pydantic-airtable-api

Manage Airtable tables and records via the pydantic-airtable Python library. Use when creating, listing, updating, deleting, or inspecting Airtable records or tables; syncing a Pydantic model to Airtable; validating schema against a model; or writing Python code that uses AirtableModel, AirtableManager, or AirtableClient.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "pydantic-airtable-api" with this command: npx skills add grishick/pydantic-airtable

Pydantic Airtable API

Use this skill for practical Airtable management through pydantic-airtable.

Keep SKILL.md focused on workflow. Read references/api-surface.md only when exact method names or signatures matter.

Setup

Install the library if needed:

pip install pydantic-airtable

Provide credentials via environment:

export AIRTABLE_ACCESS_TOKEN="pat..."
export AIRTABLE_BASE_ID="app..."

Most bundled scripts also accept --table and optional --base-id overrides.

Safety notes

  • Use a scoped Airtable token with the least privileges needed.
  • Prefer a test base before pointing the scripts at production data.
  • scripts/model_ops.py --module ./path.py will import and execute that Python module. Only use it with trusted local code.
  • --fields, --records, and --updates accept @file.json, which reads local files from disk. Inspect those files before passing them in.
  • Installing pydantic-airtable is normal for this skill, but use a virtualenv/container if you want tighter isolation.

Choose the right layer

  • Use AirtableManager for base, table, schema, and direct record operations.
  • Use AirtableClient for low-level record APIs and batch record operations.
  • Use AirtableModel when the user wants typed Pydantic models with CRUD helpers.

For exact method coverage, read references/api-surface.md.

Default workflow

  1. Confirm credentials are present.
  2. For one-off operational tasks, prefer the scripts in scripts/.
  3. For reusable application code, prefer AirtableModel or AirtableManager snippets.
  4. For schema-sensitive work, validate or sync before writing a lot of data.
  5. Catch Airtable-specific exceptions around networked operations.

Bundled scripts

scripts/manage_records.py

Use for record CRUD without rewriting the same setup code.

Supported actions:

  • list
  • get
  • create
  • update
  • delete
  • batch-create
  • batch-update

Examples:

python scripts/manage_records.py list --table Tasks --max-records 10
python scripts/manage_records.py get --table Tasks --record-id rec123
python scripts/manage_records.py create --table Tasks --fields '{"Name":"Ship it","Status":"Open"}'
python scripts/manage_records.py update --table Tasks --record-id rec123 --fields '{"Status":"Done"}'
python scripts/manage_records.py delete --table Tasks --record-id rec123
python scripts/manage_records.py batch-create --table Tasks --records '[{"Name":"A"},{"Name":"B"}]'

scripts/manage_tables.py

Use for schema and table operations.

Supported actions:

  • list-bases
  • base-schema
  • table-schema
  • create-table
  • update-table
  • delete-table

Examples:

python scripts/manage_tables.py list-bases
python scripts/manage_tables.py base-schema
python scripts/manage_tables.py table-schema --table Tasks
python scripts/manage_tables.py create-table --name Tasks --fields '[{"name":"Name","type":"singleLineText"}]'
python scripts/manage_tables.py update-table --table-id tbl123 --updates '{"name":"Tasks Archive"}'
python scripts/manage_tables.py delete-table --table-id tbl123

scripts/model_ops.py

Use when the task is explicitly model-driven.

Supported actions:

  • create-table-from-model
  • sync-model
  • validate-model

The model file must expose a class by name.

Examples:

python scripts/model_ops.py validate-model --module ./task_model.py --class-name Task
python scripts/model_ops.py sync-model --module ./task_model.py --class-name Task --create-missing-fields

Practical guidance

  • Prefer AirtableManager for table creation, schema inspection, and record CRUD across named tables.
  • Prefer AirtableClient when the user needs direct record listing or batch writes against a single configured table.
  • Prefer AirtableModel when the task should stay type-safe and reusable in app code.
  • If the user asks to "manage Airtable" without specifying code output, use the scripts first.
  • If a field payload is large or repetitive, write it to a JSON file and pass @file.json to the script arguments that accept JSON input.

JSON input rule for scripts

For --fields, --records, and --updates:

  • pass inline JSON like '{"Name":"Task"}', or
  • pass @path/to/file.json to load JSON from disk.

Exceptions

Catch and surface:

  • ConfigurationError for missing credentials/config
  • RecordNotFoundError for missing records
  • APIError for Airtable API failures
  • ValidationError for model/schema mismatches
  • AirtableError as a final fallback

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Coding

Web Research Assistant

AI-powered web research assistant that leverages BrowserAct API to supplement restricted web access by searching the internet for additional information. Designed for OpenClaw and Claude Code.

Registry SourceRecently Updated
2.5K3phheng
Coding

Pandoc Convert

Convert documents between 40+ formats using pandoc CLI. Handles Markdown ↔ Word ↔ PDF ↔ HTML ↔ LaTeX ↔ EPUB with smart defaults, professional templates, and comprehensive tooling.

Registry SourceRecently Updated
1.9K1Profile unavailable
Coding

Monitored Ralph Loop

Generate copy-paste bash scripts for Ralph Wiggum/AI agent loops (Codex, Claude Code, OpenCode, Goose). Use when asked for a "Ralph loop", "Ralph Wiggum loop", or an AI loop to plan/build code via PROMPT.md + AGENTS.md, SPECS, and IMPLEMENTATION_PLAN.md, including PLANNING vs BUILDING modes, backpressure, sandboxing, and completion conditions.

Registry SourceRecently Updated
1.3K0endogen
Coding

Forge

Autonomous quality engineering swarm that forges production-ready code through continuous behavioral verification, exhaustive E2E testing, and self-healing fix loops. Combines DDD+ADR+TDD methodology with BDD/Gherkin specifications, 7 quality gates, defect prediction, chaos testing, and cross-context dependency awareness. Architecture-agnostic — works with monoliths, microservices, modular monoliths, and any bounded-context topology.

Registry SourceRecently Updated