open-research-skills

Pass work between ORS skills with a Material Passport — a structured JSON envelope that captures inputs, outputs, decisions, and provenance so a downstream skill can resume without re-deriving context. Use when one skill produces output that another skill must consume, especially across category boundaries (e.g. analysis → writing, sequencing → statistics).

Discover and maintain `## Cross-references` between ORS skills. Given a new or edited skill, suggest related skills (same category, shared tags, complementary workflows). Verify all xref slugs resolve to existing SKILL.md files. Use when adding a new skill, when sibling skills move, or when an audit reveals dangling xrefs.

7-mode AI failure-mode checklist applied before publishing any ORS artifact (skill, manuscript section, dataset card, agent output). Catches: fabricated citations, silent failures, scope creep, missing provenance, unsupervised writes, version drift, hallucinated APIs. Use as the final pass before any 'publish', 'release', 'merge', or 'finalize' action.

Version, changelog, and marketplace workflow for ORS skills. Semver discipline, breaking-change protocol, marketplace.json update, README refresh, and the v0.X.Y → v1.0.0 graduation criteria. Use when bumping a skill version, publishing a release, or upgrading the ORS package version.

Author a new ORS skill from scratch using the v0.4.0 schema. Produces a valid SKILL.md (frontmatter + metadata + sections) following spec/skill-format.md, guided by spec/author-guide.md, validated by scripts/validate-skills.py. Use when a user says 'add a new skill', 'create a skill for X', or 'I want to contribute a skill'.

Process many FASTA/FASTQ/GenBank files in batch — merge, split, convert, summarize, organize — using Biopython 1.83+.

Align short reads to a reference with Bowtie2 — the standard aligner for ChIP-seq, ATAC-seq, and other short-fragment applications where sensitivity for short indels matters less than speed.

Align Illumina short reads to a reference genome with BWA-MEM — including index, MEM algorithm, paired-end, alt-contig handling, and 2026 best practices.

Use BWA-MEM2 — the SIMD-accelerated BWA-MEM replacement — for 2-3x speedup on human genome alignment with identical results to BWA-MEM.

Compute codon usage frequencies, RSCU, CAI, and tRNA adaptation — for gene expression prediction, heterologous expression design, and comparative genomics.

Read and write gzip, bzip2, and BGZF-compressed FASTA/FASTQ using Python stdlib and Biopython — including indexable BGZF.

Access, filter, and trim Phred quality scores in FASTQ reads using Biopython 1.83+ — including per-position profiles and encoding auto-detection.

Filter FASTA/FASTQ records by length, identity, regex on headers, GC window, complexity, and custom predicates — streaming with Biopython 1.83+.

Convert between sequence and annotation formats using Biopython SeqIO.convert and custom writers — including batch, lossy, and round-trip-safe patterns.

Splice-aware RNA-seq alignment with HISAT2 — fast, memory-efficient, and the default in many bulk RNA-seq pipelines alongside STAR.

Find patterns, motifs, and binding sites in DNA/RNA/protein sequences — using regex, IUPAC patterns, position weight matrices, and modern motif databases (JASPAR 2024, CIS-BP).

Keep R1/R2 reads in lockstep — synchronize iteration, filter pairs together, interleave, and deinterleave — using Biopython and the 2026 best practices.

Programmatically read, modify, filter, and write BAM/CRAM/VCF files using pysam 0.22+ — the Python interface to htslib for NGS I/O, variant calling, and custom pipelines.

Read and write FASTA, FASTQ, GenBank, EMBL using Biopython SeqIO — including indexed random access, conversion, and 2026 best practices.

Compute reverse complements, complements, and strand-aware operations on DNA/RNA — including IUPAC ambiguity support, batch processing, and the orientation trap.

Understand the SAM format header, alignment records, FLAGs, MAPQ, CIGAR, and coordinate conventions — the foundation for all downstream BAM analysis.

Process BAM files with samtools — sort, index, merge, flagstat, mark duplicates, fixmate, depth, filter, and stream — the standard CLI toolkit for 2026.

Master the Seq, MutableSeq, and SeqRecord objects — slicing, immutability, ambiguity codes, alphabets, and the 2026 Biopython alphabet deprecation reality.

Compute GC content, molecular weight, melting temperature, isoelectric point, and instability index for DNA, RNA, and protein sequences.

Slice, subset, and extract regions from sequences and SeqRecords — including feature extraction, fuzzy ends, and the location algebra behind GenBank features.

Compute length, GC, N50, L50, and per-file summaries for FASTA/FASTQ — including the N50 trick and modern streaming stats with Polars.

Splice-aware RNA-seq alignment with STAR — the gold-standard aligner for novel junction discovery, with 2-pass mapping and genome-generation strategies for 2026.

Transcribe DNA to RNA (and back), translate coding sequences to protein using NCBI codon tables, and pick the right genetic code for mitochondria, plastids, or non-standard organisms.

Build an academic CV for US/EU faculty or postdoc applications, with proper section ordering, professional service, mentoring statements, and NSF/NIH biosketch cross-reference. Use when applying to tenure-track or postdoctoral positions.

Prepare for an academic job interview: job talk, chalk talk, 1:1 meetings with PIs and deans, dean's talk, dinner etiquette, thank-you emails, and start-up package negotiation (funds, lab space, equipment, moving, partner accommodation). Use when invited for an on-site or virtual on-site for a faculty position.

Apply for predoctoral fellowships (Hertz, NDSEG, NSF GRFP, Ford, AAAS Mass Media) and the Packard postdoctoral fellowship. Covers personal statement, research statement, transcript curation, grad school list, referee coordination, and submission timing. Use when applying for a major external fellowship as a PhD applicant or early postdoc.

Translate a PhD into an industry resume, navigate technical screens and system design for ML roles, and compare biotech, big-pharma, ML-for-bio, science writing, and consulting paths. Includes startup vs established company trade-offs. Use when a PhD student or postdoc is preparing for a job search outside academia.

Negotiate academic and industry offers: startup packages, postdoc salaries, industry offers and counter-offers, dual-career accommodation, equity vs salary trade-offs, BATNA analysis, and the difference between written and verbal offers. Use when preparing for, conducting, or evaluating a negotiation.

Assemble a tenure case from research statement, teaching portfolio, external letters, internal documentation, mentoring record, service, and counter-narrative for low-citation fields. Use when preparing a tenure case packet for a US/EU tenure review.

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

Designs covalent inhibitors and warheads targeting cysteine (most common, ~98% of covalent drugs), lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.

Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.

Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.