13c-metabolic-flux
Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps,โฆ
Provides Python/HTSlib workflows for genomic files. Used when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.
Claude Code
$ npx -y skills add K-Dense-AI/scientific-agent-skills --skill pysam --agent claude-code
Codex
$ npx skills add K-Dense-AI/scientific-agent-skills --skill pysam -a codex -y
Cursor
$ npx skills add K-Dense-AI/scientific-agent-skills --skill pysam -a cursor -y
Gemini CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill pysam -a gemini-cli -y
GitHub Copilot
$ npx skills add K-Dense-AI/scientific-agent-skills --skill pysam -a github-copilot -y
OpenCode
$ npx skills add K-Dense-AI/scientific-agent-skills --skill pysam -a opencode -y
Windsurf
$ npx skills add K-Dense-AI/scientific-agent-skills --skill pysam -a windsurf -y
name: pysam description: Provides Python/HTSlib workflows for genomic files. Used when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references. license: MIT allowed-tools: Read Write Edit Bash compatibility: Requires Python 3.9+ and pysam 0.24.1. Bundled scripts use local files. CRAM decoding may require the matching reference FASTA or an explicitly configured REF_PATH/REF_CACHE. metadata: version: "2.3" last-reviewed: "2026-10-01" skill-author: K-Dense Inc.
Use pysam for low-level, streaming access to HTSlib-supported genomic formats:
Current upstream baseline: **pysam 0.24.1** (7 September 2026), wrapping HTSlib/samtools/bcftools 1.24. Read `references/sources.md` before updating version-specific guidance.
Use the pinned release for reproducible work:
uv pip install "pysam==0.24.1"
Confirm the runtime:
import pysam print(pysam.__version__) # 0.24.1 print(pysam.__samtools_version__) # 1.24
Prebuilt wheels are available for supported macOS and Linux platforms. A source build needs a C compiler and HTSlib build dependencies; read the official installation guide linked from `references/sources.md`.
Before writing code:
1. Identify the real format, compression, sort order, and available index. 2. Decide whether coordinates are numeric Python coordinates or a region string. Do not mix them. 3. For CRAM, identify the exact reference assembly and FASTA. 4. Prefer indexed region access; use sequential iteration only when intended. 5. Preserve headers when writing and write to a new path by default. 6. State filtering semantics: mapping/base quality, flags, overlap handling, duplicate handling, and pileup depth cap.
For unfamiliar files, start with the bundled read-only inspector:
python scripts/inspect_hts.py sample.bam python scripts/inspect_hts.py cohort.vcf.gz python scripts/inspect_hts.py reference.fa
| Script | Purpose | Typical call | |---|---|---| | `scripts/inspect_hts.py` | Metadata-only inspection for alignment, variant, FASTA, FASTQ, and tabix files | `python scripts/inspect_hts.py sample.cram --reference ref.fa` | | `scripts/alignment_qc.py` | Streaming aggregate read/QC counts as JSON | `python scripts/alignment_qc.py sample.bam --max-records 100000` | | `scripts/variant_summary.py` | Streaming variant, FILTER, and genotype summary as JSON | `python scripts/variant_summary.py cohort.vcf.gz --region chr1:1-1000000` | | `scripts/filter_alignments.py` | Filter SAM/BAM/CRAM without changing record order | `python scripts/filter_alignments.py input.bam output.bam --exclude-secondary` |
All scripts refuse to overwrite existing outputs. The filter also refuses stale output indexes and offers `--index --csi` for large BAM contigs. FASTA inspection requires an existing `.fai`; create it explicitly with `pysam.faidx()` first. Run each with `--help` for coordinate, index, and privacy notes.
Examples use illustrative filenames and assay-specific thresholds. The local synthetic suite exercises these API patterns on pysam 0.24.1; remote storage and biological datasets are not part of that validation.
**Numeric coordinates accepted by pysam APIs are 0-based, half-open.** This includes numeric `AlignmentFile.fetch()`, `VariantFile.fetch()`, `FastaFile.fetch()`, `TabixFile.fetch()`, and `pileup()` arguments.
**Region strings are samtools-style: 1-based and inclusive.**
# The same 100 bases:
bam.fetch("chr1", 99, 199) # [99, 199)
bam.fetch(region="chr1:100-199") # 1-based inclusiveVCF text uses 1-based `POS`, while record properties expose both systems:
record.pos # 1-based record.start # 0-based inclusive record.stop # 0-based exclusive
Read `references/coordinates_and_indexing.md` for format conversions, overlap semantics, index choices, and contig-name checks.
Use context managers and explicit modes:
import pysam
with pysam.AlignmentFile("sample.bam", "rb", threads=4) as bam:
for read in bam.fetch("chr1", 1_000, 2_000):
if (
not read.is_unmapped
and not read.is_secondary
and not read.is_supplementary
and read.mapping_quality >= 30
):
print(read.query_name, read.reference_start, read.cigarstring)Use `fetch(until_eof=True)` to stream every record in file order, including unplaced unmapped reads, without requiring an index:
with pysam.AlignmentFile("sample.bam", "rb") as bam:
for read in bam.fetch(until_eof=True):
...Important distinctions:
unmapped-flagged record can have a reference position. Filter `is_unmapped`.
15 plus `read_callback="all"`.
overlap, orphan, and `max_depth=8000` defaults.
For exact-region pileups, set `truncate=True` and explicit filters:
with pysam.FastaFile("reference.fa") as fasta, pysam.AlignmentFile(
"sample.bam", "rb"
) as bam:
for column in bam.pileup(
"chr1",
1_000,
2_000,
truncate=True,
stepper="samtools",
fastafile=fasta,
min_mapping_quality=20,
min_base_quality=๐ Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility โ now works with any AI agent that supports the open Agent Skills standard, not just Claude.
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