Search: "debugging"
Showing 24 of 24 results
mesh flow
What it does Mesh Flow replaces fragile, implicit prompt-chaining with a robust, artifact-driven DAG (Directed Acyclic Graph) orchestration system. By defining your agent workflows in a structured project.yaml, you move logic out of the prompt and into a compile-time validated system. It enforces hard gates—such as human approval or dependency verification—that the AI cannot bypass or hallucinate past. Why use this skill Standard agentic workflows often fail because the LLM decides to skip steps or "forgets" requirements. Mesh Flow treats your agentic workflow like a CI/CD pipeline. It features a Compile-then-Run architecture that validates your topology for cycles and missing artifacts before single token is generated. This ensures 100% predictable execution paths, explicit failure states (failed, blocked, rejected), and absolute control over recovery paths. Supported tools YAML-based workflow configurations Standardized Adapter Interfaces for cross-skill communication Mermaid DAG visualization for debugging Zod-backed schema validation CLI tools for compilation and execution (mesh compile, mesh run) Output structure The skill produces a normalized execution plan and a detailed execution trace. Every node execution returns a standardized status, a list of produced artifacts, and comprehensive metadata including tool calls and verification reasoning. Use Cases Build multi-step agent pipelines with hard verification gates Enforce human-in-the-loop approval before sensitive code deployments Visualize complex agent task dependencies using Mermaid DAGs Standardize artifact sharing between disparate AI skills and agents
prompt-engineer-lite
Building high-performance LLM applications requires more than just basic instructions. This skill equips your AI agent with a sophisticated framework for designing, debugging, and optimizing prompts across any major model provider. It solves the common problems of model drift, parsing failures, and hallucination by implementing industry-standard engineering patterns.
Debugging Love. 调试。Depuración.
Debugging your dating life — debug bad matches, debug miscommunication, and debug your profile for better connections. Debugging compatibility, debugging con...
Systematic Debugging
Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions.
Superpowers Systematic Debugging
Enforce a four-phase debugging process—root cause investigation, pattern analysis, hypothesis testing, and evidence-based fix verification—for all bugs and f...
Systematic Debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
PUA Debugging
Forces exhaustive problem-solving using corporate PUA rhetoric and structured debugging methodology. MUST trigger when: (1) any task has failed 2+ times or y...
Runtime Debugging Skill
Diagnose and fix bugs using runtime execution traces. Use when debugging errors, analyzing failures, or finding root causes in Python, Node.js, or Java appli...
Debugging R Environment And Dependencies
Diagnose and fix R environment issues, including package installation failures, dependency conflicts, system library problems, renv errors, and Bioconductor...
Debugging Reinforcement Learning
Tools and methods for controlling randomness, ensuring reproducibility, analyzing agent behavior, and debugging reward issues in stochastic reinforcement lea...
OpenClaw Debugging Assistant
Debugging assistant. Analyzes error logs, suggests breakpoints, traces execution flow, and helps identify root causes of issues.
A11y Debugging
Uses Chrome DevTools MCP for accessibility (a11y) debugging and auditing based on web.dev guidelines. Use when testing semantic HTML, ARIA labels, focus stat...
pua-debugging-pro
Professional anti-giveup debugging protocol for coding tasks where the agent starts looping, deflecting to users, or trying to end early without evidence. Us...
