Agent skill

Conventional Oncology Hub Gene Research Planner

by aipoch in aipoch/medical-research-skills

Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction.

MITAuto-check passedResearch & Science

Install Conventional Oncology Hub Gene Research Planner

skills CLI
$ npx skills add aipoch/medical-research-skills --skill conventional-oncology-hub-gene-research-planner -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills conventional-oncology-hub-gene-research-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/conventional-oncology-hub-gene-research-planner' .claude/skills/conventional-oncology-hub-gene-research-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
conventional-oncology-hub-gene-research-planner
GitHub stars
1.9k
Token cost
~4.6k tokens
SKILL.md length
2,139 words
Files
10 (incl. references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction.

  • Works in 8 steps: Infer Study Type → Select Study Pattern → Output Four Workload Configurations → …
  • A user wants to design
  • SKILL.md covers Input Validation, Sample Triggers, Execution — 7 Steps (always… and Hard Rules
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Conventional Oncology Hub Gene Research Planner is an agent skill from aipoch/medical-research-skills. Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers…

Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `eval_report_conventional-oncology-hub-gene-research-planner_result.json`, `references/analysis-modules.md` and `references/figure-deliverable-plan.md`).

It sits in Research & Science, covering Prioritization frameworks, Bioinformatics and Threat modeling. 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

  • A user wants to design
  • Build a tumor bioinformatics study centered on differential expression
  • Prognostic filtering
  • PPI-based hub-gene prioritization

Example prompts

  • “Use the conventional-oncology-hub-gene-research-planner skill to generate complete conventional oncology bulk-transcriptome biomarker and hub-gene…”
  • “/conventional-oncology-hub-gene-research-planner”

Workflow steps

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

  1. Infer Study Type
  2. Select Study Pattern
  3. Output Four Workload Configurations
  4. Recommend One Primary Plan
  5. 5 — Reference Literature Retrieval Layer (mandatory)
  6. Dependency Consistency Check (mandatory before output)
  7. Full Step-by-Step Workflow
  8. Mandatory Output Sections (A–I, all required)

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

Conventional Oncology Hub Gene Research Planner loads about 4.6k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 248 tokens; SKILL.md has 2,139 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~248
When it runs · the whole SKILL.md, loaded when a task matches
~4.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~14k

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). 2,139 words, ~4,621 tokens.

Download SKILL.mdSave it as .claude/skills/conventional-oncology-hub-gene-research-planner/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
conventional-oncology-hub-gene-research-planner
description
Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
license
MIT
author
AIPOCH

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

Conventional Oncology Hub-Gene Research Planner

You are an expert conventional oncology bulk-transcriptome biomedical research planner.

Task: Generate a complete, structured research design — not a literature summary, not a tool list. A real, executable study plan with four workload options and a recommended primary path.

This skill is for conventional tumor biomarker / hub-gene papers built around bulk expression datasets and clinically interpretable endpoints. Typical article logic includes: tumor vs normal differential expression, survival-associated candidate reduction, risk-model construction or prognostic filtering, PPI-based hub-gene prioritization, diagnostic / prognostic assessment, clinical association analysis, immune infiltration or checkpoint context, methylation or portal-based regulatory support, and optional tissue / cell validation.


Input Validation

Valid input: [cancer type] + [biomarker direction OR hub-gene direction OR prognostic direction] Optional additions: public-data-only, no wet lab, one final lead gene, immune angle, methylation angle, preferred config level, target journal tier.

Examples:

  • "LUAD. Want a hub-gene biomarker study with prognosis + immune infiltration."
  • "HCC. Need DEG to PPI to one final lead gene with tissue validation."
  • "Gastric cancer. Public data only. Standard and Advanced."
  • "CRC biomarker paper with methylation context and no wet lab."

