Airtable Master

This is NOT a user-facing skill. It's a shared resource library.

Purpose

Provides shared resources to eliminate duplication across:

• airtable-connect

  - Connect to bases, discover schema

• airtable-query

  - Query records with filters

• airtable-sync

  - Import/export records

Instead of loading this skill, users invoke the specific skill above.

Architecture: DRY Principle

Problem solved: Multiple Airtable skills would have duplicated content (setup instructions, API docs, error handling).

Solution: Extract shared content into

references/

scripts/

Result: Single source of truth, reduced context per skill.

Shared Resources

references/

setup-guide.md - Complete setup wizard

• Creating Personal Access Token (PAT)
• Configuring .env file
• Selecting scopes
• Validation steps

api-reference.md - API patterns

• Base URL and headers
• Key endpoints (bases, tables, records)
• Pagination
• Batch operations
• Rate limits

error-handling.md - Troubleshooting

• HTTP error codes (401, 403, 404, 422, 429)
• Recovery patterns
• Debugging tips

field-types.md - Field type reference

• All 20+ Airtable field types
• Read and write formats
• Type-specific validation

scripts/

Configuration & Setup

check_airtable_config.py - Pre-flight validation

python check_airtable_config.py [--verbose] [--json]

--verbose

-v

--json

Exit codes: 0=configured, 1=partial (no bases), 2=not configured

When to Use: Run this FIRST before any Airtable operation. Use to validate PAT is configured, test API connection, check base access, or diagnose authentication issues.

setup_airtable.py - Interactive setup wizard

python setup_airtable.py [--non-interactive] [--api-key KEY]

--non-interactive

--api-key

Default: Runs interactively. Guides through PAT creation, tests connection, saves to

.env

When to Use: Use when Airtable integration needs initial setup, when check_airtable_config.py returns exit code 2, or when user needs to reconfigure credentials or switch workspaces.

discover_bases.py - Base discovery (GET /meta/bases)

python discover_bases.py [--refresh] [--json] [--with-schema]

--refresh

--json

--with-schema

Saves to:

01-memory/integrations/airtable-bases.yaml

When to Use: Use when user asks "what bases do I have", "list my airtable bases", "show tables", after adding new bases, or when base/table name resolution fails (--refresh to update cache).

Record Operations

query_records.py - Query records from table (GET /{baseId}/{tableIdOrName})

python query_records.py --base BASE --table TABLE [--filter FORMULA] [--fields FIELDS] [--view VIEW] [--sort FIELD] [--limit N] [--json] [--verbose]

--base

--table

--filter

--fields

--view

--sort

-

--limit

--json

--verbose

-v

Filter Formula Examples:

--filter "{Status}='Active'"
--filter "AND({Status}='Active', {Priority}='High')"
--filter "SEARCH('john', LOWER({Name}))"
--filter "{Due Date} < TODAY()"

When to Use: Use when user wants to read records from a table, search with filters, list data from Airtable, or retrieve specific records. Supports Airtable formula syntax for complex filters.

manage_records.py - CRUD operations (POST/PATCH/PUT/DELETE /{baseId}/{tableIdOrName})

# Create record(s)
python manage_records.py create --base BASE --table TABLE --data JSON [--file FILE] [--typecast] [--json] [--verbose]

# Update record(s)
python manage_records.py update --base BASE --table TABLE --record RECID --data JSON [--file FILE] [--typecast] [--replace] [--json] [--verbose]

# Delete record(s)
python manage_records.py delete --base BASE --table TABLE --record RECID [--file FILE] [--json] [--verbose]

action

create

update

delete

--base

--table

--record

--data

--file

--typecast

--replace

--json

--verbose

-v

*For create:

--data

--file

--record

--data

--file

--record

--file

Usage Examples:

# Create a single record
python manage_records.py create --base "My CRM" --table "Contacts" \
    --data '{"Name": "John Doe", "Email": "john@example.com"}'

