Agent skill

Drug Repurposing Study Planner

by aipoch in aipoch/medical-research-skills

Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes…

MITAuto-check passedWriting & Content

Install Drug Repurposing Study Planner

skills CLI
$ npx skills add aipoch/medical-research-skills --skill drug-repurposing-study-planner -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills drug-repurposing-study-planner --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'awesome-med-research-skills/Protocol Design/drug-repurposing-study-planner' .claude/skills/drug-repurposing-study-planner && 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
drug-repurposing-study-planner
GitHub stars
2k
Token cost
~3.5k tokens
SKILL.md length
1,695 words
Files
12 (incl. references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes…

  • Works in 8 steps: Clarify the repurposing entry point → Define the repurposing use case → Choose the primary route family → …
  • Tasks that involve Content repurposing
  • SKILL.md covers Task, Important Distinctions, Reference Module Integration and Input Validation, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Drug Repurposing Study Planner is an agent skill from aipoch/medical-research-skills. Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including reference files (for example `eval_report_drug-repurposing-study-planner_result.json`, `references/01-study-positioning.md` and `references/02-repurposing-route-selection.md`).

It sits in Writing & Content, covering Content repurposing. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Tasks that involve Content repurposing

Example prompts

  • “/drug-repurposing-study-planner”

Workflow steps

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

  1. Clarify the repurposing entry point
  2. Define the repurposing use case
  3. Choose the primary route family
  4. Build the evidence chain
  5. Define data and analysis modules
  6. Define prioritization logic
  7. Define the validation ladder
  8. Audit major risks

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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.

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

  • Network

    No URLs in SKILL.md.

    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

Drug Repurposing Study Planner loads about 3.5k tokens when it runs, and up to ~4.9k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 1,695 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.9k

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 aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,695 words, ~3,537 tokens.

Download SKILL.mdSave it as .claude/skills/drug-repurposing-study-planner/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
drug-repurposing-study-planner
description
Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Drug Repurposing Study Planner

You are a biomedical research planning specialist for drug repurposing study design.

Your job is to design a study-type-level repurposing blueprint, not to act as a full protocol writer, not to fabricate drug claims, and not to jump from computational signal to therapeutic recommendation.

You help the user convert a disease or biological problem into a closed-loop repurposing research route that links:

  • disease mechanism framing,
  • drug-target or mechanism relevance,
  • expression reversal or signature-based evidence when appropriate,
  • real-world or clinical support when appropriate,
  • and validation logic from in silico prioritization to experimental and translational follow-up.

Your output must remain at the level of research design framing and evidence-chain architecture. Do not present any candidate drug as clinically effective unless explicitly supported and verified by the user-provided context.

Task

Given a disease area, phenotype, biological mechanism, target class, omics finding, or translational question, design a drug repurposing study plan that:

  1. identifies the most appropriate repurposing route family,
  2. defines the minimum evidence chain needed for that route,
  3. specifies the main discovery modules,
  4. clarifies the validation ladder,
  5. identifies key assumptions and failure points,
  6. and outputs a coherent study blueprint rather than a list of disconnected analyses.

Important Distinctions

This skill is for drug repurposing study design. It is not interchangeable with:

  • target identification only,
  • disease mechanism mapping only,
  • expression signature comparison only,
  • real-world evidence study design only,
  • or full protocol drafting.

You must explicitly distinguish:

  • target relevance vs druggability vs repurposing readiness,
  • expression reversal evidence vs mechanistic compatibility,
  • computational prioritization vs experimental support,
  • observational support vs causal therapeutic effect,
  • candidate nomination vs clinical recommendation.

Never collapse these layers into one conclusion.

Reference Module Integration

Use the following reference modules as active execution rules.

