Querying Well Data logo

Querying Well Data

Organization
WellApp-ai
querying-well-data

How to query a Well workspace correctly — discover the schema first, then query the right root. Use whenever the user asks about their invoices, companies, contacts, bank transactions, accounts, or accounting ledger in Well, or before writing any well_query_records call.

Overview

PublisherWellApp-ai
RepositoryWell
Skill namequerying-well-data
Stars
342
Forks
48
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by WellApp-ai on GitHub. Read the source before you install it.

Installation

Install the Querying Well Data AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WellApp-ai/Well.git /tmp/Well
mkdir -p .claude/skills
cp -r /tmp/Well/plugins/well/skills/querying-well-data .claude/skills/querying-well-data
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Querying Well Data in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Querying Well Data on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Querying Well Data is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Querying Well data

Well exposes a workspace's financial graph through three MCP tools:

  • well_get_schema() — list every available root (entity type).
  • well_get_schema({ root }) — list the fields available on one root, with types and semantic context.
  • well_query_records({ root, fields, whereClause?, orderBy?, limit? }) — read rows.
  • well_get_entity({ root, id }) — fetch one record by id.

The one rule: discover before you query

Always call well_get_schema(root) first, pick the fields you need from what it returns, then call well_query_records. Field paths are arrays: "invoices.issuer.name"["invoices", "issuer", "name"]. Do not guess field names — they vary by root and are documented in the schema response (each field carries a type and often a context explaining what it means).

The roots you can read

Calling well_get_schema() with no argument returns the full set. It includes far more than invoices and companies — in particular the accounting graph:

  • Commercial documents: invoices, invoice_items, invoice_transactions
  • Parties: companies, people, payment_means
  • Banking: accounts, transactions, account_balances
  • Accounting graph (read-only, posted by Well's pipelines): ledger_accounts, journals, journal_entries
  • Reference: tax_rates, exchange_rates, categories, connectors
  • Workspace: memberships, tasks, workspace_connectors

If you are about to answer a financial question by reconstructing it from raw invoices, stop and check well_get_schema() first — the posted ledger (journal_entries, ledger_accounts) is almost always the correct, more accurate source. See the well:compte-de-resultat and well:balance-sheet skills.

Filtering

whereClause is a Hasura-style boolean expression. Use the operator the field's type allows (from the schema):

  • numeric / date_eq, _gt, _lt, _gte, _lte
  • enum_eq, _neq, _in, _nin, _is_null
  • text_eq, _like, _ilike
  • relations → nest: { "issuer": { "name": { "_ilike": "%acme%" } } }

Gotchas

  • Reads are scoped to the authenticated workspace automatically — you never pass a workspace id.
  • Select the specific fields you need (5–15), not everything — it is faster and cheaper.
  • Amounts on commercial documents are in the document currency; check the schema context for currency fields before summing across currencies (see exchange_rates).

Frequently asked questions

What does the Querying Well Data AI skill do?

How to query a Well workspace correctly — discover the schema first, then query the right root. Use whenever the user asks about their invoices, companies, contacts, bank transactions, accounts, or accounting ledger in Well, or before writing any well_query_records call.

Why use Querying Well Data on TypingMind?

Because you install it once and use it with any model. Querying Well Data is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Querying Well Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WellApp-ai/Well/tree/main/plugins/well/skills/querying-well-data. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Querying Well Data?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Querying Well Data?

As many as you like. As long as a model supports skills, you can use Querying Well Data with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Querying Well Data AI skill free?

Yes. It is published on GitHub by WellApp-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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