Results for “error”

Create PostHog error tracking suppression rules to drop high-volume, low-value errors at ingestion. Use when the user asks "stop capturing this error", "drop browser extension errors", "ignore ResizeObserver loops", "suppress bot-driven errors", or wants to reduce ingestion cost from noisy unactionable errors. Identifies suppression candidates, scopes the filter tightly, decides between full suppression and sampling, and confirms the rule before creating it. Suppressed errors are dropped permanently — this skill defaults to caution.

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The frontend Error Inspector capture system (captureError, lib/diagnostics/**). Use when wiring a new CapturedErrorSource or adapter, calling captureError from a new site, picking a red/orange/yellow tier or errorTierRules downgrade, or turning a console.error or swallowed catch into a captured error.

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Set up or review production error monitoring to capture unhandled errors, failed requests, and performance signals. Check event context, release labels, and alert routing so teams receive useful information when problems occur.

• Setting up error monitoring for a new production app
• Investigating errors that monitoring fails to capture
• Reviewing critical alerts and on-call routing

Implement Python error handling with early input checks, appropriate exception types, and clear messages. Handle individual failures in batch jobs, preserve error context for debugging, and use Pydantic models to validate structured input.

• Validating user input and API parameters
• Designing exception types and clear error messages
• Continuing batch jobs after individual failures

Write, review, and upgrade Effect v4 code for Composio CLI and its related packages. It guides service definitions, typed errors, command APIs, and version alignment, checking unfamiliar APIs against source files and installed type definitions.

• Implementing and reviewing Effect v4 code
• Defining services and typed error recovery
• Checking effect/cli APIs

Define service reliability targets, choose measurements that reflect user experience, and calculate error budgets—the failures a target allows. Generate alert thresholds and review existing definitions for unrealistic targets, unclear metrics, and missing response policies.

• Defining reliability targets for a new service
• Calculating allowed downtime and alert thresholds
• Reviewing existing reliability definitions

Add Sentry error reporting and performance monitoring to services, scheduled jobs, and database operations. Follow examples for capturing exceptions with useful context and measuring execution time through spans, which record individual operations for investigation.

• Adding error reporting to an API
• Recording failures and execution time for scheduled jobs
• Measuring database and asynchronous operations

Investigate unclear errors in Python PDF generation by displaying output immediately and inspecting error details. Use the resulting traceback, a record of where execution failed, to identify missing dependencies, font problems, or invalid file paths.

• Investigating unknown errors when saving PDFs
• Checking missing fonts and dependencies
• Diagnosing incomplete PDFs or silent crashes

Troubleshoot unclear errors when converting documents to PDF with pandoc. Check installation, capture error logs, identify missing fonts or packages, and try alternative PDF engines, the tools that render the final document.

• Investigating an unknown error during PDF conversion
• Checking missing fonts and LaTeX packages
• Retrying conversion with a different PDF engine

Interpret n8n validation results and fix configuration, connection, and expression issues. Distinguish blocking errors from warnings and known false positives, choose suitable validation settings, review automatic fixes, and test actual outputs after structural checks pass.

• Understand and fix workflow validation errors
• Distinguish known false positives from real warnings
• Preview and select automatic workflow fixes

Write and troubleshoot native Python in n8n Code nodes. Covers data filtering and transformation, migration from older Python code, valid return formats, and restricted execution environments, including cases where errors pass unprocessed input downstream.

• Filtering and aggregating data in Python Code nodes
• Migrating legacy Python (Beta) code
• Fixing input-variable and return-format errors

Investigate code errors through a structured process using reproduction steps, logs, and testable explanations. The workflow guides fault isolation, fixes, and follow-up checks, with common error patterns for Python, JavaScript/TypeScript, and Bash/Zsh.

• Investigating an error using logs and reproduction steps
• Finding why code broke after a recent change
• Troubleshooting common shell script errors

Manage Python and R packages for notebook work. Resolve missing-package errors, check installed versions, install into the selected runtime, and restart the kernel when needed, with guidance on using separate environments for different dependency requirements.

• Resolve missing-package errors in a notebook
• Check installed package versions
• Separate project dependencies into a named environment

Expert at building resilient applications through proper error handling. Covers Result types, error boundaries, try-catch patterns, typed errors, and graceful degradation. Use when "error handling, try catch, error boundary, Result type, exception, error-handling, typescript, react, resilience" mentioned.

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Guidelines for fixing unhandled errors from the VS Code 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.

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Check out a GitHub pull request, fix linting and TypeScript errors, and commit and push the changes. The workflow supports forked pull requests and repeats type checks to verify fixes while keeping changes focused on these errors.

• Fix linting errors in a pull request
• Resolve TypeScript errors in a pull request
• Apply fixes to a pull request from a fork

When a user corrects a factual error, trace it through stored knowledge, memory, identity settings, and fact records. Fix the underlying source, check related records where needed, and report the cause, changes, and safeguards against recurrence.

• Tracing the source of a repeated factual error
• Correcting mistaken facts about people or companies
• Updating outdated information and related records

Write and review Gradio demos for Hugging Face ZeroGPU. Configure GPU time limits and dependencies, and troubleshoot errors caused by separate worker processes, shared state, concurrent requests, or CUDA package installation.

• Review GPU handlers in a Gradio demo
• Tune GPU duration and quota usage
• Fix PicklingError and stale state across worker processes

Investigate Convex production errors, prepare fixes on a branch, and verify them before opening a pull request for human review. After an approved deployment, check whether the same error recurs. Never automatically merge changes or deploy to production.

• Investigating recurring production errors
• Preparing verified fixes for code or schema defects
• Checking data changes against a production snapshot

Diagnose unclear failures from run_shell commands through a stepwise workflow. Capture error output, verify directories and command availability, check environment variables, and test simpler commands to narrow down problems with paths, permissions, or the execution environment.

• Investigating unknown errors returned by run_shell
• Comparing local and agent execution environments
• Checking paths, permissions, and command availability

Continue library-dependent Python tasks when sandbox execution fails by switching to a shell script. Covers recognizing import errors, preserving script text, checking execution output, and troubleshooting package availability for tasks such as PDF generation or visualization.

• Retrying Python code after missing-library errors
• Continuing PDF generation through shell execution
• Running visualization code with installed packages

Run Python through run_shell when execute_code_sandbox fails unexpectedly. The skill explains how to pass multiline code using a heredoc, a shell syntax for inline text, then check standard output and error messages.

• Continue Python work after an unknown sandbox error
• Pass multiline Python code to run_shell
• Check Python availability with a simple print statement

Diagnose Python script failures by showing error output and exit codes alongside the working directory and file listing. Use these clues to investigate missing files, missing modules, permission errors, or scripts that produce no output.

• Investigating Python failures during automated execution
• Checking file paths after a FileNotFoundError
• Diagnosing Python failures in CI/CD pipelines

Diagnose PDF conversion failures in pandoc by checking installed conversion engines, capturing full error logs, and investigating missing fonts or packages. Follow a structured sequence of retries and fallback methods to find a working conversion route.

• Investigate an unknown error during PDF conversion
• Check which PDF engines are installed
• Find missing fonts or LaTeX packages