scientific-computing
Use when "scientific computing", "astronomy", "astropy", "bioinformatics", "biopython", "symbolic math", "sympy", "statistics", "statsmodels", "scientific Python"
How do I install this agent skill?
npx skills add https://github.com/eyadsibai/ltk --skill scientific-computingIs this agent skill safe to install?
- Gen Agent Trust Hubpass
This skill is a purely informational guide that provides instructions and descriptions for using well-known scientific Python libraries such as AstroPy, BioPython, SymPy, and Statsmodels. It contains no executable code, shell commands, or security risks.
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No alerts
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Risk: LOW · No issues
- Runlayerpass
1 file scanned · No issues
What does this agent skill do?
Scientific Computing
Domain-specific Python libraries for scientific applications.
Libraries
| Library | Domain | Purpose |
|---|---|---|
| AstroPy | Astronomy | Coordinates, units, FITS files |
| BioPython | Bioinformatics | Sequences, BLAST, PDB |
| SymPy | Mathematics | Symbolic computation |
| Statsmodels | Statistics | Statistical modeling, tests |
AstroPy
Astronomy and astrophysics computations.
Key capabilities:
- Units: Physical unit handling with automatic conversion
- Coordinates: Celestial coordinate systems (ICRS, galactic, etc.)
- Time: Astronomical time scales (UTC, TAI, Julian dates)
- FITS: Read/write FITS astronomical data format
Key concept: Unit-aware calculations prevent errors from unit mismatches.
BioPython
Bioinformatics - sequences, structures, databases.
Key capabilities:
- Sequences: DNA/RNA/protein manipulation, translation, complement
- File parsing: FASTA, GenBank, PDB formats
- BLAST: Local and remote sequence alignment
- NCBI Entrez: Database access (nucleotide, protein, taxonomy)
Key concept: SeqIO for reading any sequence format, Seq for sequence operations.
SymPy
Symbolic mathematics - algebra, calculus, equation solving.
Key capabilities:
- Algebra: Solve equations, simplify, expand, factor
- Calculus: Derivatives, integrals, limits, series
- Linear algebra: Matrix operations, eigenvalues
- Printing: LaTeX output for documentation
Key concept: Work with symbols, not numbers. Get exact answers, not approximations.
Statsmodels
Statistical modeling with R-like formula interface.
Key capabilities:
- Regression: OLS, logistic, generalized linear models
- Time series: ARIMA, VAR, state space models
- Statistical tests: t-tests, ANOVA, diagnostics
- Formula API: R-style formulas (
y ~ x1 + x2)
Key concept: model.summary() gives comprehensive statistical output like R.
Decision Guide
| Domain | Library |
|---|---|
| Astronomy/astrophysics | AstroPy |
| Biology/genetics | BioPython |
| Symbolic math | SymPy |
| Statistical analysis | Statsmodels |
| Numerical computing | NumPy, SciPy |
| Data manipulation | Pandas |
Resources
- AstroPy: https://docs.astropy.org
- BioPython: https://biopython.org/docs/
- SymPy: https://docs.sympy.org
- Statsmodels: https://www.statsmodels.org
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/eyadsibai/ltk/scientific-computing">View scientific-computing on skillZs</a>