# Create multiple records from file
python manage_records.py create --base appXXX --table tblYYY --file records.json

# Update a record
python manage_records.py update --base "Tasks" --table "Tasks" \
    --record recXXX --data '{"Status": "Done"}'

# Delete a record
python manage_records.py delete --base "Tasks" --table "Tasks" --record recXXX

# Batch operations with typecast
python manage_records.py create --base "CRM" --table "Leads" \
    --file leads.json --typecast

Batch Limits: Max 10 records per API call (script handles batching automatically)

When to Use: Use

create

update

delete

Intelligent Error Detection Flow

When an Airtable skill fails due to missing configuration, the AI should:

Step 1: Run Config Check with JSON Output

python 00-system/skills/airtable/airtable-master/scripts/check_airtable_config.py --json

Step 2: Parse the

ai_action

Field

The JSON output includes an

ai_action

proceed_with_operation

proceed_with_warning

prompt_for_api_key

run_setup_wizard

python 00-system/skills/airtable/airtable-master/scripts/setup_airtable.py

Step 3: Help User Fix Issues

If

ai_action

prompt_for_api_key

1. Tell user: "Airtable integration needs setup. I need your Personal Access Token (PAT)."
2. Show them: "Get one at: https://airtable.com/create/tokens"
3. Guide them through scopes: "Add scopes: data.records:read, data.records:write, schema.bases:read"
4. Ask: "Paste your Airtable PAT here (starts with 'pat.'):"
5. Once they provide it, write directly to

   .env

   :

   # Edit .env file to add:
   AIRTABLE_API_KEY=pat.their_token_here
   

6. Re-run config check to verify

JSON Output Structure

{
  "status": "not_configured",
  "exit_code": 2,
  "ai_action": "prompt_for_api_key",
  "missing": [
    {"item": "AIRTABLE_API_KEY", "required": true, "location": ".env"}
  ],
  "fix_instructions": [...],
  "env_template": "AIRTABLE_API_KEY=pat.YOUR_TOKEN_HERE",
  "setup_wizard": "python 00-system/skills/airtable/airtable-master/scripts/setup_airtable.py"
}

How Skills Reference This

Each skill loads shared resources only when needed (progressive disclosure):

airtable-connect uses:

• check_airtable_config.py

  (validate before connection)

• discover_bases.py

  (find and cache bases)

• api-reference.md

  (schema endpoints)

• error-handling.md

  (troubleshooting)

airtable-query uses:

• check_airtable_config.py

  (validate before query)

• query_records.py

  (list/filter records)

• api-reference.md

  (query patterns)

• field-types.md

  (interpret results)

airtable-sync uses:

• check_airtable_config.py

  (validate before sync)

• manage_records.py

  (CRUD operations)

• api-reference.md

  (batch patterns)

• error-handling.md

  (recovery)

Usage Example

User says: "query my Projects base in Airtable"

What happens:

1. AI loads

   airtable-connect

   (NOT airtable-master)

2. airtable-connect

   SKILL.md says: "Run check_airtable_config.py first"
3. AI executes:

   python 00-system/skills/airtable/airtable-master/scripts/check_airtable_config.py --json

4. If exit code 2, AI prompts user for API key and helps them set up
5. If exit code 0, AI executes:

   python 00-system/skills/airtable/airtable-master/scripts/query_records.py --base "Projects"

6. If errors occur, AI loads:

   airtable-master/references/error-handling.md

airtable-master is NEVER loaded directly - it's just a resource library.

Key Differences from Notion

Version: 1.3 Created: 2025-12-11 Updated: 2025-12-11 Status: Production Ready

Changelog:

• v1.3: Added "When to Use" sections to all 5 scripts for AI routing guidance
• v1.2: Added comprehensive script argument documentation with usage examples and argument tables
• v1.1: Added Intelligent Error Detection Flow with

  --json

  support, added setup_airtable.py
• v1.0: Initial release