qsong-github/drugclaw/psytar

Name: psytar
Author: qsong-github

Claude Code and agent skill by qsong-github/drugclaw

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Query the PsyTAR psychiatric adverse-reaction corpus. Use when the user asks about patient-reported ADRs, withdrawal symptoms, drug indications, or effectiveness for Zoloft, Lexapro, Cymbalta, or Effexor XR. Accepts drug names (brand or generic), symptom terms, or UMLS CUIs.

Install

npx vskill@latest install qsong-github/drugclaw/psytar

SKILL.md

PsyTAR Query Skill

891 patient reviews → 6 009 annotated sentences → extracted ADR / WD / SSI / DI entities → mapped to 918 UMLS + 755 SNOMED CT concepts.

Entity Detection & Routing

Input Pattern	Detected As	Targets
`C0917801`	UMLS CUI	`*_Mapped` sheets only (UMLS1/UMLS2 cols)
`Zoloft` / `sertraline`	Drug name	`drug_id` or `drug` columns (alias-aware)
`nausea`, `insomnia` …	Free text	substring across all cell values

Generic ↔ brand aliases: sertraline↔Zoloft, escitalopram↔Lexapro, duloxetine↔Cymbalta, venlafaxine↔Effexor.

API

Function	Input	Returns
`search(entity, sheet?, label?)`	single string	`{sheet: [row_dict]}`
`search_batch(entities, sheet?, label?)`	list of strings	`{entity: {sheet: [row_dict]}}`
`summarize(results, entity)`	result dict + label	compact text
`to_json(results)`	result dict	`list[dict]` (flat, adds `_sheet`)
`describe()`	—	dataset overview text

Parameters

`sheet` — restrict to one sheet (fuzzy-matched: "ADR" → ADR_Identified).
`label` — when on Sentence_Labeling, keep only rows where the named

label column (ADR / WD / EF / INF / SSI / DI) equals 1.

Sheet Schema

Sheet	Description	Key Columns
`Sample`	Original posts	drug_id, rating, indication, side-effect, comment, gender, age, duration
`Sentence_Labeling`	6 009 sentences, binary labels	drug_id, sentence_index, sentences, ADR, WD, EF, INF, SSI, DI, Findings, others, rating, category
`ADR_Identified`	Extracted ADR mentions	drug_id, sentence_index, sentences, ADR1 … ADRn
`WD_Identified`	Extracted WD mentions	drug_id, sentence_index, sentences, WD1 … WDn
`SSI_Identified`	Extracted SSI mentions	drug_id, sentence_index, sentences, SSI1 … SSIn
`DI_Identified`	Extracted DI mentions	drug_id, sentence_index, sentences, DI1 … DIn
`ADR_Mapped`	ADR → UMLS/SNOMED	drug_id, sentence_index, ADR/ADRs, UMLS1, UMLS2, SNOMED-CT, mild, moderate, severe, persistent, not-persistent, body-site, rating, drug, class, type, entity_type
`WD_Mapped`	WD → UMLS/SNOMED	(same structure as ADR_Mapped)
`SSI_Mapped`	SSI → UMLS/SNOMED	(same structure)
`DI_Mapped`	DI → UMLS/SNOMED	(same structure)

Mapped-sheet qualifier columns

mild, moderate, severe — severity descriptors; persistent, not-persistent — duration; body-site — anatomical site; entity_type — Cognitive / Physiological / Psychological / Functional.

Usage

python

from importlib.machinery import SourceFileLoader
m = SourceFileLoader("psytar", "36_PSYTAR.py").load_module()
# overview
print(m.describe())
# drug → ADR mappings
res = m.search("Zoloft", sheet="ADR_Mapped")
print(m.summarize(res, "Zoloft"))
# generic name works too
res = m.search("sertraline", sheet="ADR_Mapped")
# symptom in one Identified sheet
res = m.search("nausea", sheet="ADR_Identified")
# symptom across all sheets
res = m.search("insomnia")
# UMLS CUI (auto-scoped to Mapped sheets)
res = m.search("C0917801")
# withdrawal sentences for Effexor
res = m.search("Effexor", sheet="Sentence_Labeling", label="WD")
# batch
batch = m.search_batch(["Lexapro", "insomnia", "C0917801"])
# JSON for pipeline
flat = m.to_json(m.search("Cymbalta"))

Data Source

Corpus: PsyTAR v1.0 — CC BY 4.0
File: PsyTAR_dataset.xlsx — set via DATA_PATH or env PSYTAR_XLSX
Paper: Zolnoori et al., Data in Brief 24, 103838 (2019).

https://doi.org/10.1016/j.dib.2019.103838

Stats: 891 reviews, 6 009 sentences, 4 813 ADR + 590 WD + 1 219 SSI
792 DI mentions, 918 UMLS / 755 SNOMED concepts

Security

Security scanPassed, 95 of 100Pattern checks on the skill's files.
Model reviewPassed

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Quality evaluation

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Source

SKILL.md in the repositoryCheckingskills/drug_nlp/psytar/SKILL.md

Popularity

Repository stars: 116
Repository forks: 3
7-day trend score: 24

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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