Agent skill

Bio Genome Engineering Hdr Template Design

by GPTomics in GPTomics/bioSkills

Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the…

MITAuto-check passedResearch & Science

Install Bio Genome Engineering Hdr Template Design

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-genome-engineering-hdr-template-design -a claude-code

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-genome-engineering-hdr-template-design --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/genome-engineering/hdr-template-design .claude/skills/bio-genome-engineering-hdr-template-design && 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-genome-engineering-hdr-template-design
GitHub stars
1.2k
Used in
1 other repo
Token cost
~4k tokens
SKILL.md length
1,956 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the…

  • Designing a donor for a point mutation
  • SKILL.md covers Version Compatibility, The Single Most Important…, The Pathway Competition… and Donor Format Decision, plus 10 more sections
  • Runs Python scripts from its folder; calls pip
  • Epitope/fluorophore tag

What it does

Bio Genome Engineering Hdr Template Design is an agent skill from GPTomics/bioSkills. Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and…

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/hdr_template_design.py` 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

  • Designing a donor for a point mutation
  • Epitope/fluorophore tag
  • Allele replacement
  • HDR efficiency is low

Example prompts

  • “Use the bio-genome-engineering-hdr-template-design skill to design donor/repair templates for precise CRISPR knock-ins -- choosing the format…”
  • “/bio-genome-engineering-hdr-template-design”

Requirements

  • Python 3

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 (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

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

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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 Genome Engineering Hdr Template Design loads about 4k tokens when it runs. Until then it costs about 194 tokens; SKILL.md has 1,956 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~194
When it runs · the whole SKILL.md, loaded when a task matches
~4k

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). 1,956 words, ~3,997 tokens.

Download SKILL.mdSave it as .claude/skills/bio-genome-engineering-hdr-template-design/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-genome-engineering-hdr-template-design
description
Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and ranked HDR enhancers. Use when designing a donor for a point mutation, epitope/fluorophore tag, allele replacement, or knock-in, or when HDR efficiency is low. Guide design and base/prime editing are separate skills.
tool_type
python
primary_tool
primer3-py

Version Compatibility

Reference examples tested with: BioPython 1.83+, primer3-py 2.0+.

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

  • Python: pip show <package> then help(module.function) to check signatures

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

primer3-py designs PRIMERS (use primer3.bindings.design_primers(seq_args, global_args); the camelCase designPrimers is deprecated since 1.0.0), not homology arms -- arm extraction and codon-aware blocking are the skill's own BioPython code. Design arms and guide against the actual cell line's sequence, not GRCh38 -- a SNP in an arm reduces annealing and a SNP in the PAM/seed can mean the guide does not cut.

HDR Template Design

"Design a donor for my CRISPR knock-in" -> Decide the format by edit size and whether the cell cycles, size the arms, confirm a guide cuts within ~10 bp of the edit, and add a codon-checked blocking mutation so the corrected allele cannot be re-cut.

  • Python: arm extraction + codon-aware PAM/seed blocking with Bio.Seq; primer3.bindings.design_primers() for arm-amplification and junction-validation primers
  • Decision: format/route by cell type (cycling vs post-mitotic) and insert size

The Single Most Important Modern Insight -- HDR is the minority pathway, and a donor without a blocking mutation is a self-destructing one

A Cas9 double-strand break is repaired by whichever pathway wins a kinetic race, and in most cells the winner is classical NHEJ (fast, all cell-cycle phases). MMEJ (microhomology, S/G2) and HDR (template-dependent, S/G2 only) are minority players, so unenhanced HDR knock-in is typically single-digit to low-double-digit percent -- that is normal, not a failure. The donor is not "the sequence to insert"; it is the toolkit for tilting a race NHEJ is structurally favored to win. The corollary, and the field's most expensive misread: a donor with perfect arms but no blocking mutation gets its successful edit erased -- the corrected allele still has an intact protospacer + PAM, so Cas9 re-cuts it and NHEJ scars it, and the indel reads out as "HDR failed" (indistinguishable from low HDR). So when someone reports "low HDR, lots of indels," the first question is not "how long are the arms?" -- it is "does the donor disrupt the PAM or seed?" A blocking mutation is mandatory and must be codon-checked; without it the readout is re-cutting, not HDR.

