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/irahardianto/awesome-agv/debugging-protocolnpx skills add irahardianto/awesome-agv --skill debugging-protocolgit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote 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.
[](https://agentmods.dev/skills/irahardianto/awesome-agv/debugging-protocol)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/debugging-protocol"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/debugging-protocol.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00044 | $0.01318 |
| Opus 5 | $0.00022 | $0.00659 |
| Sonnet 5 | $0.00009 | $0.00264 |
| Haiku 4.5 | $0.00004 | $0.00132 |
Grade A, and why
debugging-protocol scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
* **Code Pattern**: Provide the exact code or command to run (e.g., a specific SQL query, a Python script using the client library, a `curl` command). How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging Protocol
Overview
This skill provides a rigorous framework for debugging complex software issues. It moves beyond ad-hoc troubleshooting to a structured process of hypothesis generation and validation.
Use this skill to:
- Formalize a debugging session.
- Systematically eliminate potential root causes.
- Document findings for future reference or team communication.
Protocol Workflow
To run a structured debugging session, follow these steps:
1. Initialize the Session
Create a new debugging document using the provided template. This serves as the "source of truth" for the investigation.
Template location: assets/debugging-session-template.md
Save to: docs/debugging/{issue-name}-{YYYY-MM-DD}-{HHmm}.md
- Create
docs/debugging/if it doesn't exist - Copy the template and fill in the issue details
- This makes the session accessible from other conversations and agents (e.g., when handing off to a
/bugfixor/workflow-soloworkflow)
2. Define the Problem
Clearly articulate the System Context and Problem Statement.
- Symptom: What is the observable behavior? How does it differ from expected behavior?
- Scope: Which components are involved?
3. Formulate Hypotheses
List distinct, testable hypotheses.
- Avoid vague guesses.
- Differentiate between layers (e.g., "Frontend Hypothesis" vs "Backend Hypothesis").
- Example: "Race condition in UI state update" vs "Database schema misconfiguration".
4. Design Validation Tasks
For each hypothesis, design a specific validation task.
- Objective: What are you trying to prove or disprove?
- Steps: Precise, reproducible actions.
- Code Pattern: Provide the exact code or command to run (e.g., a specific SQL query, a Python script using the client library, a
curlcommand). - Success Criteria: Explicitly state what output confirms the hypothesis.
5. Execute and Document
Run the tasks in order. For each task, record:
- Status: ✅ VALIDATED, ❌ FAILED, or ⚠️ INCONCLUSIVE.
- Findings: Key observations and raw evidence (logs, screenshots).
- Conclusion: Does this support or refute the hypothesis?
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/debugging-session-template.md 1.5 KB
- languages/cpp.md 6.6 KB
- languages/csharp.md 6.8 KB
- languages/flutter.md 7.0 KB
- languages/frontend.md 15 KB
- languages/go.md 8.2 KB
- languages/java.md 7.4 KB
- languages/kotlin.md 6.4 KB
- languages/php.md 5.7 KB
- languages/python.md 6.9 KB
- languages/ruby.md 6.1 KB
- languages/rust.md 6.3 KB
- languages/swift.md 6.5 KB
- languages/typescript.md 8.1 KB
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.
- 5d ago First seen · 105 lines · 44 tokens per session scan A a68847cb2935
debugging-protocol is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 14d ago), licensed MIT. It adds 44 tokens to every session and 1,318 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
agenttrace-session-audit
Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.
maintaining-windows-health
Hands-on playbook for Windows 11 disk cleanup, dev-machine optimization, and proactive health alerting. Use when the PC is full or slow, when a BSOD / Kernel-Power 41 / crash dump / commit-memory pressure happened, when the user asks to free disk space, audit storage, set up disk/memory alerts, or restore the same…
prompt-engineering
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic…
plugins-management
Create, publish, delete, and submit plugins for coding agents (Claude Code, OpenCode). Use when user wants to (1) create a new plugin with proper structure, (2) create or configure a plugin marketplace, (3) publish plugins to GitHub/GitLab/npm, (4) delete/uninstall plugins, (5) validate plugin structure, or (6)…
installing-cli-tools
Install, upgrade, configure, and verify developer CLI tools safely. Use when a user asks to install a new CLI, command-line app, SDK tool, package-manager binary, GitHub release binary, language runtime tool, or AI/vendor CLI; configure shell PATH/completions; run first login; set API keys, tokens, or env variables…
repo-activity-summary
Summarize a repository's recent engineering activity from git history — technologies, work types, churn hotspots, contributor patterns, and velocity. Use when asking "what has this repo been working on", "is this project active", "who contributes what", "where are the hotspots", or before onboarding onto an unfamiliar…