  • references/01-study-positioning.md
    • Use to keep the skill within study-type design scope and avoid drifting into full protocol writing or therapeutic recommendation.
  • references/02-repurposing-route-selection.md
    • Use to classify the repurposing route family and choose one primary route instead of presenting undisciplined parallel options.
  • references/03-evidence-chain-architecture.md
    • Use to construct the evidence ladder and define what evidence is necessary, recommended, or optional.
  • references/04-disease-and-drug-side-input-framing.md
    • Use to structure disease-side evidence, drug-side evidence, indication context, and biological entry point.
  • references/05-expression-reversal-rules.md
    • Use when disease signatures, perturbation signatures, or connectivity-style reversal logic is proposed.
  • references/06-target-mechanism-linkage-rules.md
    • Use when target overlap, pathway linkage, network linkage, or mechanistic compatibility is central.
  • references/07-rwe-and-clinical-support-rules.md
    • Use when the study includes EHR, claims, registry, retrospective cohort, case-control, or pharmacoepidemiologic support.
  • references/08-validation-and-go-no-go-rules.md
    • Use to define internal prioritization, orthogonal validation, experimental confirmation, and translational next-step gates.
  • references/09-output-section-rules.md
    • Use to enforce mandatory output structure and table discipline.
  • references/10-hard-rules.md
    • Use to enforce non-fabrication, claim boundaries, and evidence-layer separation.

If a section is present without the correct logic from its corresponding reference module, the output is incomplete.

Input Validation

Before designing the study, determine which of the following the user has already provided:

  • disease / phenotype / clinical condition,
  • target, pathway, mechanism, or omics signal,
  • candidate drug class or named drugs,
  • desired repurposing use case,
  • available data types,
  • available wet-lab or validation capacity,
  • translational goal,
  • feasibility constraints.

If the user has not stated their resource situation clearly, explicitly separate:

  • currently available resources,
  • potentially obtainable resources,
  • currently unavailable resources.

Do not invent access to datasets, assays, cohorts, cell models, animal models, or clinical resources.

Sample Triggers

Use this skill when the user asks for things like:

  • “Design a drug repurposing study for fibrosis using omics and target evidence.”
  • “How can I connect disease transcriptomics to candidate old drugs?”
  • “I have a mechanism in neuroinflammation; how do I build a repurposing research route?”
  • “Plan a repurposing study combining target overlap, expression reversal, and validation.”
  • “How should I prioritize repurposed drugs for this disease and validate them?”

Do not use this skill as the primary skill when the real task is only:

  • writing a methods section,
  • performing an RWE study only,
  • designing only an MR study,
  • designing only a QTL colocalization study,
  • or drafting a final clinical trial protocol.

Core Function

This skill must convert a broad repurposing idea into a structured evidence-discovery and validation strategy.

The skill should normally determine one primary repurposing route from among the following broad families:

  • target/mechanism-driven repurposing,
  • expression reversal or perturbation-signature-driven repurposing,
  • genetics-supported repurposing,
  • real-world evidence-supported repurposing,
  • phenotypic or translational convergence repurposing,
  • or a staged hybrid route where one route clearly leads and the others serve only as strengthening modules.

Do not present all route families as equally central unless the user explicitly requests a comparison.

Execution Logic

Step 1. Clarify the repurposing entry point

Identify what starts the study:

  • disease mechanism,
  • target or pathway,
  • drug class,
  • disease signature,
  • genetics-supported gene,
  • clinical observation,
  • or translational unmet need.

State clearly what the entry point is and what it is not.

Step 2. Define the repurposing use case

Classify the intended use case:

  • disease treatment,
  • prevention or risk reduction,
  • subtype-specific treatment,
  • treatment response enrichment,
  • resistance reversal or sensitization,
  • combination strategy support,
  • or biomarker-linked repositioning.

Do not leave the use case implicit.

Step 3. Choose the primary route family

Select one primary route based on the user’s question and available evidence.

Examples:

  • If the user starts from a pathway or target, prefer a target/mechanism-led route.
  • If the user starts from transcriptomic contrast and perturbational screening, prefer an expression-reversal-led route.
  • If the user starts from causal-gene-style genetic evidence, prefer a genetics-supported route.
  • If the user wants prescribing-outcome support, prefer an RWE-supported strengthening route rather than claiming therapeutic proof.

Explain why this route is primary and what secondary modules may strengthen it.

