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.
npx agentmods add skills/noizefield/audio-plugin-coder/debugnpx skills add Noizefield/audio-plugin-coder --skill debuggit clone --depth 1 https://github.com/Noizefield/audio-plugin-coderWhat 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.
| Model | Per session | Once 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 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- skill_debug — 100% identical, 0 lines differ
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:
- Inspect a codebase without human intervention
- Identify likely failure points
- Insert breakpoints programmatically
- Generate a valid VS Code:
launch.jsondebugging configuration - Enter VS Code: debug mode
- Capture runtime errors, logs, and stack traces
- Filter noise while preserving full raw error telemetry
- 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
- Enumerate the workspace root
- Identify:
- Primary language(s)
- Entry points (e.g.
main.py,index.js,app.ts,Program.cs) - Existing test suites
- Existing
.vscodeconfiguration
- 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:
- Parse the AST (or equivalent)
- 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
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.
- 2d ago First seen · 247 lines · 0 tokens per session scan A 50dfc52db898
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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