Polars

What it does

When your agent uses it

• Tasks that involve DataFrames

Example prompts

• “/polars”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Use lazy evaluation for large datasets
2. 2Avoid Python functions in hot paths
3. 3Use streaming for very large data
4. 4Select only needed columns early
5. 5Use appropriate data types

What it can do on your machine

Read from SKILL.md and the folder at commit 46b4d8b. 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

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

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

• Network

  No URLs in SKILL.md.

  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

Polars loads about 2.3k tokens when it runs, and up to ~20k if it reads all its reference files. Until then it costs about 44 tokens; SKILL.md has 545 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~44

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~20k

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

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 davila7/claude-code-templates at commit 46b4d8b, republished under its MIT licence (© davila7). 545 words, ~2,302 tokens.

name

polars

description

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

uv pip install polars

import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["NY", "LA", "SF"]
})

# Select columns
df.select("name", "age")

# Filter rows
df.filter(pl.col("age") > 25)

# Add computed columns
df.with_columns(
    age_plus_10=pl.col("age") + 10
)

# Expression-based computation
df.select(
    pl.col("name"),
    (pl.col("age") * 12).alias("age_in_months")
)

df = pl.read_csv("file.csv")  # Reads immediately
result = df.filter(pl.col("age") > 25)  # Executes immediately

lf = pl.scan_csv("file.csv")  # Doesn't read yet
result = lf.filter(pl.col("age") > 25).select("name", "age")
df = result.collect()  # Now executes optimized query

# Select specific columns
df.select("name", "age")

# Select with expressions
df.select(
    pl.col("name"),
    (pl.col("age") * 2).alias("double_age")
)

# Select all columns matching a pattern
df.select(pl.col("^.*_id$"))

# Single condition
df.filter(pl.col("age") > 25)

# Multiple conditions (cleaner than using &)
df.filter(
    pl.col("age") > 25,
    pl.col("city") == "NY"
)

# Complex conditions
df.filter(
    (pl.col("age") > 25) | (pl.col("city") == "LA")
)

# Add new columns
df.with_columns(
    age_plus_10=pl.col("age") + 10,
    name_upper=pl.col("name").str.to_uppercase()
)

# Parallel computation (all columns computed in parallel)
df.with_columns(
    pl.col("value") * 10,
    pl.col("value") * 100,
)

# Basic grouping
df.group_by("city").agg(
    pl.col("age").mean().alias("avg_age"),
    pl.len().alias("count")
)

# Multiple group keys
df.group_by("city", "department").agg(
    pl.col("salary").sum()
)

# Conditional aggregations
df.group_by("city").agg(
    (pl.col("age") > 30).sum().alias("over_30")
)

# Add group statistics to each row
df.with_columns(
    avg_age_by_city=pl.col("age").mean().over("city"),
    rank_in_city=pl.col("salary").rank().over("city")
)

# Multiple grouping columns
df.with_columns(
    group_avg=pl.col("value").mean().over("category", "region")
)

# Eager
df = pl.read_csv("file.csv")
df.write_csv("output.csv")

# Lazy (preferred for large files)
lf = pl.scan_csv("file.csv")
result = lf.filter(...).select(...).collect()

df = pl.read_parquet("file.parquet")
df.write_parquet("output.parquet")

df = pl.read_json("file.json")
df.write_json("output.json")

# Inner join
df1.join(df2, on="id", how="inner")

# Left join
df1.join(df2, on="id", how="left")

# Join on different column names
df1.join(df2, left_on="user_id", right_on="id")

# Vertical (stack rows)
pl.concat([df1, df2], how="vertical")

# Horizontal (add columns)
pl.concat([df1, df2], how="horizontal")

# Diagonal (union with different schemas)
pl.concat([df1, df2], how="diagonal")

# Pivot (wide format)
df.pivot(values="sales", index="date", columns="product")

# Unpivot (long format)
df.unpivot(index="id", on=["col1", "col2"])

df.assign(
    col_a=lambda df_: df_.value * 10,
    col_b=lambda df_: df_.value * 100
)

df.with_columns(
    col_a=pl.col("value") * 10,
    col_b=pl.col("value") * 100,
)

pl.when(condition).then(value).otherwise(other_value)

df.select(pl.col("^.*_value$") * 2)  # Regex pattern

pl.col("x").fill_null(0)
pl.col("x").is_null()
pl.col("x").drop_nulls()

© davila7, 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 6 other files (references) in cli-tool/components/skills/scientific/polars of davila7/claude-code-templates.

• SKILL.md
• references/best_practices.md
• references/core_concepts.md
• references/io_guide.md
• references/operations.md
• references/pandas_migration.md
• references/transformations.md

Open the folder on GitHubat commit 46b4d8b

Used in 14 other repositories

We found 26 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 14 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

• jimmc414/Kosmoskosmos-claude-scientific-skills/scientific-skills/polars
• Microck/ordinary-claude-skillsskills_all/claude-scientific-skills/scientific-skills/polarsskills_categorized/scientific-computing/claude-scientific-skills/scientific-skills/polars
• aiskillstore/marketplaceskills/davila7/polarsskills/k-dense-ai/polars
• sickn33/agentic-awesome-skillsskills/polarsplugins/agentic-awesome-skills/skills/polars+1 more
• lamm-mit/scienceclawskills/polars
• agent-skills-hub/agent-skills-hubskills/polars
• lingxling/awesome-skills-cnantigravity-awesome-skills/skills/polarsantigravity-awesome-skills/plugins/agentic-awesome-skills/skills/polars+1 more
• foryourhealth111-pixel/Vibe-Skillsbundled/skills/polars
• LeonChaoX/qinyan-academic-skillsskills/11-数据分析与统计建模/polars
• wu-yc/LabClawskills/general/polars
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/polars
• henryalouf/ruflow.agents/skills/polars.agents/skills/antigravity-awesome-skills/skills/polars+4 more
• BioTender-max/awesome-bio-agent-skillsskills/kdense/polarsskills/openclaw/polars
• infometa/workbuddyskillsplugins/codebuddy-plugins-official/external_plugins/scientific-skills/polars