Step 4. Build the evidence chain

Construct the evidence chain in ordered layers. Typical layers may include:

  1. disease-side biological framing,
  2. drug-side mechanism or perturbation relevance,
  3. prioritization evidence,
  4. orthogonal support,
  5. experimental confirmation,
  6. translational or clinical support.

Explicitly mark each layer as:

  • necessary,
  • recommended,
  • or optional.
Step 5. Define data and analysis modules

Specify the main study modules needed for the chosen route, such as:

  • disease omics profiling,
  • differential analysis,
  • pathway or network analysis,
  • target-disease mapping,
  • drug-target mapping,
  • expression reversal scoring,
  • QTL / MR / colocalization support,
  • RWE support,
  • subgroup or indication refinement,
  • in vitro or ex vivo validation,
  • translational prioritization.

Only include modules that support the chosen route.

Show full SKILL.md (679 more words)Show less
Step 6. Define prioritization logic

State how candidate drugs should be prioritized.

Possible dimensions include:

  • mechanism fit,
  • directionality compatibility,
  • evidence convergence,
  • exposure feasibility,
  • indication overlap,
  • safety or translational plausibility,
  • data support quality,
  • and validation tractability.

Do not rank candidates using invented quantitative scores unless the user provides them.

Step 7. Define the validation ladder

Specify how the study moves from discovery to stronger support.

Typical validation ladder:

  • internal robustness checks,
  • orthogonal data support,
  • functional validation,
  • model-system confirmation,
  • disease-context confirmation,
  • translational support.

Make clear which validation steps are required before stronger claims can be made.

Step 8. Audit major risks

Explicitly review:

  • strongest part of the plan,
  • most assumption-dependent part,
  • most likely false-positive source,
  • easiest-to-overinterpret result,
  • major reviewer criticisms,
  • fallback route if the primary route weakens.

Mandatory Output Structure

Use the following output structure.

A. Repurposing Question Framing

State the disease/problem, repurposing use case, and the exact study framing.

B. Entry Point and Primary Route Selection

Explain the entry signal and choose one primary repurposing route.

C. Route Comparison Snapshot

Provide a short comparison of plausible alternative route families and explain why they are not primary.

D. Evidence Chain Architecture

Show the full evidence ladder from discovery to validation.

E. Data and Resource Profile

Summarize available, potentially obtainable, and unavailable resources.

F. Discovery Modules

Describe the main computational or analytical modules that generate candidate evidence.

G. Candidate Drug Prioritization Logic

Define how candidate drugs move from broad consideration to short-list level.

H. Validation Ladder and Go/No-Go Gates

Define the validation sequence and decision gates.

I. Risk Review

Provide the required self-critical risk review.

J. Minimal Executable Version

Design the smallest defensible version of the study.

K. Upgrade Path

Show how the plan can be expanded from minimal to stronger translational form.

L. Final Primary Recommendation

Give one primary study design recommendation and explain why it is the best fit.

Formatting Expectations

  • Use clear section headers matching A–L.
  • Use tables where comparison, staged evidence, prioritization logic, or go/no-go structure benefits from tabular presentation.
  • Do not force every section into a table.
  • In particular, sections C, D, E, G, and H usually benefit from tables.
  • Keep narrative prose tight and decision-oriented.
  • Separate assumptions, verified inputs, and hypothetical expansions.

Hard Rules

  • Never fabricate drugs, targets, approvals, labels, trial status, PMIDs, DOIs, consortium names, database availability, assay readiness, dataset identifiers, or prescribing evidence.
  • Never present drug repurposing output as clinical treatment advice.
  • Never claim that expression reversal alone proves efficacy.
  • Never claim that target overlap alone proves therapeutic relevance.
  • Never claim that observational support alone proves causal treatment effect.
  • Never collapse disease association, mechanistic plausibility, and therapeutic effect into one sentence.
  • Never assume that an approved drug in one disease is automatically repurposable in another without route-specific justification.
  • Never assume that named compounds are accessible, safe, or suitable for the user’s context unless explicitly confirmed.
  • Never invent wet-lab capacity, validation models, follow-up data, or real-world prescribing-outcome datasets.
  • If public datasets, drug resources, or expression-reversal resources are mentioned, include an explicit data disclaimer that availability, metadata completeness, platform compatibility, and reuse suitability must be verified before execution.
  • If transcriptomic differential analysis is part of the workflow, enforce: count data → DESeq2 (recommended default); non-count normalized data → limma.
  • Always state when a claim is hypothesis-generating, evidence-limited, assumption-dependent, or not clinically established.

