Agent skill

Bio Workflows Tcr Pipeline

by GPTomics in GPTomics/bioSkills

Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks.

MITAuto-check passedResearch & Science

Install Bio Workflows Tcr Pipeline

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-workflows-tcr-pipeline -a claude-code

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-workflows-tcr-pipeline --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/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/workflows/tcr-pipeline .claude/skills/bio-workflows-tcr-pipeline && 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
bio-workflows-tcr-pipeline
GitHub stars
1.2k
Used in
1 other repo
Token cost
~4.7k tokens
SKILL.md length
1,532 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks.

  • Works in 5 steps: License and preset selection → MiXCR assembly (all branches) → Export (branch-specific handoff) → …
  • 10x paired VDJ - MiXCR 10x preset
  • SKILL.md covers Version Compatibility, The governing principle: the…, Pipeline overview and Stage 0: License and preset…, plus 10 more sections
  • Runs Shell scripts from its folder

What it does

Bio Workflows Tcr Pipeline is an agent skill from GPTomics/bioSkills. Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Use when deciding bulk vs single-cell (bulk amplicon/RNA-seq - MiXCR analyze preset - VDJtools/immunarch depth-normalized diversity and overlap - figures; 10x paired VDJ - MiXCR 10x preset or Cell Ranger - scirpy gene-expression integration, chain QC, clonotype clusters); and TCR vs BCR (TCR - exact CDR3-nt+V/J clonotypes, VDJtools diversity is fine; BCR -…

Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/tcr_full_pipeline.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • 10x paired VDJ - MiXCR 10x preset
  • Cell Ranger - scirpy gene-expression integration
  • Clonotype clusters)
  • TCR vs BCR (TCR - exact CDR3-nt+V/J clonotypes

Example prompts

  • “Use the bio-workflows-tcr-pipeline skill to orchestrate an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap…”
  • “/bio-workflows-tcr-pipeline”

Requirements

  • A Bash shell
  • Docker

Workflow steps

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

  1. License and preset selection
  2. MiXCR assembly (all branches)
  3. Export (branch-specific handoff)
  4. Visualization
  5. (optional): Specificity annotation

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. 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

    Ships script files (Shell), which the agent can run.

    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

Bio Workflows Tcr Pipeline loads about 4.7k tokens when it runs. Until then it costs about 225 tokens; SKILL.md has 1,532 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~225
When it runs · the whole SKILL.md, loaded when a task matches
~4.7k

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 GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,532 words, ~4,719 tokens.

Download SKILL.mdSave it as .claude/skills/bio-workflows-tcr-pipeline/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-workflows-tcr-pipeline
description
Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Use when deciding bulk vs single-cell (bulk amplicon/RNA-seq -> MiXCR analyze preset -> VDJtools/immunarch depth-normalized diversity and overlap -> figures; 10x paired VDJ -> MiXCR 10x preset or Cell Ranger -> scirpy gene-expression integration, chain QC, clonotype clusters); and TCR vs BCR (TCR -> exact CDR3-nt+V/J clonotypes, VDJtools diversity is fine; BCR -> somatic hypermutation makes exact clonotypes wrong -> Immcantation distToNearest/findThreshold clonal clustering, germline reconstruction, SHM, Dowser lineages); selecting the MiXCR 4.x preset by chemistry and activating its license; downsampling to equal depth before comparing diversity or overlap; and optionally annotating antigen specificity.
tool_type
cli
primary_tool
MiXCR
workflow
true
depends_on
tcr-bcr-analysis/mixcr-analysis, tcr-bcr-analysis/vdjtools-analysis, tcr-bcr-analysis/immcantation-analysis, tcr-bcr-analysis/scirpy-analysis…

Version Compatibility

Reference examples tested with: MiXCR 4.7+, VDJtools 1.2.1+, Immcantation suite 4.x, scirpy 0.24+

Before using code patterns, verify installed versions match. If versions differ:

  • CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Note: MiXCR 4.x replaced the 3.x hand-built mixcr align -s hsa -p rna-seq chain with the preset-driven mixcr analyze <preset> system, and 4.x refuses to run any command without an activated license (mixcr activate-license, or MI_LICENSE_FILE on HPC/Docker). A copied 3.x recipe fails on both counts.

TCR/BCR Repertoire Pipeline

"Analyze my immune-repertoire sequencing data end-to-end" -> Route the data by chemistry and receptor, assemble clonotypes with MiXCR, then hand off to depth-normalized diversity (bulk TCR), clonal clustering plus somatic-hypermutation and lineages (BCR), or single-cell gene-expression integration (10x), and finally figures.

