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Use when the user needs to implement a basic Sciris features — finding array values, plotting with date formatting, objdict containers, saving/loading objects, or parallelization.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Use when saving or loading files with Sciris — sc.save, sc.load, sc.savejson, sc.loadjson, sc.saveyaml, sc.readyaml, sc.savearchive, sc.loadarchive, sc.savefig, sc.loadmetadata, sc.getfilelist, sc.thispath, sc.makefilepath, sc.rmpath, sc.metadata, sc.compareversions, sc.require, or version/reproducibility tracking.
Use when working with Sciris miscellaneous utilities — sc.mergedicts, sc.mergelists, sc.tolist, sc.toarray, sc.isnumber, sc.suggest, sc.download, sc.runcommand, sc.importbypath, sc.loadtext, sc.help, sc.traceback, sc.autolist, sc.pp, or type checking/conversion.
Use when working with advanced Sciris features — nested dictionaries (sc.makenested, sc.getnested, sc.setnested, sc.iternested, sc.search, sc.iterobj), context blocks (sc.capture, sc.tryexcept), sc.smoothinterp interpolation, sc.asd optimization, sc.animation, sc.savemovie, or sc.printjson.
Use when working with NumPy arrays in Sciris — finding indices, nearest values, concatenation, handling NaN/missing values, smoothing data, 2D Gaussian smoothing, or linear regression with sc.findinds, sc.findnearest, sc.cat, sc.rmnans, sc.fillnans, sc.smooth, sc.rolling, sc.linregress.
Use when working with dates, times, or timing in Sciris — sc.timer, sc.tic, sc.toc, sc.date, sc.daterange, sc.datedelta, sc.now, sc.getdate, sc.time, sc.timedsleep, sc.randsleep, date format conversion, or timing code blocks.
Use when working with Sciris dictionaries or dataframes — sc.odict, sc.objdict, sc.dataframe, integer indexing of dicts, enumitems, object-syntax access, dataframe creation with dtypes, appendrow, or sc.dataframe.cat.
| name | sciris-intro |
| description | Use when the user needs to implement a basic Sciris features — finding array values, plotting with date formatting, objdict containers, saving/loading objects, or parallelization. |
Quick reference for Sciris' most commonly used features. See the full tutorial: docs/tutorials/tut_intro.ipynb.
If you need more detail, use your MCP tools (Context7 or GitMCP) to look up current Sciris documentation, or consult the other Sciris skills.
Use the sciris-arrays skill for more details.
import numpy as np
import sciris as sc
data = np.random.rand(50)
inds = sc.findinds(data > 0.9) # Find indices matching condition
mean_str = sc.arraymean(data) # Mean ± std as string
joined = sc.strjoin(inds) # Join values into comma-separated string
Use the sciris-dates skill for more details.
dates = sc.daterange('2022-01-01', '2022-02-28', as_date=True)
values = 1e6 * np.random.randn(59)**2
data = sc.dataframe(x=dates, y=values) # Shortcut to pd.DataFrame
plt.scatter(data.x, data.y)
sc.dateformatter() # Format date axis
sc.SIticks() # SI notation on y-axis
Use the sciris-dicts skill for more details.
data = sc.objdict(a=[1,2,3], b=[4,5,6])
assert data.a == data['a'] == data[0] # Three ways to access
assert data[:].sum() == 21 # Slice and sum
for i, key, value in data.enumitems(): # Enumerate with keys
print(f'Item {i}: {key} = {value}')
Use the sciris-files skill for more details.
sc.save('my-sim.obj', sim) # Save (gzipped pickle)
new_sim = sc.load('my-sim.obj') # Load — methods still work!
new_sim.plot()
Use the sciris-parallel skill for more details.
results = sc.parallelize(func, iterkwargs=dict(scale=[40,30,20,10]), x_offset=5, y_offset=10)
Use the sciris-plotting skill for more details.
sc.options(dpi=120, jupyter=True) # Set DPI and backend
sc.boxoff() # Remove top/right spines