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Radiology Transcriptomics Analysis

CommunityPopular
huang-sir1
radiology-transcriptomics-analysis

Build/run/audit bulk RNA, sc/snRNA and spatial workflows with traceable QC; not mechanism claims.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-transcriptomics-analysis
Stars
2K
Forks
20
Bundled files
10
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huang-sir1 on GitHub. Read the source before you install it.

Installation

Install the Radiology Transcriptomics Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-transcriptomics-analysis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git /tmp/radiology-skills
mkdir -p .claude/skills
cp -r /tmp/radiology-skills/radiology-skills/skills/radiology-transcriptomics-analysis .claude/skills/radiology-transcriptomics-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Transcriptomics Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Radiology Transcriptomics Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Radiology Transcriptomics Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Transcriptomics analysis execution

Use this skill when the requested object is an executable or auditable bulk RNA, sc/snRNA, or spatial-transcriptomics workflow. It can be entered directly with raw matrices, processed objects, code, logs, or only a proposed analysis. It does not require an imaging dataset.

Core contract

  • Separate PLAN_ONLY, CODE_READY, RUNNING, RUN_COMPLETE, RUN_FAILED, AUDIT_ONLY, and REPRODUCED. Never turn a plan, code draft, screenshot, reported result, or partial log into a completed run.
  • Preserve the chain source files -> sample/donor map -> QC decisions -> transformed object -> analysis object -> result table/figure -> claim handoff with exact paths and versions.
  • Use the biological sampling unit for inference. Cells, nuclei, spots, genes, tiles, fields, technical replicates, bootstrap draws, seeds and clusters are not new patients or donors.
  • Build the shortest workflow that answers the locked question. Optional trajectory, cell-cell communication, GRN, deconvolution or integration is activated only when its assumptions and decision value are explicit.
  • Never invent data availability, sample annotations, reference labels, package versions, QC thresholds, commands, run success, results, cell identities, spatial domains or mechanisms.
  • Review is read-only by default. Build, edit, or execute code only when the user requests it and the target inputs, working directory, output root, environment and protected-data boundary are known.

Choose the entry mode

ModeUse whenPrimary output
planquestion and data state are known but no locked workflow existsmodality plan, decision points, expected artifacts and analysis lock
buildcode/configuration must be created or repairedversioned code/config plus dry-run and output contract
run-auditcode should be executed/monitored, or an existing run must be auditedrun manifest, log/QC evidence, failures and artifact inventory
reproducean earlier modality-specific run has command, environment and reference outputscontrolled assay/workflow rerun, QC comparison and domain verdict; cross-domain evidence level goes to radiology-reproducibility
interpret-handoffreal outputs exist and need scientific interpretationresult-to-question packet with evidence and claim ceilings; no invented mechanism
mentora learner needs constructive choices before or after analysisminimum defensible, standard publishable and ambitious routes with rationale, cost, failure signal, fallback and closure evidence
writing-handoffverified outputs must be placed into a manuscript without claim driftMethods/Results/display/Supplement/Discussion/Abstract-title placement map with evidence locators and prohibited upgrades

Declare exactly one primary modality: bulk-rna, scrna, snrna, or spatial. Declare secondary modalities only when there is a real matched or bridge analysis. If the user asks whether a parameter or method choice is defensible rather than to configure or run it, route that decision to radiology-method-evaluation.

Minimal intake

Record what is available and mark the rest AUTHOR_INPUT_NEEDED:

  1. question, endpoint/contrast, intended use, affected Claim IDs and whether imaging is involved;
  2. source type and exact locations: FASTQ, counts, features/barcodes, matrices, object files, histology images, coordinates, annotations, code, logs and prior outputs;
  3. donor/patient/specimen/region/section/batch/library hierarchy and matched-sample map;
  4. platform/chemistry/reference build or assay version when known; strandedness and feature definition for bulk; spot/bin/cell resolution for spatial;
  5. clinical covariates, group/contrast, repeated measures, confounders and protected test state;
  6. environment, package lock, compute limits, output root, seed policy and execution authority;
  7. pre-existing QC thresholds, exclusions, reference atlases, labels, checkpoints and deviations.

If donor identity, sample-to-donor mapping, contrast, output root or execution authority is missing, planning may continue but execution returns RUN_BLOCKED.

Required workflow

  1. Open the modality workflows and the runtime/reproducibility contract.
  2. Freeze an intake manifest and choose the narrowest modality branch.
  3. Define the true independent unit, analysis unit, repeated-measure structure and aggregation rule before filtering, integration or testing.
  4. Create a decision ledger for every irreversible or conclusion-bearing choice: inclusion, filtering, normalization, reference/annotation, integration, contrast, spatial scale and exclusion.
  5. Write the analysis plan and expected-output contract. Preserve a protected-test boundary where applicable.
  6. In build, create code/configuration with explicit inputs, outputs, versions, seeds, assertions, checkpoints and failure behavior. Run only safe dry checks until real execution is authorized.
  7. In run-audit, record the exact command, working directory, environment, start/end state, log, exit status, warnings, QC decisions, deviations and artifact checksums where feasible.
  8. Stop on mapping ambiguity, corrupt/incompatible objects, sample-count mismatch, output overwrite risk, unresolved privacy boundary, or a requested analysis that changes the locked estimand without approval.
  9. Package real outputs for statistical inference, biological interpretation, method evaluation, writing and review without silently upgrading evidence state.

Use the analysis plan, run manifest, QC decision log, local runtime profile, and local analysis experience registry when persistent artifacts are useful. Local experience can guide failure diagnosis only inside its recorded platform/tissue/data-state boundary; it never becomes an unreported default.

