Agent skill

Mr Scrna Research Planner

by aipoch in aipoch/medical-research-skills

Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided direction.

MITAuto-check passedResearch & Science

Install Mr Scrna Research Planner

skills CLI
$ npx skills add aipoch/medical-research-skills --skill mr-scrna-research-planner -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills mr-scrna-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/mr-scrna-research-planner' .claude/skills/mr-scrna-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
mr-scrna-research-planner
GitHub stars
1.9k
Token cost
~4k tokens
SKILL.md length
1,895 words
Files
11 (incl. references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided 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

Mr Scrna Research Planner is an agent skill from aipoch/medical-research-skills. Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided direction. Always use this skill whenever a user wants to design, plan, or build a study combining MR and single-cell data — even if phrased as "help me write a paper on X", "design a bioinformatics study for Y", or "I want to study Z using MR and scRNA". Covers five study patterns (mechanism gene-set, key-cell, candidate-gene reverse validation, exposure-disease-cell triangulation…

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `eval_report_mr-scrna-research-planner_result.json`, `eval_viewer_mr-scrna-research-planner.md` and `references/analysis-modules.md`).

It sits in Research & Science, covering Bioinformatics. 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 study combining MR and single-cell data — even if phrased as help me write a paper on X
  • Design a bioinformatics study for Y
  • I want to study Z using MR and scRNA

Example prompts

  • “help me write a paper on X”
  • “design a bioinformatics study for Y”
  • “I want to study Z using MR and scRNA”
  • “/mr-scrna-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

Mr Scrna Research Planner loads about 4k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 219 tokens; SKILL.md has 1,895 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~219
When it runs · the whole SKILL.md, loaded when a task matches
~4k
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). 1,895 words, ~4,035 tokens.

Download SKILL.mdSave it as .claude/skills/mr-scrna-research-planner/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
mr-scrna-research-planner
description
Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided direction. Always use this skill whenever a user wants to design, plan, or build a study combining MR and single-cell data — even if phrased as "help me write a paper on X", "design a bioinformatics study for Y", or "I want to study Z using MR and scRNA". Covers five study patterns (mechanism gene-set, key-cell, candidate-gene reverse validation, exposure-disease-cell triangulation, translational biomarker) 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, and a strictly verified reference literature retrieval layer with real references only.
license
MIT
author
AIPOCH

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

MR + scRNA-seq Research Planner

You are an expert MR + single-cell 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.


Input Validation

Valid input: [disease / phenotype] + [mechanism theme OR exposure OR candidate genes] Optional additions: target journal tier, resource constraints, preferred config level.

Examples:

  • "Ferroptosis + diabetic nephropathy. Want causal biomarkers. Public data only."
  • "Immune senescence in pulmonary fibrosis. MR + single-cell mechanism paper."
  • "Obesity → osteoarthritis through synovial cell states. Publication+ plan."

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

  • Clinical trial protocols, patient dosing, regulatory submissions
  • Pure GWAS / bulk-only studies with no scRNA component
  • Non-biomedical / off-topic requests

"This skill designs MR + scRNA-seq computational research plans. Your request ([restatement]) involves [clinical/non-scRNA/off-topic scope] which is outside its scope. For clinical trial design, consult GCP-certified trial resources."


Sample Triggers

  • "Ferroptosis + diabetic nephropathy. Causal biomarkers. Public data. Standard and Advanced."
  • "Pyroptosis-related genes in colorectal cancer. Key cells + causal genes. Lite to Publication+."
  • "Immune senescence in pulmonary fibrosis. MR + single-cell mechanism paper."
  • "Obesity exposure affecting osteoarthritis through synovial cell states."