The Pathway Competition (everything follows from this)

PathwayCell cycleTemplateSignatureRelevance
c-NHEJall phases (dominant)noneindelsthe competitor; the engine HITI exploits
MMEJ / alt-EJ (Pol theta)S/G25-25 bp microhomologymicrohomology-flanked deletionsthe PITCh route
HDR / HRS/G2 onlysister chromatid or exogenous donorprecise, scarlessthe classic knock-in route; minority
SSAS/G2repeatsdeletion between repeatsnuisance

End resection (cell-cycle-gated, licensed in S/G2; 53BP1-RIF1 protects ends/pro-NHEJ, BRCA1 antagonizes it/pro-HDR) decides the fork. Consequences: post-mitotic cells barely do HDR -> for neurons/muscle/in-vivo tissue, HITI/HMEJ (NHEJ-based) is the correct first choice, not a fallback. Timing RNP+donor delivery into S/G2 raises HDR (Lin 2014, up to ~38% in HEK293T) -- the donor is necessary but the cell-cycle state gates it.

Donor Format Decision

FormatInsertArmsBest forCaveat
ssODN<= ~50 bp edits~30-60 nt each (total ~120-200 nt)point mutations, small tags, loxPsynthesis ceiling ~200 nt; strand choice contested
long ssDNA (Easi-CRISPR)~0.2-2 kb~50-100 ntcassettes, floxed/conditional alleles, zygote KIless toxic & less random integration than dsDNA; harder to make
dsDNA (PCR/linear)~0.1-1.5 kb~200-800 bpmedium cassettesdsDNA is toxic (innate sensing) + random integration
plasmid / HMEJup to several kb~500-2000 bplarge insertions, conditional allelesbackbone integration risk; slowest
AAV6<= ~4.5 kb (ITR-to-ITR, arms included)~400 bp-1 kbhard-to-transfect primary cells (HSPC, T, iPSC), in vivomanufacturing cost; cargo cap is a hard wall

Heuristics: point/small edit -> ssODN; 0.2-2 kb -> lssDNA over dsDNA (cleaner) for animal/zygote work; large cassette -> plasmid/HMEJ (lines) or AAV6+RNP (primary cells); post-mitotic -> HITI.

Homology Arms, Edit-to-Cut Distance, and the Blocking Mutation (the most-botched trio)

  • Arm length: ssODN ~30-60 nt each (more does not help, costs synthesis); dsDNA/plasmid ~500-800 bp sweet spot. Match the actual cell-line sequence, not the reference.
  • Edit-to-cut distance drives guide choice: HDR incorporation falls sharply with distance, so place the cut within ~10 bp of the edit (Paquet 2016). The guide and donor are a joint design -- a "great" guide cutting 25 bp away is worse than a mediocre one cutting 3 bp away. If no guide cuts within ~10 bp, HDR is the wrong tool -> reconsider base/prime editing.
  • Blocking mutation (mandatory, codon-checked): disrupt the PAM synonymously (preferred -- change a G in the NGG at a wobble position); if the PAM has no synonymous option, introduce silent seed-region mutations (PAM-proximal ~10-12 nt; PAM-distal mismatches are tolerated and do not block). The blocking edit must sit within the ~10 bp incorporation window (which is also where it blocks best). Paquet 2016 (CORRECT) raises per-allele accuracy ~10-fold and allows zygosity control by distance.

ssODN Strand & Asymmetry (an over-cited rule) + the reliable win

Richardson 2016 proposed an ssODN complementary to the non-target strand, asymmetric with the longer arm PAM-proximal (~91 nt) and the shorter PAM-distal (~36 nt). Subsequent systematic work could not reproduce this as universal: the optimal strand flips by locus and the asymmetric advantage often vanishes once both arms are >=30 nt. Treat it as a prior to test, not a law -- generate both strands and symmetric+asymmetric variants and test them. By contrast, phosphorothioate (PS) end-protection (2-3 terminal bases each end) is a near-universal cheap win (exonuclease resistance) -- encode these at opposite confidence levels.