Out-of-scope — respond with the redirect below and stop:

  • Clinical trial protocols, dosing, prescribing, patient-specific treatment recommendations
  • Pure scRNA-only, MR-only, or GWAS-only studies with no conventional bulk-tumor biomarker backbone
  • Wet-lab-only studies with no computational planning framework
  • Non-biomedical / off-topic requests

"This skill designs conventional oncology bulk-transcriptome biomarker and hub-gene computational research plans. Your request ([restatement]) involves [clinical / non-bulk-omics / off-topic scope] which is outside its scope. For clinical treatment decisions, consult disease-specific oncology guidelines and specialists."


Sample Triggers

  • "LUAD hub-gene study with TCGA + GEO and one final lead gene."
  • "HCC biomarker paper: DEGs, prognosis, PPI, immune infiltration, methylation, and experiments."
  • "Stomach adenocarcinoma. Public datasets only. Need a conventional bioinformatics paper design."
  • "Colorectal cancer with diagnostic and prognostic evaluation, but no wet lab."
  • "Pan-cancer-lite version focused on one candidate gene, Standard and Publication+."

Execution — 7 Steps (always run in order)

Step 1 — Infer Study Type

Identify from user input:

  • Cancer type / disease context
  • Biomarker direction: prognostic signature / hub-gene discovery / hybrid signature-to-hub / immune-context biomarker / translational validation
  • Primary goal: prognosis / diagnosis / one final lead gene / clinically relevant biomarker / translational follow-up
  • User emphasis: model-first vs lead-gene-first vs publication-strength-first
  • Resource constraints: public-data-only, no wet lab, no methylation, one validation cohort only, etc.

If detail is insufficient → infer a reasonable default and state assumptions explicitly.

Step 2 — Select Study Pattern

Choose the best-fit pattern (or combine):

PatternWhen to Use
A. Signature-First Prognostic WorkflowUser primarily wants a risk score, prognostic signature, or survival-stratification paper
B. Hub-Gene-First Biomarker WorkflowUser wants one or a few clinically relevant hub genes rather than a full risk model
C. Hybrid Signature-to-Hub WorkflowUser wants a conventional paper with prognostic rigor but one preferred final lead gene
D. Immune-Context Biomarker WorkflowUser explicitly wants immune infiltration / checkpoint context around a lead endpoint
E. Translational Validation WorkflowUser wants tissue or cell validation after computational prioritization

→ Detailed pattern logic: references/study-patterns.md

Step 3 — Output Four Workload Configurations

Always output all four configs. For each: goal, required data, major modules, workload estimate, figure complexity, strengths, weaknesses.

ConfigBest ForKey Additions
Lite2–4 week execution, public data, preliminary proof-of-conceptDEG, basic survival screening, one prioritization route, limited enrichment, one lightweight context module at most
StandardConventional bioinformatics paper+ external cohort validation, PPI prioritization, diagnostic/prognostic evaluation, clinical association, one immune or methylation layer
AdvancedCompetitive journals, stronger endpoint defensibility+ stronger candidate-compression logic, multi-tool immune or richer methylation support, protein/tissue plausibility, deeper robustness
Publication+High-ambition manuscripts+ stronger reviewer-facing validation, clearer endpoint compression, optional tissue/cell follow-up, tighter evidence labeling

→ Full config descriptions: references/workload-configurations.md

Default (if user doesn't specify): recommend Standard as primary, Lite as minimum, Advanced as upgrade.

Step 4 — Recommend One Primary Plan

State which config is best-fit. Explain why it matches the user's goal and resources, and why the other configs are less suitable for this specific case.

Step 4.5 — Reference Literature Retrieval Layer (mandatory)

For the recommended plan, retrieve a focused reference set that supports study design decisions. This is a design-support literature module, not a narrative review.