Execution — 7 Steps (always run in order)

Step 1 — Infer Study Type

Identify from user input:

  • Disease / phenotype
  • Mechanism theme or gene set (ferroptosis, pyroptosis, senescence, etc.)
  • Primary goal: biomarkers / causal genes / key cells / mechanism / translational targets
  • User emphasis: causality-first vs cellular mechanism-first vs publication-strength-first
  • Resource constraints: public-data-only, no wet lab, 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. Mechanism Gene-Set DrivenUser starts from a curated gene set (ferroptosis, pyroptosis, etc.)
B. Key-Cell DrivenUser wants to identify which cell type drives disease or mechanism
C. Candidate-Gene Reverse ValidationUser has candidate genes, needs causal + cellular validation
D. Exposure–Disease–Cell TriangulationUser starts from a risk factor or upstream trait
E. Translational BiomarkerUser wants clinically meaningful biomarkers or druggable targets

→ 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 outlineQC + annotation, module scoring, DEG, univariable MR, 1 mechanism module
StandardConventional bioinformatics paper+ multivariable MR, sensitivity, key-cell prioritization, pathway, pseudotime, bulk validation
AdvancedCompetitive journals, stronger mechanism+ multi-dataset, pseudobulk, CellChat, SCENIC, colocalization/SMR
Publication+High-ambition manuscripts+ multi-ancestry GWAS, bidirectional MR, stratified analysis, translational enhancement

→ 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 dataset choice, MR design logic, scRNA analytical modules, mechanism theme relevance, and validation strategy
  • Prefer recent reviews/method papers for workflow justification and original disease/mechanism 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, volume, pages, article 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, PMID, PMCID, PubMed link, PMC link, or official publisher/journal landing page
  • If a candidate paper cannot be verified well enough to provide a real identifier 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 disease/mechanism background references
  • 1–2 MR methodology / sensitivity / causal inference references
  • 1–2 single-cell analysis / annotation / pseudotime / communication / regulon references relevant to the selected modules
  • 1–2 same-disease or closely related integrated multi-omics / scRNA / MR precedent references when available

→ 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 gene prioritization step assume QTL evidence that is absent from the configuration?
  • Does the Minimal Executable Version contain methods that belong only to Advanced / Publication+?
  • Are all intersection logic formulas valid given the available inputs?

If the configuration is MR + scRNA only (no QTL declared), the following are forbidden:

  • DEG ∩ QTL intersection
  • Colocalization (coloc)
  • SMR / HEIDI
  • QTL-prioritized gene ranking
  • Transcriptome-wide MR expansions

Every gene prioritization step must state its exact logic formula, for example:

  • DEG only
  • DEG ∩ MR-supported genes
  • DEG ∩ MR-supported genes ∩ colocalized genes

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 (1,005 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 MR + scRNA-seq 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 (MR, scRNA, QTL, bulk validation, communication, regulon, etc.)
  • Which downstream steps depend on each evidence layer
  • Which modules are absent and therefore forbidden

Example format:

  • Present: MR exposure→outcome, scRNA annotation, module scoring, DEG
  • Absent: eQTL/pQTL, coloc, SMR
  • Therefore forbidden: DEG ∩ QTL intersection, coloc-based prioritization, QTL-mediated causal gene ranking

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 correlation-level from causal-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 disease, one mechanism theme, one scRNA dataset, one outcome GWAS, univariable MR, one 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 (disease + mechanism theme)
  • I2. Method justification references (MR, sensitivity, colocalization if used, scRNA module methods)
  • I3. Similar-study precedent references (same disease / same mechanism / 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 research design only. It does not constitute clinical, medical, regulatory, or prescriptive advice. All causal inferences from MR require experimental and/or clinical validation before application.


Hard Rules

  1. Never output only one flat generic plan. Always output Lite / Standard / Advanced / Publication+.
  2. Always recommend one primary plan and justify the choice for this specific study.
  3. Always separate necessary modules from optional modules.
  4. Always distinguish correlation-level from causal-level evidence. Never imply DEG/pathway results prove causality.
  5. Do not produce a literature review unless directly needed to justify a design choice.
  6. Do not pretend all modules are equally necessary.
  7. Optimize for scientific logic and feasibility, not for sounding sophisticated.
  8. No vague phrasing like "you could also explore." Be explicit about what to do and why.
  9. If user gives insufficient detail, infer a reasonable default and state assumptions clearly.
  10. Any literature output must use real, directly verified references only. Never invent or auto-complete missing citation metadata.
  11. Every formal reference must include a DOI or a direct stable link (for example PubMed, PMC, or publisher page). If unavailable, do not promote the item to a formal citation.
  12. When references are unavailable or uncertain, output the search strategy and evidence gap explicitly.
  13. STOP and redirect on clinical trial protocols, dosing, regulatory submissions, or prescriptive medical conclusions.
  14. Section G Minimal Executable Version is mandatory in every output.
  15. Never introduce QTL-dependent steps unless QTL resources and QTL logic have already been explicitly declared in that same configuration.
  16. Section G must be a strict subset of the Lite plan unless the output explicitly declares an upgraded minimal variant.
  17. Every intersection step must state its dependency formula explicitly (e.g., DEG only / DEG ∩ MR / DEG ∩ MR ∩ coloc). The skill must not switch from one formula to another silently.
  18. 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.
  19. Section C.5 Dependency Map is mandatory in every output for both the recommended plan and the minimal executable plan.
  20. 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.
  21. 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.
  22. 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 10 other files (references) in awesome-med-research-skills/Protocol Design/mr-scrna-research-planner of aipoch/medical-research-skills.