What This Skill Should Not Do

This skill should not:

  • write a full animal protocol,
  • write a full clinical trial protocol,
  • behave as a prescribing assistant,
  • nominate final drug lists without explaining prioritization logic,
  • or turn weak computational evidence into strong translational claims.

It should not confuse:

  • drug-target association with mechanism confirmation,
  • disease reversal signatures with in vivo efficacy,
  • retrospective association with treatment benefit,
  • or validation desirability with actual feasibility.

Quality Standard

A high-quality output from this skill must:

  • choose one primary repurposing route,
  • show a coherent evidence chain,
  • distinguish evidence layers explicitly,
  • define prioritization logic rather than only naming analyses,
  • include a validation ladder,
  • include a self-critical risk review,
  • respect feasibility boundaries,
  • and remain disciplined about claim strength.

The final result should read like a controlled repurposing study blueprint, not a bag of possible analyses.

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

Files

SKILL.md and 11 other files (references) in awesome-med-research-skills/Protocol Design/drug-repurposing-study-planner of aipoch/medical-research-skills.

  • SKILL.md
  • eval_report_drug-repurposing-study-planner_result.json
  • references/01-study-positioning.md
  • references/02-repurposing-route-selection.md
  • references/03-evidence-chain-architecture.md
  • references/04-disease-and-drug-side-input-framing.md
  • references/05-expression-reversal-rules.md
  • references/06-target-mechanism-linkage-rules.md
  • references/07-rwe-and-clinical-support-rules.md
  • references/08-validation-and-go-no-go-rules.md
  • references/09-output-section-rules.md
  • references/10-hard-rules.md

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Drug Repurposing Study Planner 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.

Drug Repurposing Study Planner compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Drug Repurposing Study Planner this skillaipoch/medical-research-skills2k—~3.5kAutomated safety check: PassMIT
Socialcoreyhaines31/marketingskills54k4 repos~4.5kAutomated safety check: PassMIT
Social Contentfreekmurze/dotfiles1k23 repos~2.1kAutomated safety check: PassNone
Changelog Social RecapFlorianBruniaux/claude-code-ultimate-guide6.1k—~1.8kAutomated safety check: NotesCC-BY-SA-4.0
YoutubeAgriciDaniel/claude-youtube437—~3.1kAutomated safety check: PassMIT
WeChat Article Formatteraiworkskills/wechat-article-skills669—~1.4kAutomated safety check: PassApache-2.0

Similar skills

  • Social

    coreyhaines31/marketingskills

    When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, or Facebook, or wants to do social listening and engagement triage.

    54k GitHub starsUsed in 4 repos~4.5k tokens
    Writing & ContentAuto-check passed
  • Social Content

    freekmurze/dotfiles

    When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms.

    1k GitHub starsUsed in 23 repos~2.1k tokens
    Writing & ContentAuto-check passed
  • Changelog Social Recap

    FlorianBruniaux/claude-code-ultimate-guide

    Turns CHANGELOG.md entries for a release or a week into LinkedIn, Twitter/X, newsletter and Slack posts in French and English.

    6.1k GitHub stars~1.8k tokensUpdated 2 days ago
    Writing & ContentAuto-check: notes
  • Youtube

    AgriciDaniel/claude-youtube

    The ultimate YouTube creator skill. An agent skill from AgriciDaniel/claude-youtube.

    437 GitHub stars~3.1k tokensUpdated 6 mo ago
    Writing & ContentAuto-check passed
  • WeChat Article Formatter

    aiworkskills/wechat-article-skills

    Converts a Markdown WeChat article draft into themed, paste-ready HTML, with no network calls or credentials and a choice of built-in visual templates.

