ownyourcode: Skill for Claude Code

.claude/skills/fundamentals/debugging/SKILL.md

protocol-d-debugging is a skill for Claude Code from DanielPodolsky/ownyourcode. It costs 75 tokens per session (2,319 once invoked), scanned A, original, MIT.

A step-by-step method for debugging software called Protocol D: read the error, isolate the cause, check documentation, form a hypothesis, and verify it.

In plain words
What is it for?
Use it when investigating crashes, bugs, failing tests, error messages, or code that is not working as expected.
Why use it?
It replaces random guessing with a repeatable way to narrow down what went wrong. It is intended for errors, broken behavior, failed code, and situations where you are stuck.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

This is DanielPodolsky/ownyourcode's own configuration. It tells Claude Code how to work on ownyourcode itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ownyourcode configures →

Reuse

Borrowing it

Nothing to install: this file belongs to DanielPodolsky/ownyourcode. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/DanielPodolsky/ownyourcode/main/.claude/skills/fundamentals/debugging/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/DanielPodolsky/ownyourcode

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for protocol-d-debugging

README.md
[![agentmods](https://agentmods.dev/badge/skills/danielpodolsky/ownyourcode/debugging/github.svg)](https://agentmods.dev/skills/danielpodolsky/ownyourcode/debugging)
Your own site
<a href="https://agentmods.dev/skills/danielpodolsky/ownyourcode/debugging"><img src="https://agentmods.dev/badge/skills/danielpodolsky/ownyourcode/debugging/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for protocol-d-debugging

Your own site · 80×15
<a href="https://agentmods.dev/skills/danielpodolsky/ownyourcode/debugging"><img src="https://agentmods.dev/badge/skills/danielpodolsky/ownyourcode/debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,319 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00075 $0.02319
Opus 5.5 $0.00030 $0.00928
Sonnet 5.5 $0.00015 $0.00464
Haiku 4.5 $0.00007 $0.00232

Measured 1mo ago against content hash 3b1302395add, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

protocol-d-debugging 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 1mo 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.

.claude/skills/fundamentals/debugging/SKILL.md · 315 lines

How it starts

The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Protocol D: Systematic Debugging

"Debugging is not guessing. It's a systematic elimination of possibilities."

When to Apply

Activate this skill when:

  • Junior says "it's not working" or "I'm stuck"
  • Junior encounters an error they don't understand
  • Junior has been spinning on the same problem
  • Junior is frustrated and can't find the bug
  • Junior asks "why isn't this working?"

The Protocol D Framework

┌─────────────────────────────────────────────────────────────────┐
│                      PROTOCOL D                                  │
│              Systematic Debugging Flow                           │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  STEP 1: READ                                                    │
│  ────────────────────────────────────────────────                │
│  "Read the error message OUT LOUD. What is it actually saying?"  │
│                                                                  │
│  - Don't skim. Read every word.                                  │
│  - What file? What line? What type of error?                     │
│  - Is there a stack trace? Follow it.                            │
│                                                                  │
│                         ↓                                        │
│                                                                  │
│  STEP 2: ISOLATE                                                 │
│  ────────────────────────────────────────────────                │
│  "Where EXACTLY is the failure? Can you point to the line?"      │
│                                                                  │
│  - Frontend or Backend?                                          │
│  - Which function? Which line?                                   │
│  - Add console.log/print statements to narrow down               │
│  - Binary search: comment out half, does it still fail?          │
│                                                                  │
│                         ↓                                        │
│                                                                  │
│  STEP 3: DOCS                                                    │
│  ────────────────────────────────────────────────                │
│  "What does the official documentation say about this?"          │
│                                                                  │
│  - Google the EXACT error message                                │
│  - Check official docs for the function/API                      │
│  - Read the types/signatures carefully                           │
│  - Are you using it correctly?                                   │
│                                                                  │
│                         ↓                                        │
│                                                                  │
│  STEP 4: HYPOTHESIZE                                             │
│  ────────────────────────────────────────────────                │
│  "What do YOU think the problem is? Form a hypothesis."          │
│                                                                  │
│  - Based on the error and your investigation                     │
│  - What's your best guess?                                       │
│  - What would need to be true for your code to work?             │
│  - What assumption might be wrong?                               │
│                                                                  │
│                         ↓                                        │
│                                                                  │
│  STEP 5: VERIFY                                                  │
│  ────────────────────────────────────────────────                │
│  "Test your hypothesis. Did it work? Why or why not?"            │
│                                                                  │
│  - Make ONE change at a time                                     │
│  - Did it fix it? Great, explain WHY                             │
│  - Didn't fix it? What did you learn? New hypothesis.            │
│  - Loop until resolved                                           │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Read the full file on GitHub · 315 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. 1mo ago First seen · 315 lines · 75 tokens per session scan A 3b1302395add

Subscribe to this mod's changes

protocol-d-debugging is a skill published in the GitHub repository DanielPodolsky/ownyourcode (292 stars, last pushed 4mo ago), licensed MIT. It adds 75 tokens to every session and 2,319 once invoked, about $0.0003 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

hns-lsel-curator

Local Self-Evolution Loop (LSEL) curator — the CLUSTER + drain engine for the GOOS-local PROPOSE→APPLY seam closure (SPEC-LSEL-LOCAL-EVOLUTION-001). Companion-offset drain of .moai/lessons-inbox.jsonl with a drain-side severity filter that drops the 65% Bash-timeout/sandbox noise, eventkey clustering with a frequency…

modu-ai/moai-adk · 135 tokens

moai-lane-watchdog

One lane stall watchdog iteration: measure lane progress on three channels, classify the stall cause, apply the remedy or resolve the judgment through the decision ladder — instead of waiting on a reply — then record the outcome and report. Reads the unified --auto doctrine at…

modu-ai/moai-adk · 86 tokens

moai-workflow-loop

Ralph Engine - Automated feedback loop with LSP diagnostics and AST-grep integration for continuous code quality improvement. Use when implementing error-driven development, automated fixing, or continuous quality validation workflows.

modu-ai/moai-adk · 44 tokens

moai-workflow-testing

Use when writing tests, measuring coverage, or running characterization, performance, or PR-review QA. Comprehensive specialist combining DDD testing, characterization tests, performance profiling, and TRUST 5 quality-assurance validation.

modu-ai/moai-adk · 47 tokens

langsmith-tracing

LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.

a5c-ai/babysitter · 34 tokens

quality-hooks

Language-specific auto-lint/format/typecheck pipeline. Supports Python (ruff+pyright), TypeScript (prettier+eslint+tsc), Go (gofmt+golangci-lint). Auto-fix and convergence loops.

a5c-ai/babysitter · 53 tokens