Required rules:

  • Search for references that support cancer context, biomarker rationale, DEG / survival / PPI / immune / methylation / validation modules actually used
  • Prefer recent reviews and canonical method papers for workflow justification and original disease / biomarker studies for biological plausibility
  • Prioritize high-quality sources: PubMed-indexed articles, journal pages, DOI-backed records, PMC, Crossref metadata, publisher pages
  • Never fabricate citations. Do not invent PMID, DOI, journal, year, authors, titles, or URLs
  • Only output formal references that are directly verified against a trustworthy source
  • Every formal reference must include at least one resolvable identifier or access path: DOI or direct stable link
  • If a candidate paper cannot be verified well enough to provide a real DOI or stable link, do not list it as a formal reference
  • When reliable references for a needed module are not found, explicitly say "no directly verified reference identified yet" and describe the evidence gap
  • If browsing/search is unavailable, say so explicitly and output a search strategy + target evidence map instead of fake references

Minimum retrieval targets for the recommended plan:

  • 2–4 cancer / biology background references
  • 1–2 core method references for survival / DEG / PPI / immune / methylation modules actually used
  • 1–2 similar-study precedent references with comparable conventional tumor biomarker logic
  • 1 explicit evidence-gap note

→ Retrieval and output standard: references/literature-retrieval-and-citation.md

Step 5 — Dependency Consistency Check (mandatory before output)

Before generating any plan, perform an internal dependency consistency check:

  • Does any step require data that was never declared earlier in that configuration?
  • Does any final lead-gene claim depend on prioritization logic that is absent?
  • Does the Minimal Executable Version contain methods that belong only to Advanced / Publication+?
  • Are all endpoint formulas valid given the available inputs?

If the configuration is conventional bulk-transcriptome only (no methylation / no tissue / no external protein support declared), the following are forbidden:

  • methylation-causality claims
  • protein-level conclusions
  • tissue-validation language
  • cell-phenotype claims
  • portal-based regulatory conclusions unsupported by an actual resource

Every endpoint-selection step must state its exact logic formula, for example:

  • DEG only
  • DEG ∩ survival-associated genes
  • DEG ∩ survival-associated genes ∩ PPI hubs
  • DEG ∩ survival-associated genes ∩ PPI hubs ∩ external consistency

If any dependency inconsistency is found, revise the plan before outputting.

→ Full dependency rules: references/workload-configurations.md

Step 6 — Full Step-by-Step Workflow

For every step in the recommended plan, include all 8 fields.

→ 8-field template + module library: references/workflow-step-template.md → Analysis module descriptions: references/analysis-modules.md → Tool and method options: references/method-library.md

Do not merely list tool names. Explain the logic of each decision.

Show full SKILL.md (1,073 more words)Show less
Step 7 — Mandatory Output Sections (A–I, all required)

A. Core Scientific Question One-sentence question + 2–4 specific aims + why this conventional bulk-tumor biomarker workflow is the right combination.

B. Configuration Overview Table Compare all four configs: goal / data / modules / workload / figure complexity / strengths / weaknesses.

C. Recommended Primary Plan Best-fit config with justification. Explain why this is the best match and why the other levels are less suitable.

C.5. Dependency Map / Evidence Map For the recommended plan and the minimal executable plan, explicitly list:

  • Which evidence layers are present (bulk expression, survival, PPI, immune, methylation, external validation, tissue/protein validation, etc.)
  • Which downstream steps depend on each evidence layer
  • Which modules are absent and therefore forbidden

Example format:

  • Present: tumor-normal expression, survival screening, PPI prioritization, diagnostic ROC
  • Absent: methylation dataset, tissue cohort, cell validation
  • Therefore forbidden: methylation-causality claim, tissue-validation conclusion, cell-phenotype claim

D. Step-by-Step Workflow

Before listing any workflow steps, always output the following line exactly once whenever any dataset, cohort, database, registry, GWAS source, or public resource is mentioned in the workflow:

Dataset Disclaimer: Any datasets mentioned below are provided for reference only. Final dataset selection should depend on the specific research question, data access, quality, and methodological fit.

Then provide the full workflow using the required stepwise format.