This workflow is a router, not a fixed line. Two forks decide everything downstream; pick both before running anything.

The governing principle: the pipeline forks on two axes

A repertoire measurement is a depth- and chemistry-confounded sample of an unevenly-expanded clonal population, so the correct pipeline depends on how the library was made and which receptor was sequenced. Choosing the wrong branch silently produces plausible-but-wrong numbers.

Fork A -- bulk vs single-cell
AxisBulk (amplicon or RNA-seq)Single-cell (10x VDJ)
Assemblermixcr analyze <bulk preset>mixcr analyze 10x-sc-xcr-vdj OR Cell Ranger vdj
Chain pairingUNPAIRED (TRB or IGH alone)Native pairing (TRA+TRB, IGH+IGK/L)
Depth vs breadthDeep repertoire, no cell stateShallower, links receptor to transcriptome
DownstreamVDJtools / immunarch diversity + overlap -> figuresscirpy: chain QC, clonotype clusters, GEX integration
Best whenDiversity, overlap, tracking, deep clonotype captureAntigen-specific cell state, alpha-beta / heavy-light pairing
Fork B -- TCR vs BCR
AxisTCR (TRA/TRB/TRG/TRD)BCR (IGH/IGK/IGL)
Somatic hypermutationNoneYes -- clone members are NOT identical
Clonotype definitionExact CDR3-nt + V + J (after UMI/error correction)NEVER exact CDR3; cluster same-V/J/junction-length by distance
Diversity pathVDJtools CalcDiversityStats on exact clonotypes is fineCluster clones FIRST, then diversity on clone_id
Extra stagesnonegermline reconstruction, SHM, selection, Dowser lineage trees
ToolVDJtools / immunarchImmcantation (Change-O, SHazaM, SCOPer, Dowser, TIGGER)

The most common pipeline mistake is running BCR through exact-CDR3 VDJtools diversity. SHM shatters one clone into hundreds of near-identical variants, so exact clonotypes over-count diversity and destroy lineage structure. BCR must route to Immcantation clonal clustering (distToNearest -> findThreshold) before any diversity, SHM, or lineage step. TCR has no SHM, so exact CDR3-nt+V/J is the correct, defensible clonotype and VDJtools diversity is appropriate.

Pipeline overview

FASTQ (+ chemistry, species, receptor known)
    |
    v
[0. License + preset choice] --- mixcr activate-license ; pick preset by kit
    |
    v
[1. MiXCR analyze] ---------- <preset> R1 R2 out_prefix  ->  clones.clns + reports
    |
    +-- QC: mixcr qc / exportQc align + chainUsage
    |
    v
  FORK on data type
    |
    |-- bulk --> [2b. Export] exportClones (VDJtools) / exportAirr
    |               |
    |               v
    |            FORK on receptor
    |               |-- TCR --> [3t. DownSample to equal depth] --> CalcDiversityStats + overlap
    |               |-- BCR --> [3b. Immcantation] distToNearest->findThreshold->clones
    |               |                                -> CreateGermlines --cloned -> SHM -> Dowser trees
    |               v
    |            [4. Visualization] VDJtools / immunarch / ggplot
    |
    |-- single-cell --> [2s. exportAirr / Cell Ranger] --> [3s. scirpy]
                            chain_qc -> ir_dist -> define_clonotypes -> GEX integration
    |
    v
[5. Optional] specificity annotation (VDJdb / GLIPH2 / TCRdist) -- hypothesis, not label

Stage 0: License and preset selection

MiXCR 4.x will not run unlicensed. Activate once (academic license is free), then choose the preset by the exact kit -- the preset encodes species, RNA vs DNA, 5' boundary model (floating for multiplex primers, rigid for 5'-RACE), tag pattern, and assembling feature. The wrong preset does not error; it silently mis-calls V and truncates CDR3.

bash
mixcr activate-license                 # or: export MI_LICENSE_FILE=/path/mi.license
mixcr exportPreset --preset-name generic-amplicon   # audit what a preset actually does

Preset by chemistry (verify against mixcr analyze --help and the built-in preset list; MiLaboratories renames occasionally):

DataPreset
Generic multiplex/RACE amplicongeneric-amplicon, generic-amplicon-with-umi (+ --species hsa, --rna/--dna, boundary mixins)
Bulk RNA-seq miningrna-seq (judge by clonotype yield, not alignment %)
10x single-cell V(D)J10x-sc-xcr-vdj
Takara SMARTertakara-human-rna-tcr-umi-smarter-v2, takara-human-rna-bcr-umi-smarter
BD Rhapsodybd-human-sc-xcr-rhapsody-cdr3
Full component-skill preset tabletcr-bcr-analysis/mixcr-analysis

