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 skills add majiayu000/claude-skill-registry --skill agent-debugginggit clone --depth 1 https://github.com/majiayu000/claude-skill-registryWrote 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/majiayu000/claude-skill-registry/agent-debugging)<a href="https://agentmods.dev/skills/majiayu000/claude-skill-registry/agent-debugging"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-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.
<a href="https://agentmods.dev/skills/majiayu000/claude-skill-registry/agent-debugging"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-debugging.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00058 | $0.06667 |
| Opus 5 | $0.00029 | $0.03334 |
| Sonnet 5 | $0.00012 | $0.01333 |
| Haiku 4.5 | $0.00006 | $0.00667 |
Grade B, and why
agent-debugging scanned grade B with 2 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 8d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
const response = await fetch('https://your-domain.com/twiml/test', { method: 'POST', Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST https://your-domain.com/twiml/test-123 This is a copy
100% identical to agent-debugging — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,061 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Debugging Skill
Systematic debugging and troubleshooting framework for ElevenLabs Conversational AI + Twilio integration on the Next Level Real Estate platform. This skill provides diagnostic procedures, root cause analysis techniques, and resolution strategies.
When to Use This Skill
Invoke this skill when you need to:
- ✅ Debug agent configuration issues
- ✅ Analyze failed or poor-quality calls
- ✅ Troubleshoot audio/latency problems
- ✅ Investigate conversation breakdowns
- ✅ Review and interpret error logs
- ✅ Diagnose context injection failures
- ✅ Identify performance bottlenecks
- ✅ Generate diagnostic reports
Debugging Methodology
The 5-Step Debug Process
1. OBSERVE → Gather symptoms and error data
2. REPRODUCE → Confirm issue is consistent
3. ISOLATE → Narrow down to specific component
4. DIAGNOSE → Identify root cause
5. FIX → Implement and verify solution
Common Issues & Solutions
Issue Category 1: Agent Configuration
Problem: Agent Not Starting/Responding
Symptoms:
- Agent status shows "inactive" or "error"
- Conversation fails to start
- No greeting heard on call
- Error: "Agent not found" or "Invalid agent ID"
Diagnostic Steps:
# 1. Check agent exists and is active
Use mcp__elevenlabs__elevenlabs_get_agent with:
{
"agentId": "your_agent_id"
}
# Expected output:
# - status: "active"
# - voiceId: valid voice ID
# - modelId: valid model
# - systemPrompt: non-empty
# 2. Verify voice is valid
Use mcp__elevenlabs__elevenlabs_get_voice with:
{
"voiceId": "voice_from_agent_config"
}
# 3. Check agent configuration file
Read .claude/agents/elevenlabs-agent-manager.md
# Look for:
# - Syntax errors in frontmatter
# - Invalid tool names
# - Malformed YAML
Common Root Causes:
| Root Cause | Symptoms | Solution |
|---|---|---|
| Invalid voice ID | Agent fails to start | Use list_voices to find valid ID |
| Voice not available for TTS | "Voice not supported" error | Select voice with availableForTts: true |
| Empty system prompt | Generic/confused responses | Add detailed system prompt |
| Wrong model ID | "Model not found" error | Use: eleven_flash_v2_5, eleven_turbo_v2_5, or eleven_multilingual_v2 |
| Agent not deployed | "Agent not found" | Re-create agent or verify agent ID |
What ships with it
1 file 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.
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.
- 8d ago First seen · 1,061 lines · 58 tokens per session scan B 039f1e6805cd
agent-debugging is a skill published in the GitHub repository majiayu000/claude-skill-registry (604 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 6,667 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). It is 100% identical to agent-debugging, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
performance-regression-analysis
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distributed-trace-analysis
Use this skill when you need to analyze distributed traces for call paths, latency, errors, and evidence gaps; triggers include distributed trace analysis.
flaky-test-analysis
Use this skill when you need to investigate intermittent test failures from run history and evidence; triggers include flaky test analysis.
log-analysis
Use this skill when you need to analyze logs into evidence, timelines, anomalies, and follow-up hypotheses; triggers include log analysis.
production-incident-analysis
Use this skill when you need to analyze production-incident evidence, impact, and follow-up actions; triggers include production incident analysis.
root-cause-analysis
Use this skill when you need to form and verify evidence-based root-cause hypotheses; triggers include root cause analysis.