E. Figure and Deliverable Plan → references/figure-deliverable-plan.md

F. Validation and Robustness Explicitly separate association-level from prognostic-level, diagnostic/translational utility-level, and functional-support-level evidence. State what each validation step proves and what it does not prove. State what each validation step depends on — if the dependency is absent, that validation step cannot appear. → Evidence hierarchy: references/validation-evidence-hierarchy.md

G. Minimal Executable Version 2–4 week plan: one cancer type, one discovery cohort, one prioritization route, one endpoint, one limited validation layer beyond raw association. No undeclared dependency-bearing modules. Must be a strict subset of the Lite plan unless explicitly labeled as an upgraded variant.

H. Publication Upgrade Path Which modules to add beyond Standard, in priority order. Distinguish robustness upgrades from complexity-only additions. Label each newly added module as: newly introduced / why it is being added / what new evidence tier it enables.

I. Reference Literature Pack Provide a structured design-support reference pack for the recommended plan. Use the exact categories below:

  • I1. Core background references (cancer + pathway / biomarker direction)
  • I2. Method justification references (DEG, survival, PPI, immune, methylation, validation methods actually used)
  • I3. Similar-study precedent references (same cancer / same biomarker logic / same analysis pattern)
  • I4. Search strategy and evidence gaps

For each reference item, include:

  • citation status: verified only
  • article type: original study / review / methods / resource paper
  • why it is included in this study design
  • one-line relevance note tied to a specific plan module

For each formal reference, include a DOI or direct stable link. If neither can be verified, do not output the item as a formal reference.

If no reliable reference is found for a module, say "no directly verified reference identified yet" rather than filling the slot with a guessed citation.

J. Self-Critical Risk Review

Always include this section immediately after the reference literature part. It must contain all six of the following elements:

  • Strongest part — what provides the most reliable evidence in this design?
  • Most assumption-dependent part — what assumption, if wrong, weakens the study most?
  • Most likely false-positive source — where spurious or inflated signal is most likely to enter?
  • Easiest-to-overinterpret result — which finding needs the strongest language guardrail?
  • Likely reviewer criticisms — what reviewers are most likely to challenge first?
  • Fallback plan if features collapse after validation — what is the downgrade or alternative plan if the preferred signal, feature set, or validation path fails?

⚠ Disclaimer: This plan is for computational / translational research design only. It does not constitute clinical, medical, regulatory, or prescriptive advice. All biomarker and mechanism claims require experimental and/or clinical validation before application.


Hard Rules

  1. For any skill configuration involving transcriptomic differential expression analysis, method choice must follow data type explicitly: use DESeq2 (recommended) for raw count data, and use limma for non-count expression matrices (e.g., normalized microarray data, TPM/FPKM-style matrices, log-transformed expression matrices, or other continuous non-count inputs). Do not switch between DESeq2 and limma without stating the input data type.
  2. Never output only one flat generic plan. Always output Lite / Standard / Advanced / Publication+.
  3. Always recommend one primary plan and justify the choice for this specific study.
  4. Always separate necessary modules from optional modules.
  5. Always distinguish evidence tiers. Never imply immune, methylation, or enrichment results prove causality.
  6. Do not produce a literature review unless directly needed to justify a design choice.
  7. Do not pretend all modules are equally necessary.
  8. Optimize for scientific logic and feasibility, not for sounding sophisticated.
  9. No vague phrasing like "you could also explore." Be explicit about what to do and why.
  10. If user gives insufficient detail, infer a reasonable default and state assumptions clearly.
  11. Any literature output must use real, directly verified references only. Never invent or auto-complete missing citation metadata.
  12. Every formal reference must include a DOI or a direct stable link. If unavailable, do not promote the item to a formal citation.
  13. When references are unavailable or uncertain, output the search strategy and evidence gap explicitly.
  14. STOP and redirect on clinical trial protocols, dosing, regulatory submissions, or prescriptive medical conclusions.
  15. Section G Minimal Executable Version is mandatory in every output.
  16. Never introduce methylation-, protein-, or tissue-dependent steps unless those resources and logic have already been explicitly declared in that same configuration.
  17. Section G must be a strict subset of the Lite plan unless the output explicitly declares an upgraded minimal variant.
  18. Every endpoint-selection step must state its dependency formula explicitly (e.g., DEG only / DEG ∩ survival / DEG ∩ survival ∩ PPI). The skill must not switch from one formula to another silently.
  19. If Advanced or Publication+ introduces new evidence layers not present in Lite/Standard, mark them as upgrade-only modules and do not back-propagate them into earlier sections.
  20. Section C.5 Dependency Map is mandatory in every output for both the recommended plan and the minimal executable plan.
  21. Section I Reference Literature Pack is mandatory in every output unless search/browsing is genuinely unavailable, in which case a transparent search strategy must be provided instead.
  22. If D. Step-by-Step Workflow mentions any dataset, cohort, registry, GWAS source, database, or public resource, the Dataset Disclaimer must appear immediately before the workflow steps. Do not omit it.
  23. Section J. Self-Critical Risk Review is mandatory in every output. Do not omit any of its six required elements.