Stage 1: MiXCR assembly (all branches)

bash
# One command runs align -> refineTagsAndSort -> (assemblePartial) -> assemble -> export.
# From MiXCR 4.7, presets without an intrinsic assembling feature require --assemble-clonotypes-by.
# generic-amplicon REQUIRES material type + both alignment-boundary mixins (it errors without them).
# Multiplex primers on both ends -> floating boundaries; 5'RACE -> --rigid-left-alignment-boundary.
mixcr analyze generic-amplicon \
    --species hsa \
    --rna \
    --floating-left-alignment-boundary \
    --floating-right-alignment-boundary C \
    sample_R1.fastq.gz sample_R2.fastq.gz \
    results/sample

# QC every sample -- low alignment or off-target chains means wrong preset/species/contamination
mixcr qc results/sample.clns
mixcr exportQc align results/*.clns results/qc_align.pdf
mixcr exportQc chainUsage results/*.clns results/qc_chains.pdf

QC checkpoint 1 (after align): amplicon libraries should align at high rate (often >80-90%); a low rate signals wrong species, wrong boundary model, or untrimmed primers. RNA-seq mining legitimately aligns a tiny fraction -- judge it by absolute clonotype yield. chainUsage catches cross-contamination and index hopping (a TRB library showing appreciable IGH).

Detailed alignment, UMI/cell-barcode handling, and export flags: tcr-bcr-analysis/mixcr-analysis.

Stage 2: Export (branch-specific handoff)

bash
# Bulk -> VDJtools-readable clonotype table (per chain)
mixcr exportClones -c TRB results/sample.clns results/sample.clones_TRB.tsv

# BCR or single-cell -> AIRR Rearrangement TSV (the Immcantation / scirpy interchange)
mixcr exportAirr results/sample.clns results/sample.airr.tsv

QC checkpoint 2 (after assemble): a large reads-to-clonotypes drop-off is normal (millions of reads -> thousands of clones), especially after UMI collapse. Report the right denominator: uniqueMoleculeCount on UMI libraries (reporting reads re-introduces the PCR bias the UMIs removed), cells on single-cell, reads only on non-UMI bulk.

Stage 3t: Bulk TCR diversity and overlap -- downsample FIRST

Diversity (richness, Shannon, clonality) and set-based overlap (Jaccard, shared-clonotype counts) are strictly increasing functions of sequencing depth. Comparing raw values across samples of unequal depth measures depth, not biology -- the single most common error in the field. DownSample every sample to a common depth (or read rarefaction curves at a common x) before comparing.

bash
# 1. Equalize depth: set the target near the cohort lower quartile, and EXCLUDE (do not drag
#    everyone down to) any sample far below it -- an under-sampled library cannot support a claim.
vdjtools DownSample -x 50000 -m metadata.txt ds/

# 2. Diversity on depth-normalized samples; report the resampled table for cross-sample claims
vdjtools CalcDiversityStats -m ds/metadata.txt diversity/

# 3. Overlap with a depth-robust, abundance-weighted metric (F2 / Morisita-Horn), not Jaccard
vdjtools CalcPairwiseDistances -m ds/metadata.txt overlap/

Version caveat (MiXCR 4.x -> VDJtools): VDJtools is unmaintained for MiXCR 4.x and its parser breaks on raw exportClones output (Unable to parse clonotype string; 4.x injects commas and renames/moves columns). For a MiXCR 4.7+ cohort, prefer MiXCR's own mixcr postanalysis individual / mixcr postanalysis overlap (its native 4.x replacement for VDJtools diversity/overlap, with the same downsample-first semantics) over the vdjtools Convert -S mixcr route; if VDJtools is required, strip the added contig/target-sequence columns before Convert. immunarch (R) is the other modern alternative.

QC checkpoint 3 (before diversity): confirm all samples share one depth, and drop any sample whose rarefaction curve is still climbing steeply below that depth (under-sampled -- exclude rather than normalize the cohort down to it). Hold the clonotype match key (nt vs aa, +/-V, +/-J) constant study-wide; aa-level matching inflates apparent sharing via convergent recombination. Report clonality alongside a q=2 Hill number (inverse Simpson) and a rarefaction curve, not alone. immunarch is the modern R alternative with the same normalization semantics: tcr-bcr-analysis/vdjtools-analysis.

