data-formulator: Skill for Cursor

.cursor/skills/error-handling/SKILL.md

error-handling is a skill for Cursor from microsoft/data-formulator. It costs 34 tokens per session (3,637 once invoked), scanned A, original, MIT.

A shared way to report and handle errors between a frontend and backend application.

In plain words
What is it for?
Use it when adding API endpoints, changing error behavior, adding frontend API calls, or writing tests for error cases.
Why use it?
It gives API failures a consistent treatment and helps avoid exposing sensitive details in errors or logs.

Skill for Cursor ✓ vendor

Written for Cursor: installed under .cursor/.

This is microsoft/data-formulator's own configuration. It tells Cursor how to work on data-formulator itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything data-formulator configures →

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is import { apiRequest } from '../app/apiClient';.

About the project

Data Formulator is an interactive workspace where AI agents connect to data sources, answer questions, and create visualizations. It is used to explore and analyze data while branching into related questions without losing the context of each investigative path.

microsoft/data-formulator · 17,584 stars · on GitHub · data-formulator.ai

Reuse

Borrowing it

Nothing to install: this file belongs to microsoft/data-formulator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/microsoft/data-formulator/main/.cursor/skills/error-handling/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/microsoft/data-formulator

Made for: Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for error-handling

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/data-formulator/error-handling/github.svg)](https://agentmods.dev/skills/microsoft/data-formulator/error-handling)
Your own site
<a href="https://agentmods.dev/skills/microsoft/data-formulator/error-handling"><img src="https://agentmods.dev/badge/skills/microsoft/data-formulator/error-handling/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for error-handling

Your own site · 80×15
<a href="https://agentmods.dev/skills/microsoft/data-formulator/error-handling"><img src="https://agentmods.dev/badge/skills/microsoft/data-formulator/error-handling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,637 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Snyk fail 7 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.03637
Opus 5.5 $0.00014 $0.01455
Sonnet 5.5 $0.00007 $0.00727
Haiku 4.5 $0.00003 $0.00364

Measured 1mo ago against content hash 760917e7d8c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

error-handling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/skills/error-handling/SKILL.md · 382 lines

How it starts

The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Error Handling Skill

Unified error handling system for DF. Use when adding API endpoints, modifying error handling, or adding frontend API calls.

Prerequisites: Read docs/dev-guides/7-unified-error-handling.md before changing API error behavior. Read docs/dev-guides/2-log-sanitization.md when the work involves logging, credentials, external services, or DataLoaders. If your work introduces new error handling patterns or conventions, update this file and related dev-guides accordingly.

Architecture Overview

Frontend                              Backend
────────                              ───────
apiClient.ts                          errors.py
├── apiRequest()    ←── JSON ────     ├── ErrorCode (enum)
├── streamRequest() ←── NDJSON ──     └── AppError (exception)
└── parseStreamLine()
                                      error_handler.py
errorCodes.ts                         ├── register_error_handlers(app)
└── getErrorMessage()                 ├── classify_and_wrap_llm_error()
                                      └── stream_error_event()
errorHandler.ts
└── handleApiError()                  security/sanitize.py
                                      └── classify_llm_error() (internal)
MessageSnackbar ← dfSlice.messages

Protocol Snapshot

Use this contract for all new or reworked DF APIs:

Scenario HTTP Shape
Non-streaming success 200 {"status": "success", "data": ...}
Non-streaming business/validation error 200 {"status": "error", "error": {"code", "message", "retry", "request_id"}}
Non-streaming auth/authorization error 401 / 403 same structured error body
Streaming preflight error 200 application/json + {"status": "error", "error": ...}
Streaming in-flight fatal error 200 NDJSON line: {"type": "error", "error": ...}
No Flask route / too large / unhandled crash 404 / 413 / 500 transport-level error

Do not use HTTP 400/422 for application validation errors in new code. Do not convert in-flight NDJSON errors to status: "error"; once the stream has started, event type is the protocol discriminator.

Read the full file on GitHub · 382 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 382 lines · 34 tokens per session scan A 760917e7d8c8

Subscribe to this mod's changes

error-handling is a skill published in the GitHub repository microsoft/data-formulator (17,584 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 3,637 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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