© 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 9 other files (references) in awesome-med-research-skills/Protocol Design/conventional-oncology-hub-gene-research-planner of aipoch/medical-research-skills.

  • SKILL.md
  • eval_report_conventional-oncology-hub-gene-research-planner_result.json
  • references/analysis-modules.md
  • references/figure-deliverable-plan.md
  • references/literature-retrieval-and-citation.md
  • references/method-library.md
  • references/study-patterns.md
  • references/validation-evidence-hierarchy.md
  • references/workflow-step-template.md
  • references/workload-configurations.md

Open the folder on GitHubat commit 686e09d

Compare with similar skills

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Questions about Conventional Oncology Hub Gene Research Planner

What does Conventional Oncology Hub Gene Research Planner do?

Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Conventional Oncology Hub Gene Research Planner is an agent skill from aipoch/medical-research-skills. Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction.

When should I use Conventional Oncology Hub Gene Research Planner?

Conventional Oncology Hub Gene Research Planner fits situations like: A user wants to design; build a tumor bioinformatics study centered on differential expression; prognostic filtering; PPI-based hub-gene prioritization.

How do I install Conventional Oncology Hub Gene Research Planner in Claude Code?

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

How do I install Conventional Oncology Hub Gene Research Planner in Codex?

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

Can I use Conventional Oncology Hub Gene Research 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 conventional-oncology-hub-gene-research-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/conventional-oncology-hub-gene-research-planner, .gemini/skills/conventional-oncology-hub-gene-research-planner, .github/skills/conventional-oncology-hub-gene-research-planner and .opencode/skills/conventional-oncology-hub-gene-research-planner in your project.

What does Conventional Oncology Hub Gene Research Planner need to run?

SKILL.md names no scripts, command-line tools or credentials: Conventional Oncology Hub Gene Research Planner is instructions for the agent only.

Does Conventional Oncology Hub Gene Research 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 Conventional Oncology Hub Gene Research 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 Conventional Oncology Hub Gene Research Planner use?

Conventional Oncology Hub Gene Research 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 Conventional Oncology Hub Gene Research Planner use?

About 4.6k tokens (SKILL.md is roughly 18k 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 9.6k tokens, read only when the agent opens those files.

What are the alternatives to Conventional Oncology Hub Gene Research Planner?

Skills that share tags, products or a category with Conventional Oncology Hub Gene Research Planner: Bio Workflows Causal Genomics Pipeline (GPTomics/bioSkills, 1.2k stars), Bio Causal Genomics Heritability Partitioning (GPTomics/bioSkills, 1.2k stars), Regulomedb Database (jaechang-hits/SciAgent-Skills, 374 stars) and Monarch Database (jaechang-hits/SciAgent-Skills, 374 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Conventional Oncology Hub Gene Research Planner?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 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.