Show full SKILL.md (651 more words)Show less

Stage 3b: BCR clonal clustering, SHM and lineages (Immcantation)

BCR cannot use exact clonotypes. Feed the AIRR TSV to Immcantation and follow the mandatory order: annotate -> (TIGGER genotype) -> per-sequence germline -> data-derived clonal threshold -> cluster -> per-clone germline -> SHM/selection -> lineage trees. The threshold from the bimodal distance-to-nearest distribution drives every downstream number; a wrong threshold merges or splits clones.

r
library(shazam); library(scoper); library(dowser)
db <- airr::read_rearrangement('results/sample.airr.tsv')
# 1. Clonal threshold: valley between the intra-clone and inter-clone modes (per dataset, never reused)
dtn <- distToNearest(db, model = 'ham', normalize = 'len')
thr <- findThreshold(dtn$dist_nearest, method = 'density')@threshold
# 2. Cluster within same-V/J/junction-length partitions at that threshold
cl <- hierarchicalClones(dtn, threshold = thr)
# 3. Reconstruct per-clone germline (CreateGermlines.py --cloned), then observedMutations, then Dowser getTrees

Cluster clones within an individual only (genotypes and thresholds are private). Diversity for BCR runs on clone_id, not exact CDR3. Full germline reconstruction, SHM quantification, BASELINe selection, and IgPhyML/Dowser trees: tcr-bcr-analysis/immcantation-analysis.

Stage 3s: Single-cell integration (scirpy)

10x paired VDJ carries native chain pairing and links receptor to cell state. The clonotype definition is a choice, not a default, and multichain cells are likely doublets.

python
import scirpy as ir
import mudata as mu
airr = ir.io.read_airr('results/sample.airr.tsv')           # or read_10x_vdj on Cell Ranger output
mdata = mu.MuData({'gex': gex_adata, 'airr': airr})         # scirpy 0.13+ stores AIRR as an awkward array
ir.pp.index_chains(mdata)                                    # REQUIRED before chain_qc / clonotyping
ir.tl.chain_qc(mdata)                                        # flag multichain (doublet) / orphan cells
# TCR path: exact-identity clonotypes on CDR3-nt + V/J
ir.pp.ir_dist(mdata)                                         # default metric='identity'
ir.tl.define_clonotypes(mdata)
# BCR path: SHM breaks exact identity, so cluster. ir_dist MUST be recomputed with the SAME
# metric+sequence the clustering call uses (scirpy keys the matrix as ir_dist_{sequence}_{metric};
# the identity matrix above will not satisfy a normalized_hamming call).
# ir.pp.ir_dist(mdata, metric='normalized_hamming', sequence='nt')
# ir.tl.define_clonotype_clusters(mdata, sequence='nt', metric='normalized_hamming', same_v_gene=True)
# integrate with the scanpy GEX modality; measure expansion vs cell state

Filtering multichain/orphan cells before expansion analysis preferentially deletes small clones and inflates apparent expansion -- state the trade-off, do not blindly drop them. CellRanger BCR contigs are not IMGT-numbered and include partial/nonproductive contigs; reannotate with IgBLAST (dandelion/airrflow) before rigorous BCR clustering. GEX side (clustering, annotation): single-cell/preprocessing and single-cell/clustering. Full clonotype-definition decisions: tcr-bcr-analysis/scirpy-analysis.

Stage 4: Visualization

Spectratype (CDR3-length), V-J usage circos, clonal-space bars, rarefaction curves, and clonal tracking across timepoints. Every figure inherits the depth caveat -- plot rarefaction at a common x, and track clones only after downsampling timepoints to equal depth. Recipes: tcr-bcr-analysis/repertoire-visualization.

Stage 5 (optional): Specificity annotation

Annotate or cluster clonotypes by likely antigen (VDJdb/McPAS lookup, or GLIPH2/TCRdist clustering). A database hit is a sequence match to a published antigen-specific receptor, not proof the clone binds that antigen. "Public" clonotypes are enriched for high generation-probability (Pgen), short, low-insertion CDR3s produced independently in many donors by convergent recombination (Venturi 2006 PNAS 103:18691-18696) -- publicity is not antigen selection. Treat every specificity call as a hypothesis, condition on Pgen, and validate. Handoff: tcr-bcr-analysis/specificity-annotation.