MMEJ / Homology-Independent Routes (when HDR is the wrong tool)

  • PITCh / CRIS-PITCh (MMEJ, Nakade 2014): ~5-25 bp microhomologies instead of long arms; Pol theta joins donor to genome. Appeal is purely donor-construction convenience (microhomologies are primer overhangs); cost is error-prone junctions.
  • HITI (Suzuki 2016): homology-INDEPENDENT, NHEJ-based -> works in non-dividing cells. The donor carries the same Cas9 target site(s) in reverse orientation flanking the insert; wrong-orientation insertions reconstitute the site and get re-cut/ejected, right-orientation insertions destroy it and lock in. Junctions can carry small indels.
  • HMEJ (Yao 2017): ~800 bp arms PLUS flanking gRNA sites that linearize the donor in vivo; higher KI than HR/NHEJ/MMEJ in some contexts but ties/loses in others (mESC, N2a) -- test at the target locus.
SituationRoute
Point/small edit, cycling cellsssODN + HDR (with blocking mutation)
Medium/large cassette, cycling lineHDR (lssDNA/plasmid) or HMEJ
Large cassette, primary cells (HSPC/T/iPSC)AAV6 donor + RNP + HDR
Clean zygote/animal KI, <=2 kblssDNA Easi-CRISPR + HDR
Trivial donor construction wantedPITCh (MMEJ)
Non-dividing / post-mitotic / in vivoHITI (or HMEJ)
Edit far from any cut / single base-> base-editing-design or prime-editing-design (donor-free)

HDR Enhancers -- ranked experiments, not multipliers

Most enhancers are marginal, cell-type-specific, and frequently non-reproducible; the published fold-changes are line-specific maxima. A blocking mutation and a cut near the edit matter more than any small molecule.

  • First tier (try by default, low risk): cell-cycle timing of RNP delivery (Lin 2014); cold shock (32 C, 24-48 h; Guo 2018); PS end-protection; RNP+ssODN co-delivery.
  • Second tier (test in the target cells, expect variability): DNA-PKcs inhibition (M3814/nedisertib -- the most consistently potent small molecule); 53BP1 inhibition (i53 / Alt-R HDR Enhancer).
  • Bottom tier (mention with a reproducibility warning): SCR7 (widely un-reproducible), RS-1.
Show full SKILL.md (781 more words)Show less

Generate the Donor, Block Re-cutting (codon-checked), and Design Validation Primers

Goal: Assemble a donor that incorporates the edit AND survives re-cutting, with primers to amplify the arms and genotype the junction.

Approach: Extract arms flanking the cut, insert the edit, then add a blocking mutation -- disrupt the PAM synonymously if a wobble option exists, else introduce silent seed mutations -- verifying the change does not alter the encoded amino acid. Use primer3-py for arm-amplification/junction primers. (See examples/hdr_template_design.py for codon-aware blocking and a primer3 call.)

python
from Bio.Seq import Seq

def synonymous_pam_block(codon_table, pam_codon, alt_codon):
    '''Return True only if a PAM-disrupting codon swap keeps the same amino acid (silent).'''
    return codon_table.get(pam_codon) == codon_table.get(alt_codon)   # never mutate the PAM without this check

Per-Method Failure Modes

"I got an indel, so HDR failed"

Trigger: low edit, mostly indels, no blocking mutation. Mechanism: the corrected allele keeps an intact PAM -> Cas9 re-cuts -> NHEJ scar. Symptom: indels indistinguishable from no-HDR. Fix: add a codon-checked PAM/seed blocking mutation; the readout was re-cutting, not HDR.

Edit far from the cut

Trigger: best-cutting guide is 25 bp from the edit. Mechanism: HDR incorporation falls with distance. Symptom: only the blocking mutation is incorporated (useless silent-only allele) or no edit. Fix: choose a guide cutting within ~10 bp; if none, switch to base/prime editing.

