fix-errors

Trace ShadowCode telemetry stack traces and fix producer-side unhandled errors.

Updated Mar 19, 2026
One-click install
npx skills add https://github.com/Nithwin/shadowcode --skill fix-errors-nithwin
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: fix-errors
Source: https://github.com/Nithwin/shadowcode/tree/main/.github/skills/fix-errors
Command: npx skills add https://github.com/Nithwin/shadowcode --skill fix-errors-nithwin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guidelines for fixing unhandled errors from the ShadowCode error telemetry dashboard. Use when investigating error-telemetry issues with stack traces, error messages, and hit/user counts. Covers tracing data flow through call stacks, identifying producers of invalid data vs. consumers that crash, enriching error messages for telemetry diagnosis, and avoiding common anti-patterns like silently swallowing errors.

Core Features & Use Cases

  • Trace data flow: Read each frame in the stack trace from bottom to top to identify producers of invalid data.
  • Enrich telemetry: Add diagnostic context to error messages to reveal the sender in telemetry.
  • Producer fixes: Apply fixes at the data producer stage to sanitize data before sending to IPC or logs.
  • Guidelines for safe changes: Do not swallow errors; ensure tests run.

Quick Start

Identify the unhandled telemetry error, trace the data flow from producer to consumer, and implement a producer-side fix that enriches error messages.

Frequently Asked Questions about fix-errors

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I trace unhandled telemetry errors to find the producer of invalid data?

Trace unhandled telemetry errors by reading stack trace frames from bottom to top to identify producers of invalid data versus consumers that crash. Analyze error messages and user counts from the telemetry dashboard to locate the source. Apply producer-side validation to sanitize data before sending to IPC or logs.

What is the best way to enrich error messages for telemetry diagnosis?

Enrich error messages for telemetry diagnosis by adding diagnostic context that reveals the sender in the telemetry data. This exposes richer information for diagnosis, ensuring changes preserve behavior while avoiding common anti-patterns like silently swallowing errors in production code.

How do I fix unhandled errors from a telemetry dashboard without breaking existing behavior?

Fix unhandled errors from a telemetry dashboard by applying producer-side fixes that sanitize data before sending to IPC or logs. Ensure changes preserve behavior while exposing richer telemetry for diagnosis. Do not swallow errors and ensure tests run to validate safety improvements.

Why should I fix telemetry errors at the data producer stage instead of the consumer?

Fixing telemetry errors at the data producer stage sanitizes invalid data before it reaches IPC or logs, preventing consumer crashes. Tracing data flow through call stacks identifies the producer, enabling validation and enrichment at the source rather than patching downstream consumers.

When do I need to trace data flow through call stacks to diagnose unhandled errors?

Trace data flow through call stacks when investigating unhandled telemetry errors with stack traces, error messages, and hit or user counts. Read each frame from bottom to top to identify the producer of invalid data and apply fixes at the data producer stage for telemetry-safety improvements.