---

name: pandasai description: "Routes PandasAI tasks for conversational dataframe analysis, semantic data layers, custom skills, sandboxed execution, CLI usage, and repo maintenance guidance." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PandasAI Repo Skill

Use this skill when a task involves the PandasAI Python package (pandasai), especially natural-language analysis over pandas dataframes, semantic dataset schemas, PandasAI custom skills, sandboxed code execution, or the pai CLI.

PandasAI 3.x turns dataframes and semantic datasets into conversational analysis surfaces. It uses an LLM to generate Python/SQL code, executes that code locally or through an optional sandbox, and returns typed response objects.

First checks

For a fresh environment, install the core package and the LLM/provider extension that matches the user's provider:

pip install pandasai
pip install pandasai-litellm        # common provider bridge
# or: pip install pandasai-openai   # OpenAI/Azure extension

Minimum import check:

python - <<'PY'
import pandasai as pai
from pandasai import Agent, DataFrame
print("pandasai import ok", pai)
print("DataFrame", DataFrame)
print("Agent", Agent)
PY

For local deterministic smoke checks without real LLM credentials, run the bundled diagnostic:

python scripts/check_pandasai_environment.py --chat-smoke

Read references/repo-provenance.md before refreshing this skill or comparing it against a checkout. Read references/package-overview.md for the shared public API map and references/troubleshooting.md for cross-cutting install/import/optional-dependency issues.

Route map

User task	Read next
Configure an LLM, ask questions against a DataFrame, inspect response objects, use Agent, debug generated code, or migrate from SmartDataframe/SmartDatalake	sub-skills/conversational-analysis/SKILL.md
Create/load semantic datasets, validate schema.yaml, define CSV/parquet/SQL/view sources, transformations, group_by, or relation rules	sub-skills/semantic-layer/SKILL.md
Add custom callable functions to PandasAI with @pai.skill, inspect SkillType, or manage the global skills registry	sub-skills/custom-skills/SKILL.md
Decide whether to sandbox generated code, use the optional Docker sandbox, or implement/check a custom Sandbox subclass	sub-skills/sandbox-and-security/SKILL.md
Use the pai CLI, validate/login with a PandaBI API key, run guided dataset creation, or follow repo maintainer commands	sub-skills/cli-and-project-ops/SKILL.md

Common workflow skeleton

1. Identify whether the user is using raw pandas data, a PandasAI DataFrame, or a semantic dataset path.
2. If the task calls .chat() or pai.chat(), make sure an LLM is configured with pai.config.set({"llm": llm}) unless the user is deliberately running a FakeLLM smoke test.
3. If the task writes datasets, confirm the dataset path is organization/dataset with lowercase hyphenated segments and that generated files belong under a project datasets/ directory.
4. If prompts, data, or generated code are untrusted, route to sandbox guidance before running code.
5. If optional extensions are missing, install only the needed package: pandasai-litellm, pandasai-openai, pandasai-sql[...], or pandasai-docker.

Important constraints

• PandasAI executes LLM-generated code. Treat generated code as unsafe unless it is reviewed or executed inside an appropriate sandbox.
• Core chat code generation expects use of an execute_sql_query(...) function and a result = {"type": ..., "value": ...} dictionary.
• Chart responses use the plot result type internally and return a chart response object; save or show charts through response methods.
• SQL connector, Docker sandbox, external LLM provider, cloud connector, and enterprise vector-store features are optional and should not be assumed in a base environment.
• SmartDataframe and SmartDatalake are compatibility wrappers that emit deprecation warnings; prefer pai.DataFrame, pai.chat, and Agent for new code.

Shared references and scripts

• references/package-overview.md summarizes the package surface, optional extensions, and route ownership.
• references/troubleshooting.md lists cross-cutting install, import, LLM, SQL, sandbox, and project-root issues.
• references/maintainer-notes.md captures contributor-oriented test/lint/package guidance without requiring full extras for ordinary package use.
• scripts/check_pandasai_environment.py checks imports, version metadata, CLI availability, optional extensions, and a deterministic offline chat smoke.

Avoid when

Use a generic pandas/data-science skill instead if the user only needs standard pandas operations without PandasAI APIs. Use an LLM provider-specific or gateway skill when the problem is provider routing, billing, or model hosting outside PandasAI's configuration surface. Use a Docker or security-hardening skill when the task is container infrastructure rather than PandasAI sandbox integration.