Frame-unaware blocking mutation

Trigger: blindly changing the NGG's second G to A. Mechanism: the PAM may be in a coding frame. Symptom: an unintended missense/nonsense change. Fix: verify the swap is synonymous; else use silent seed mutations.

dsDNA in sensitive cells

Trigger: a plasmid/PCR donor in iPSC/primary/zygotes. Mechanism: dsDNA toxicity + random integration. Symptom: low viability, random integrants. Fix: use lssDNA (Easi-CRISPR) or AAV6.

HDR donor in post-mitotic cells

Trigger: ssODN/plasmid for neurons/in-vivo tissue. Mechanism: HDR runs only in S/G2. Symptom: essentially no knock-in. Fix: use HITI (NHEJ-based) or HMEJ.

Arms designed against the reference

Trigger: GRCh38 arms for a passaged/cancer line. Mechanism: line-specific SNPs in the arm or PAM/seed. Symptom: poor annealing or no cut. Fix: design against the cell line's actual sequence; account for ploidy/zygosity.

Quantitative Thresholds

ParameterValueSource
ssODN total length~120-200 ntsynthesis ceiling
ssODN arm~30-60 nt eachbelow ~30 HDR drops; above ~60 diminishing returns
dsDNA/plasmid arm~200-800 bp (up to ~2 kb)~500-800 bp common sweet spot
lssDNA insert~0.2-2 kbEasi-CRISPR range
AAV cargo<= ~4.5 kb (arms included)packaging limit (hard wall)
PITCh microhomology~5-25 bpMMEJ working range
Edit-to-cut distance<= ~10 bpHDR incorporation falls with distance (Paquet 2016)
Phosphorothioate2-3 terminal bases each endexonuclease resistance
Cold shock32 C, 24-48 hG2/M accumulation (Guo 2018)
Typical raw HDRsingle-digit to ~20% (up to ~38-60% optimized)minority pathway

Common Errors

Error / symptomCauseSolution
Low HDR, mostly indelsno blocking mutation (re-cutting)add codon-checked PAM/seed block
Only the silent mutation incorporatededit too far from cutcut within ~10 bp or switch to base/prime editing
Toxicity / random integrationdsDNA in sensitive cellslssDNA or AAV6
No knock-in in neurons/in vivoHDR donor in post-mitotic cellsHITI/HMEJ
AAV donor will not packagearms + insert exceed ~4.5 kbshorten arms/insert; budget against the cap

References

  • Richardson CD, Ray GJ, DeWitt MA, Curie GL, Corn JE (2016). Enhancing homology-directed genome editing by catalytically active and inactive CRISPR-Cas9 using asymmetric donor DNA. Nat Biotechnol 34(3):339-344.
  • Lin S, Staahl BT, Alla RK, Doudna JA (2014). Enhanced homology-directed human genome engineering by controlled timing of CRISPR/Cas9 delivery. eLife 3:e04766.
  • Paquet D, Kwart D, Chen A, et al. (2016). Efficient introduction of specific homozygous and heterozygous mutations using CRISPR/Cas9 (CORRECT). Nature 533(7601):125-129.
  • Quadros RM, Miura H, Harms DW, et al. (2017). Easi-CRISPR: a robust method for one-step generation of mice carrying conditional and insertion alleles using long ssDNA donors and CRISPR ribonucleoproteins. Genome Biol 18:92.
  • Nakade S, Tsubota T, Sakane Y, et al. (2014). Microhomology-mediated end-joining-dependent integration of donor DNA in cells and animals using TALENs and CRISPR/Cas9 (PITCh). Nat Commun 5:5560.
  • Suzuki K, Tsunekawa Y, Hernandez-Benitez R, et al. (2016). In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration (HITI). Nature 540(7631):144-149.
  • Yao X, Wang X, Hu X, et al. (2017). Homology-mediated end joining-based targeted integration using CRISPR/Cas9 (HMEJ). Cell Res 27(6):801-814.
  • Guo Q, Mintier G, Ma-Edmonds M, et al. (2018). 'Cold shock' increases the frequency of homology directed repair gene editing in induced pluripotent stem cells. Sci Rep 8:2080.
  • Untergasser A, Cutcutache I, Koressaar T, et al. (2012). Primer3 -- new capabilities and interfaces. Nucleic Acids Res 40(15):e115.
  • grna-design - Choose the guide; HDR consumes its cut site (tightly coupled via edit-to-cut distance)
  • base-editing-design - Donor-free alternative for single-base transitions far from any cut
  • prime-editing-design - Donor-free precise small edits, and twinPE/PASTE for large insertions
  • primer-design/primer-basics - PCR primers for arm amplification and junction genotyping
  • primer-design/primer-validation - Check genotyping primers for dimers and hairpins
  • primer-design/primer-specificity - Confirm the genotyping amplicon is unique (off-target/pseudogenes)
  • sequence-io/read-sequences - Parse GenBank CDS/start/stop features for tag placement and codon-aware design
  • variant-calling/variant-annotation - Confirm the installed edit and its consequence

