlp/tac2017
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/QSong-github/DrugClaw.git /tmp/DrugClaw
mkdir -p .claude/skills
cp -r /tmp/DrugClaw/skills/drug_nlp/tac2017 .claude/skills/tac2017-adr

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

Use it in TypingMind

Enable Tac2017 Adr 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 Tac2017 Adr 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 Tac2017 Adr 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.

TAC 2017 ADR Query Skill

Search 200 FDA drug labels annotated with adverse reactions, severity, and MedDRA normalization from the TAC 2017 shared task.

Entity Auto-detection

Input Pattern	Detected As	Match Logic
`10019211` (8 digits)	MedDRA ID	exact on `meddra_pt_id` or `meddra_llt_id`
`ACTEMRA` (known drug)	Drug name	exact (case-insensitive) on drug label name
`headache` (known ADR)	ADR string	exact on ADR reaction string
anything else	Free text	substring on drug names, ADR strings, MedDRA PT/LLT names

API

Function	Input	Returns
`search(entity)`	single entity string	`list[dict]` — matching label hit(s)
`search_batch(entities)`	list of entity strings	`dict[str, list[dict]]`
`summarize(hits, entity)`	hit list + query label	compact LLM-readable text
`to_json(hits)`	hit list	`list[dict]` (JSON-serializable)
`list_drugs()`	—	sorted list of all drug names
`stats()`	—	dataset-level statistics dict

Hit Dict Structure

Each hit returned by search() contains:

Field	Type	Description
`drug`	str	Drug label name
`source_file`	str	XML filename
`sections`	list[str]	Annotated section names (e.g. "adverse reactions")
`mention_counts`	dict	Count per mention type (AdverseReaction, Severity, …)
`num_reactions`	int	Total unique reactions in this label
`positive_adrs`	list[str]	Positive (non-negated, non-hypothetical) ADR strings
`reactions`	list[dict]	Each with `adr`, `meddra_pt`, `meddra_pt_id`, optional `meddra_llt`, `meddra_llt_id`, `flag`

Usage

See if __name__ == "__main__" block in 37_TAC_2017_ADR.py for runnable examples covering: drug name lookup, ADR string search, MedDRA ID search, batch search, and JSON output.

python
from importlib.machinery import SourceFileLoader
tac = SourceFileLoader("tac2017", "/path/to/37_TAC_2017_ADR.py").load_module()

# Single drug
hits = tac.search("ACTEMRA")
print(tac.summarize(hits, "ACTEMRA"))

# ADR across all labels
hits = tac.search("headache")
print(tac.summarize(hits, "headache"))

# MedDRA PT ID
hits = tac.search("10019211")

# Batch
results = tac.search_batch(["ENBREL", "nausea", "10002198"])

Data

Source: TAC 2017 ADR shared task (NLM / FDA)
Files: gold_xml/ (99 test labels) + train_xml/ (101 training labels), each annotated XML
Annotations: Mentions (AdverseReaction, Severity, Factor, DrugClass, Negation, Animal), Relations (Negated, Hypothetical, Effect), Reactions (unique ADRs with MedDRA PT/LLT normalization)
MedDRA version: 18.1
Path: DATA_DIR variable in 37_TAC_2017_ADR.py
Reference: https://bionlp.nlm.nih.gov/tac2017adversereactions/

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 Tac2017 Adr AI skill do?

Why use Tac2017 Adr on TypingMind?

Because you install it once and use it with any model. Tac2017 Adr 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 Tac2017 Adr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/QSong-github/DrugClaw/tree/main/skills/drug_nlp/tac2017. 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 Tac2017 Adr?

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 Tac2017 Adr?

As many as you like. As long as a model supports skills, you can use Tac2017 Adr with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tac2017 Adr AI skill free?

It is published on GitHub by QSong-github. Check the repository for licensing terms. 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.

View all

Notcrawl

openclaw

OrganizationPopular

Notion archive: search, sync freshness, pages/databases, Markdown exports, SQL counts, and Notcrawl repo work.

Openclaw Test Heap Leaks

openclaw

OrganizationPopular

Investigate OpenClaw pnpm test memory growth, Vitest OOMs, RSS spikes, and heap snapshot deltas.

Prototype Openclaw Tui

openclaw

OrganizationPopular

Build throwaway, fixture-driven OpenClaw Clack or Pi TUI prototypes and compare multiple interactive variants side by side in tmux without running the full application or touching live state.

Slacrawl

Slack archive: search, sync freshness, threads/DMs, SQL counts, and Slacrawl repo work.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

TAC 2017 ADR Query Skill

Entity Auto-detection

API

Hit Dict Structure

Usage

Data

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now