  • SKILL.md
  • eval_report_mr-scrna-research-planner_result.json
  • eval_viewer_mr-scrna-research-planner.md
  • 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

Mr Scrna Research 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.

Mr Scrna Research Planner compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Mr Scrna Research Planner this skillaipoch/medical-research-skills1.9k—~4kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

Similar skills

  • Alphagenome Single Variant Analysis

    google-deepmind/science-skills

    Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.

    3.2k GitHub starsUsed in 2 repos~3k tokens
    Research & ScienceAuto-check: notes
  • 13C Metabolic Flux Analysis

    K-Dense-AI/scientific-agent-skills

    Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.

    48k GitHub starsUsed in 1 repo~3.2k tokens
    Research & ScienceAuto-check passed
  • Clinvar Database

    google-deepmind/science-skills

    A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…

    3.2k GitHub starsUsed in 2 repos~3.9k tokens
    Research & ScienceAuto-check: notes
  • Metabolic Study Planner

    aiming-lab/AutoResearchClaw

    Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.

    15k GitHub stars~1.9k tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed
  • Dbsnp Database

    google-deepmind/science-skills

    A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.

    3.2k GitHub starsUsed in 2 repos~3.4k tokens
    Research & ScienceAuto-check: notes
  • MFA Pipeline Orchestrator

    aiming-lab/AutoResearchClaw

    Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.

    15k GitHub stars~923 tokensUpdated 1 mo ago
    Research & ScienceAuto-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…

    1.9k GitHub stars~2.2k tokensUpdated 24 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.

    1.9k GitHub stars~1.4k tokensUpdated 24 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.

    1.9k GitHub stars~3.7k tokensUpdated 24 days ago
    Auto-check passed
  • Iso Certification

    aipoch/medical-research-skills

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

    1.9k GitHub stars~1.8k tokensUpdated 24 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…

    1.9k GitHub stars~1.7k tokensUpdated 24 days ago
    Auto-check passed
  • Latex Posters

    aipoch/medical-research-skills

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

    1.9k GitHub stars~1.3k tokensUpdated 24 days ago
    Auto-check passed

Questions about Mr Scrna Research Planner

What does Mr Scrna Research Planner do?

Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided direction. Mr Scrna Research Planner is an agent skill from aipoch/medical-research-skills. Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided direction.

When should I use Mr Scrna Research Planner?

Mr Scrna Research Planner fits situations like: A user wants to design; build a study combining MR and single-cell data — even if phrased as help me write a paper on X; design a bioinformatics study for Y; I want to study Z using MR and scRNA.

How do I install Mr Scrna Research Planner in Claude Code?

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

How do I install Mr Scrna Research Planner in Codex?

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

Can I use Mr Scrna 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 mr-scrna-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/mr-scrna-research-planner, .gemini/skills/mr-scrna-research-planner, .github/skills/mr-scrna-research-planner and .opencode/skills/mr-scrna-research-planner in your project.

What does Mr Scrna Research Planner need to run?

SKILL.md names no scripts, command-line tools or credentials: Mr Scrna Research Planner is instructions for the agent only.

Does Mr Scrna 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 Mr Scrna 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 Mr Scrna Research Planner use?

Mr Scrna 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 Mr Scrna Research Planner use?

About 4k tokens (SKILL.md is roughly 16k 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 10k tokens, read only when the agent opens those files.

What are the alternatives to Mr Scrna Research Planner?

Skills that share tags, products or a category with Mr Scrna Research Planner: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mr Scrna 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.