debugging-and-error-recovery
# Debugging and Error Recovery ## Overview Systematic debugging with structured triage. When something breaks, stop adding features, preserve evidence, and follow a structured process to find and fix the root cause. Guessing wastes time. The triage checklist works for test failures, build errors, runtime bugs, and production incidents. ## When to Use - Tests fail after a code change - The build breaks - Runtime behavior doesn't match expectations - A bug report arrives - An error appears in logs or console
n8n-debugging-official
# n8n Debugging When something breaks, the cause is almost always: 1. **Parameter misconfiguration** (wrong value, wrong type, missing field). 2. **Stale assumptions** (different version, different behavior than you remember). 3. **Paths misconfigured or misconnected** (wrong output index, wrong merge input, missing wire, IF/Switch wired to the wrong branch). 4. **Upstream data stripped** (an intermediate node replaced `$json` with its own output, so downstream `$json.x` resolves to null even though "it sho
code-debugging
# Code Debugging Systematically debug experiment code with structured error categorization and fix strategies. ## Input - `$0` — Error message, stderr output, or code file with issues - `$1` — Optional: the code that produced the error ## References - Debug patterns and state machine: `~/.claude/skills/code-debugging/references/debug-patterns.md` ## Workflow ### Step 1: Categorize the Error | Category | Examples | Severity | |----------|----------|----------| | SyntaxError | Invalid syntax, indentation | Lo
clr-activation-debugging
# CLR Activation Debugging Diagnose .NET Framework runtime activation issues by analyzing CLR activation logs (CLRLoad logs) produced by the shim (mscoree.dll). These logs record every decision the shim makes when selecting and loading a CLR version. ## When to Use - A process fails to load the CLR at all ("Unable to find a version of the runtime to use") - The shim picks the wrong CLR version (e.g., v2.0 instead of v4.0) - Unexpected .NET 3.5 Feature-on-Demand (FOD) install dialogs appear - FOD dialogs are
anti-debugging-techniques
# SKILL: Anti-Debugging Techniques — Detection & Bypass Playbook > **AI LOAD INSTRUCTION**: Expert anti-debug techniques across Linux and Windows. Covers ptrace, PEB flags, NtQueryInformationProcess, timing attacks, signal-based detection, TLS callbacks, VEH tricks, and all corresponding bypass methods. Base models often miss the distinction between user-mode and kernel-mode detection and the correct patching strategy for each. ## 0. RELATED ROUTING - [code-obfuscation-deobfuscation](../code-obfuscation-deo
debugging-instruments
# Debugging and Instruments Keep interactive graph and Instruments triage here. Route detailed `.memgraph` command-line ownership/growth analysis and ETTrace work to their focused skills. ## Contents - [LLDB Debugging](#lldb-debugging) - [Memory Debugging](#memory-debugging) - [Hang Diagnostics](#hang-diagnostics) - [Build Failure Triage](#build-failure-triage) - [Instruments Overview](#instruments-overview) - [Common Mistakes](#common-mistakes) - [Review Checklist](#review-checklist) - [References](#refere
debugging-lambda-timeouts
# Debugging Lambda Timeouts ## Overview Domain expertise for systematically investigating AWS Lambda function timeout failures by analyzing function configuration, CloudWatch logs, metrics, dependencies, cold start patterns, and code. Identifies common causes such as insufficient timeout settings, external service delays, database connection issues, memory constraints, and inefficient code patterns, then provides prioritized recommendations. ## Debug a Lambda timeout To investigate and resolve Lambda timeou
parallel-debugging
# Parallel Debugging Framework for debugging complex issues using the Analysis of Competing Hypotheses (ACH) methodology with parallel agent investigation. ## When to Use This Skill - Bug has multiple plausible root causes - Initial debugging attempts haven't identified the issue - Issue spans multiple modules or components - Need systematic root cause analysis with evidence - Want to avoid confirmation bias in debugging ## Hypothesis Generation Framework Generate hypotheses across 6 failure mode categories
debugging-strategies
# Debugging Strategies Transform debugging from frustrating guesswork into systematic problem-solving with proven strategies, powerful tools, and methodical approaches. ## When to Use This Skill - Tracking down elusive bugs - Investigating performance issues - Understanding unfamiliar codebases - Debugging production issues - Analyzing crash dumps and stack traces - Profiling application performance - Investigating memory leaks - Debugging distributed systems ## Core Principles ### 1. The Scientific Method
ia-debugging
Systematic root-cause debugging with verification. Use for errors, stack traces, broken tests, flaky tests, regressions, or anything not working as expected. For validating bug reports before fixing, use bug-reproduction-validator agent.
GitHub PAT Debugging
Diagnose GitHub Personal Access Token failures — 401 Bad credentials, push failures, tokens that appear valid but fail — before declaring them expired or revoked. Checks the command, variable propagation, and request parameters first, then cross-validates with curl, Node.js, Python, or PowerShell, and only then investigates permission, revocation, or network causes. Covers the GitHub REST API, Contents API, and file-push workflows. 中文摘要:GitHub PAT 认证排障。先查命令、环境变量传递与请求参数,再用 curl/Node/Python 交叉验证,最后才判断权限、撤销或网络原因。触发词:GitHub token 失效排查、401 Bad credentials、PAT 认证失败、推送失败诊断.