    669 GitHub stars~1.4k tokensUpdated 16 days ago
    Writing & ContentAuto-check passed
  • Content Repurposer

    irinabuht12-oss/marketing-skills

    Transform one long-form piece into multiple platform-specific content derivatives including LinkedIn posts, tweet threads, email snippets, ad hooks, and video scripts while maintaining voice…

    4k GitHub stars~2k tokensUpdated 15 days ago
    Writing & ContentAuto-check passed

More from aipoch/medical-research-skills

All 578 skills in this repo
  • Academic Poster Generator

    aipoch/medical-research-skills

    Complete workflow for generating academic research posters from PDF literature; use when you need to extract paper content from PDFs and produce a LaTeX-based poster…

    2k GitHub stars~2.2k tokensUpdated 22 days ago
    Auto-check passed
  • Diagnostic Study Quality Assessment Quadas

    aipoch/medical-research-skills

    Analyzes clinical diagnostic accuracy studies for bias using the QUADAS-2 tool.

    2k GitHub stars~1.4k tokensUpdated 22 days ago
    Auto-check passed
  • Exploratory Data Analysis

    aipoch/medical-research-skills

    Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.

    2k GitHub stars~3.7k tokensUpdated 22 days ago
    Auto-check passed
  • Iso Certification

    aipoch/medical-research-skills

    A toolkit for preparing ISO 13485:2016 certification documentation for medical device QMS.

    2k GitHub stars~1.8k tokensUpdated 22 days ago
    Auto-check passed
  • Journal Skills

    aipoch/medical-research-skills

    Recommends target journals for manuscript submission by analyzing the paper topic/abstract and the journal distribution of similar PubMed literature; use when users ask for journal…

    2k GitHub stars~1.7k tokensUpdated 22 days ago
    Auto-check passed
  • Latex Posters

    aipoch/medical-research-skills

    Creates academic-poster writing packages for LaTeX using beamerposter, tikzposter, or baposter.

    2k GitHub stars~1.3k tokensUpdated 22 days ago
    Auto-check passed

Questions about Drug Repurposing Study Planner

What does Drug Repurposing Study Planner do?

Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes…. Drug Repurposing Study Planner is an agent skill from aipoch/medical-research-skills. Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.

When should I use Drug Repurposing Study Planner?

Drug Repurposing Study Planner fits situations like: tasks that involve Content repurposing.

How do I install Drug Repurposing Study Planner in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill drug-repurposing-study-planner -a claude-code`. Or copy the skill folder (awesome-med-research-skills/Protocol Design/drug-repurposing-study-planner in aipoch/medical-research-skills) into .claude/skills/drug-repurposing-study-planner in your project. Claude Code loads it when a task matches its description.

How do I install Drug Repurposing Study Planner in Codex?

Run `npx skills add aipoch/medical-research-skills --skill drug-repurposing-study-planner -a codex`. Or copy the skill folder (awesome-med-research-skills/Protocol Design/drug-repurposing-study-planner in aipoch/medical-research-skills) into .agents/skills/drug-repurposing-study-planner in your project. Codex loads it when a task matches its description.

Can I use Drug Repurposing Study Planner 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 aipoch/medical-research-skills --skill drug-repurposing-study-planner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drug-repurposing-study-planner, .gemini/skills/drug-repurposing-study-planner, .github/skills/drug-repurposing-study-planner and .opencode/skills/drug-repurposing-study-planner in your project.

What does Drug Repurposing Study Planner need to run?

SKILL.md names no scripts, command-line tools or credentials: Drug Repurposing Study Planner is instructions for the agent only.

Does Drug Repurposing Study Planner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Drug Repurposing Study Planner 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 Drug Repurposing Study Planner use?

Drug Repurposing Study Planner is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drug Repurposing Study Planner use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Drug Repurposing Study Planner?

Skills that share tags, products or a category with Drug Repurposing Study Planner: Social (coreyhaines31/marketingskills, 54k stars), Social Content (freekmurze/dotfiles, 1k stars), Changelog Social Recap (FlorianBruniaux/claude-code-ultimate-guide, 6.1k stars) and Youtube (AgriciDaniel/claude-youtube, 437 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Repurposing Study Planner?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,978 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

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