skill_debug

A self-directed debugging workflow for an AI coding assistant working with Visual Studio Code, a code editor. It describes how to inspect a project, run its debugger, and collect runtime evidence.

In plain words
What is it for?
It helps inspect a codebase, identify likely failure points, add breakpoints, create a VS Code debugging configuration, run debug mode, and collect diagnostic output.
Why use it?
It reduces guesswork when finding bugs by checking what the program actually does and preserving errors, logs, and stack traces for analysis.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/noizefield/audio-plugin-coder/debug
Any agent
npx skills add Noizefield/audio-plugin-coder --skill debug
Clone the repo
git clone --depth 1 https://github.com/Noizefield/audio-plugin-coder

Made for: Claude Code, Codex.

Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,201 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00017 $0.01201
Opus 5 $0.00009 $0.00600
Sonnet 5 $0.00003 $0.00240
Haiku 4.5 $0.00002 $0.00120

Measured 2d ago against content hash 50dfc52db898, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

skill_debug 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 2d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.agent/skills/debug/SKILL.md · 247 lines

How it starts

The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Purpose

This document defines a self-directed debugging workflow for a Large Language Model (LLM) operating inside or alongside Visual Studio Code: (VS Code:). The goal is for the LLM to:

  1. Inspect a codebase without human intervention
  2. Identify likely failure points
  3. Insert breakpoints programmatically
  4. Generate a valid VS Code: launch.json debugging configuration
  5. Enter VS Code: debug mode
  6. Capture runtime errors, logs, and stack traces
  7. Filter noise while preserving full raw error telemetry
  8. Transmit all collected diagnostic data back to the LLM for analysis

This workflow assumes the LLM has:

  • Read access to the workspace
  • Write access to configuration files
  • The ability to invoke VS Code: commands (directly or via an agent/tooling layer)

High-Level Debugging Strategy

The LLM must operate as a deterministic debugger, not a conversational assistant.

Core principles:

  • Prefer evidence over speculation
  • Favor runtime inspection over static guesses
  • Never suppress errors at source
  • Always preserve original error output

Step 1: Workspace Reconnaissance

  1. Enumerate the workspace root
  2. Identify:
    • Primary language(s)
    • Entry points (e.g. main.py, index.js, app.ts, Program.cs)
    • Existing test suites
    • Existing .vscode configuration
  3. Detect build systems and runtimes:
    • Node.js, Python, Java, .NET, Go, etc.

Output a workspace map internally before proceeding.


Step 2: Static Code Analysis

For each execution path:

  1. Parse the AST (or equivalent)
  2. Identify:
    • Unhandled exceptions
    • Unsafe casts
    • Null/undefined dereferences
    • Infinite loops
    • Race conditions (async / threading)
    • External I/O boundaries (filesystem, network, DB)

Mark all high-risk lines.


Step 3: Breakpoint Placement Heuristics

Automatically insert breakpoints at:

  • Program entry point
  • All caught and uncaught exception blocks
  • Function boundaries with:
    • Complex conditionals
    • State mutation
    • External side effects
  • Before and after async boundaries
  • Any line referenced in stack traces from prior runs

Read the full file on GitHub · 247 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. 2d ago First seen · 247 lines · 0 tokens per session scan A 50dfc52db898

Subscribe to this mod's changes

skill_debug is a skill published in the GitHub repository Noizefield/audio-plugin-coder (313 stars, last pushed 7d ago), licensed MIT. It adds 17 tokens to every session and 1,201 once invoked, about $0.0001 per session on Opus 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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