Results for “flow-debugging”

Provides a structured debugging workflow for analyzing errors, test failures, and unexpected behavior, including root cause analysis and fix recommendations.

Generates detailed, technology-agnostic implementation blueprints by analyzing project architecture, technology stacks, and data flow patterns.

Guides architecture decisions, flow type selection, bulk safety validation, and fault handling standards for Salesforce Flow design and review.

Investigate a problem, log it to the debug changelog, cross-check past issues, and suggest a non-duplicate fix.

Use when investigating a bug, runtime error, unexpected behavior, test failure, or crash. For systematic root cause analysis, error message interpretation, stack trace reading, reproduction steps, and targeted fixes. Activate when the user says "it's broken", "this doesn't work", "I'm getting an error", or "help me debug".

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns using the Python netflow library.

Decomposes an approved specification into dependency-aware waves and atomic executable tasks with exact file scope, test-first actions, verification commands, and an execution gate.

Diagnose the current OMC session or repo state using logs, traces, state, and focused reproduction

Delivers a small, bounded code change through inspection, a mini-plan, implementation, testing, review, and adversarial verification. Use for bug fixes, configuration changes, and features expected to touch no more than five files.

Debug application issues with techniques for Python, JavaScript, performance profiling, production incidents, and network problems.

Systematic debugging — reproduce, read the error, isolate, hypothesize, fix, verify, document. Use when tracking down a bug, unexpected behavior, or test failure — including phrases like "this doesn't work", "why is this failing", or "help me debug".

debug-logging

Provides a structured debugging workflow for diagnosing errors, test failures, and unexpected behavior, including root cause analysis and verification steps.

Enforces a disciplined, evidence-based debugging protocol. Use when the user reports a bug, an unexpected behavior, a broken test, or asks "why is X not working?". Prevents premature fixes and treats symptoms. Blocks code changes until a root cause is confirmed.

Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

Systematic debugging with root cause investigation. (gstack)

Debugging is the scientific method under time pressure.

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

Follow a request end-to-end through the stack from route to database

Essential Zephyr RTOS kernel services. Covers thread lifecycle and priority, the logging subsystem for diagnostics, and the interactive shell for real-time hardware inspection and debugging. Trigger when writing application flows, adding logging to modules, or creating interactive CLI commands.

Supabase-first reference and implementation skill for Nick's app stack. Use when writing Supabase queries, auth flows, RLS policies, storage rules, realtime features, edge-function integrations, migrations, or debugging Supabase behavior. Prefer this instead of guessing Supabase APIs or patterns. Bias toward product-led implementation choices, trust-aware access control, MVP discipline, and using only the Supabase features the product actually needs.

Use when work depends on the user's live browser session or visible rendered state rather than static fetches, especially for browser debugging contexts or DevTools-selected elements or requests, logged-in dashboards or CMS flows, localhost apps, forms, uploads, downloads, media inspection, DOM or iframe inspection, Shadow DOM, or browser failures that look like soft 404s, auth walls, anti-bot checks, or rate limits.