Common Errors

SymptomCauseFix
MiXCR exits immediately, "no license"4.x needs an activated licensemixcr activate-license, or set MI_LICENSE_FILE on HPC/Docker; whitelist the phone-home IPs on firewalled clusters
mixcr align -s hsa -p rna-seq unrecognized3.x syntax removed in 4.xUse mixcr analyze <preset> R1 R2 out_prefix
Analysis runs but clonotypes look wrong (truncated CDR3, inflated diversity)Wrong preset -- RNA/DNA, boundary model, or missing tag patternMatch preset to the exact kit; mixcr exportPreset to audit; set --species on generic presets
MiXCR 4.7 errors on analyze needing an assembling featurePreset lacks an intrinsic assembling featureAdd --assemble-clonotypes-by CDR3
Diversity/clonality differ across samples but tracks read countComparing raw diversity at unequal depthDownSample to a common depth first; report the resampled table / rarefaction at common x
BCR clones fragmented, diversity absurdly high, no lineagesExact-CDR3 clonotypes applied to a hypermutating receptorRoute BCR to Immcantation distToNearest -> findThreshold clustering before any diversity/SHM
SHM counts inflated, spurious mutations in junctionNo germline reconstruction, or junction not maskedCreateGermlines.py -g dmask then --cloned; restrict observedMutations to IMGT_V
scirpy expansion inflated by doubletsmultichain cells not filteredRun chain_qc and drop multichain cells before clonotype/expansion analysis
Overlap dominated by the shallower sampleJaccard / shared-count on unequal depthDownsample, then use abundance-weighted F2 or Morisita-Horn
  • tcr-bcr-analysis/mixcr-analysis - V(D)J alignment and clonotype assembly
  • tcr-bcr-analysis/vdjtools-analysis - Depth-normalized diversity and overlap
  • tcr-bcr-analysis/immcantation-analysis - BCR clonal clustering, SHM and lineages
  • tcr-bcr-analysis/scirpy-analysis - Single-cell VDJ + gene-expression integration
  • tcr-bcr-analysis/repertoire-visualization - Figures for the pipeline outputs
  • tcr-bcr-analysis/specificity-annotation - Optional antigen-specificity annotation

References

  • Bolotin DA, et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat Methods 2015; 12:380-381.
  • Shugay M, et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput Biol 2015; 11:e1004503.
  • Gupta NT, et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 2015; 31:3356-3358.
  • Sturm G, et al. Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data. Bioinformatics 2020; 36:4817-4818.
  • Chao A, et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 2014; 84:45-67.
  • Venturi V, et al. Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination. PNAS 2006; 103:18691-18696.

© GPTomics, 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 2 other files in workflows/tcr-pipeline of GPTomics/bioSkills.

  • SKILL.md
  • examples/tcr_full_pipeline.sh
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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    1.2k GitHub starsUsed in 2 repos~2.4k tokens
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Questions about Bio Workflows Tcr Pipeline

What does Bio Workflows Tcr Pipeline do?

Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks. Bio Workflows Tcr Pipeline is an agent skill from GPTomics/bioSkills. Orchestrates an end-to-end immune-repertoire pipeline from FASTQ to clonotypes, diversity, overlap, somatic hypermutation and lineages, routing on two forks.

When should I use Bio Workflows Tcr Pipeline?

Bio Workflows Tcr Pipeline fits situations like: 10x paired VDJ - MiXCR 10x preset; cell Ranger - scirpy gene-expression integration; clonotype clusters); TCR vs BCR (TCR - exact CDR3-nt+V/J clonotypes.

How do I install Bio Workflows Tcr Pipeline in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-workflows-tcr-pipeline -a claude-code`. Or copy the skill folder (workflows/tcr-pipeline in GPTomics/bioSkills) into .claude/skills/bio-workflows-tcr-pipeline in your project. Claude Code loads it when a task matches its description.

How do I install Bio Workflows Tcr Pipeline in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-workflows-tcr-pipeline -a codex`. Or copy the skill folder (workflows/tcr-pipeline in GPTomics/bioSkills) into .agents/skills/bio-workflows-tcr-pipeline in your project. Codex loads it when a task matches its description.

Can I use Bio Workflows Tcr Pipeline 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 GPTomics/bioSkills --skill bio-workflows-tcr-pipeline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-workflows-tcr-pipeline, .gemini/skills/bio-workflows-tcr-pipeline, .github/skills/bio-workflows-tcr-pipeline and .opencode/skills/bio-workflows-tcr-pipeline in your project.

What does Bio Workflows Tcr Pipeline need to run?

Going by SKILL.md and its folder, Bio Workflows Tcr Pipeline needs a shell for the scripts in its folder. Our summary lists: A Bash shell; Docker.

Does Bio Workflows Tcr Pipeline 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 Bio Workflows Tcr Pipeline 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 Bio Workflows Tcr Pipeline use?

Bio Workflows Tcr Pipeline is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Workflows Tcr Pipeline use?

About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Workflows Tcr Pipeline?

Skills that share tags, products or a category with Bio Workflows Tcr Pipeline: 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 Bio Workflows Tcr Pipeline?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.