© 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 genome-engineering/hdr-template-design of GPTomics/bioSkills.

  • SKILL.md
  • examples/hdr_template_design.py
  • 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

Bio Genome Engineering Hdr Template Design 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.

Bio Genome Engineering Hdr Template Design compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bio Genome Engineering Hdr Template Design this skillGPTomics/bioSkills1.2k1 repos~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 GPTomics/bioSkills

All 559 skills in this repo
  • Bio Alignment Io

    GPTomics/bioSkills

    Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.

    1.2k GitHub starsUsed in 3 repos~4.9k tokens
    Auto-check passed
  • bioSkills Installer

    GPTomics/bioSkills

    Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.

    1.2k GitHub starsUsed in 1 repo~789 tokens
    Auto-check passed
  • Bio Write Sequences

    GPTomics/bioSkills

    Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.

    1.2k GitHub starsUsed in 3 repos~2.1k tokens
    Auto-check passed
  • Amplicon Primer Clipping

    GPTomics/bioSkills

    Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.

    1.2k GitHub starsUsed in 2 repos~2.2k tokens
    Auto-check passed
  • Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.

    1.2k GitHub starsUsed in 2 repos~3.6k tokens
    Auto-check passed
  • Bio Alignment Indexing

    GPTomics/bioSkills

    Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.

    1.2k GitHub starsUsed in 2 repos~2.4k tokens
    Auto-check passed

Questions about Bio Genome Engineering Hdr Template Design

What does Bio Genome Engineering Hdr Template Design do?

Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the…. Bio Genome Engineering Hdr Template Design is an agent skill from GPTomics/bioSkills. Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut.

When should I use Bio Genome Engineering Hdr Template Design?

Bio Genome Engineering Hdr Template Design fits situations like: designing a donor for a point mutation; epitope/fluorophore tag; allele replacement; HDR efficiency is low.

How do I install Bio Genome Engineering Hdr Template Design in Claude Code?

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

How do I install Bio Genome Engineering Hdr Template Design in Codex?

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

Can I use Bio Genome Engineering Hdr Template Design 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-genome-engineering-hdr-template-design -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-genome-engineering-hdr-template-design, .gemini/skills/bio-genome-engineering-hdr-template-design, .github/skills/bio-genome-engineering-hdr-template-design and .opencode/skills/bio-genome-engineering-hdr-template-design in your project.

What does Bio Genome Engineering Hdr Template Design need to run?

Going by SKILL.md and its folder, Bio Genome Engineering Hdr Template Design needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Bio Genome Engineering Hdr Template Design access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Genome Engineering Hdr Template Design 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 Genome Engineering Hdr Template Design use?

Bio Genome Engineering Hdr Template Design 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 Genome Engineering Hdr Template Design 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.

What are the alternatives to Bio Genome Engineering Hdr Template Design?

Skills that share tags, products or a category with Bio Genome Engineering Hdr Template Design: 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 Genome Engineering Hdr Template Design?

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.