Modality routes

ModalityMinimum execution chainDecision-bearing extensions
bulk RNAoptional FASTQ front end -> gene/transcript counts + raw-read QC -> metadata identity/QC -> filtering -> normalization/exploration -> design/contrast -> gene-level model -> multiplicity -> pathway/score -> exportbatch/composition adjustment, paired/repeated design, deconvolution, signature validation, survival/clinical association
sc/snRNAoptional chemistry-aware FASTQ front end -> raw + filtered feature-barcode matrices -> calling/ambient/RBC/QC -> doublets -> normalization/HVG -> dimension reduction -> integration decision -> neighbors/clustering -> annotation -> donor-aware DE/abundance -> exportsubclustering, trajectory, state scoring, communication, GRN, reference mapping, perturbation response
spatialoptional platform-aware FASTQ/image front end -> matrix + barcode/coordinate/image bundle -> geometry/registration/segmentation QC -> filtering/normalization -> cell-type mapping/deconvolution decision -> domains -> spatial statistic/neighborhood -> patient aggregation -> exportmulti-section alignment, ligand-receptor context, boundary gradients, image-feature bridge, spatial trajectory

Scientific red lines

  • Do not treat cell/spot-level P values as patient-level evidence. For case-control or clinical claims, use donor/patient-aware models, pseudobulk or another justified hierarchical strategy.
  • Integration is not automatic quality improvement. It can erase real biological or clinical differences; preserve unintegrated evidence and test whether the intended contrast remains.
  • Cluster labels are hypotheses supported by declared markers/references and discordance checks, not ground truth supplied by an algorithm.
  • Post hoc QC tuned until the desired biology appears is outcome leakage. Record timing and inspect conclusion stability under defensible alternatives.
  • Gene-set enrichment depends on the tested universe, ranking/statistic, database version and multiplicity family. A pathway label is not direct pathway activity or causal mechanism.
  • Deconvolution, imputation, virtual cells/spots, spatial completion and inferred interactions are estimates. They are not measured cells, molecules, physical contact, perturbation or validation.
  • Trajectory ordering is an inferred geometry; it does not establish real time, lineage or causal direction without external evidence.
  • A spatial neighborhood result must state coordinate system, radius/kernel/k, edge handling, null model, section/patient aggregation and sensitivity to scale.
  • Cross-modal agreement in the same cohort is orthogonal concordance, not independent replication.
  • Never overwrite raw inputs or the only copy of a processed object. Write to an explicit output root and preserve provenance.

Output contract

Return only the sections needed for the current mode, but preserve this order:

  1. Execution scope — mode, modality, question/contrast, hierarchy, independent unit, evidence state, protected-test state and supplied artifacts.
  2. Input and identity audit — source paths, dimensions/checksums when available, donor/sample mapping, missing/duplicate identities and blockers.
  3. Workflow and decision ledger — each step's input, output, rationale, alternatives, assumption, decision owner, freeze state and failure condition.
  4. Run record — exact command/environment/seed, start/end/exit status, log path, deviations and monitored output paths; omit only in plan.
  5. QC and exclusion ledger — metric, level, rule, timing, affected units, retained/excluded counts, reason, sensitivity need and author approval.
  6. Artifact inventory — object/table/figure/report path, schema/dimensions, checksum or other integrity evidence, producer and verification state.
  7. Findings and failure boundary — real observed results only, alternative explanations, unresolved confounding and what failed or was not run.
  8. Handoffs — exact packet for inference, method judgement, mechanism interpretation, wet-lab validation, writing or prereview.
  9. State and next action — one of the declared execution states, blockers, surviving claim ceiling and the single next decision-bearing action.

In mentor, every major recommendation also reports: why it matters, minimum repair, stronger alternative, resource/time burden, expected failure signal, fallback, closure evidence and the claim that survives if it cannot be repaired. In writing-handoff, do not draft around missing evidence: map frozen workflow/version/unit/QC to Methods; verified prespecified or labeled exploratory outputs to Results; source-data locators to figures/tables; full exclusions/sensitivities/environment/ deviations to Supplement; alternatives and limits to Discussion; and enforce the weakest-link claim ceiling in Abstract/title.

Handoffs and non-ownership

  • Biological interpretation, alternative mechanism hypotheses, cross-scale imaging bridge and claim ceiling -> radiology-radiogenomics, using only verified result artifacts.
  • Parameter provenance, method fit, integration/normalization alternatives, sensitivity and fair comparison -> radiology-method-evaluation.
  • Donor-aware effect estimation, confidence intervals, multiplicity, hierarchical modelling or survival inference -> radiology-stats after this skill freezes units and contrasts.
  • Functional/perturbational wet-lab validation design -> radiology-experiment-design.
  • Data governance, de-identification and sharing -> radiology-data and radiology-ethics.
  • Frozen replay packaging, prespecified tolerance, operator independence and cumulative evidence level -> radiology-reproducibility; this skill retains assay/workflow-specific rerun and QC.
  • Fixed-evidence manuscript construction -> radiology-writing; whole-manuscript review -> radiology-prereview; upload-file audit -> radiology-submission.

This skill may explain what an output means operationally, but a computational association, estimated cell state, inferred spatial relation or computational perturbation is not causal proof.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Radiology Transcriptomics Analysis AI skill do?

Build/run/audit bulk RNA, sc/snRNA and spatial workflows with traceable QC; not mechanism claims.

Why use Radiology Transcriptomics Analysis on TypingMind?

Because you install it once and use it with any model. Radiology Transcriptomics Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Radiology Transcriptomics Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-transcriptomics-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Radiology Transcriptomics Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Radiology Transcriptomics Analysis?

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

Is the Radiology Transcriptomics Analysis AI skill free?

It is published on GitHub by huang-sir1. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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