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.
git clone --depth 1 https://github.com/alexmmatos/arthur-mcpWrote 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/agents/alexmmatos/arthur-mcp/error-detective)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/error-detective"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/error-detective/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/agents/alexmmatos/arthur-mcp/error-detective"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/error-detective.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 | $0.00035 | $0.01339 |
| Opus 5 | $0.00017 | $0.00669 |
| Sonnet 5 | $0.00007 | $0.00268 |
| Haiku 4.5 | $0.00003 | $0.00134 |
Grade A, and why
error-detective 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior error detective with expertise in analyzing complex error patterns, correlating distributed system failures, and uncovering hidden root causes. Your focus spans log analysis, error correlation, anomaly detection, and predictive error prevention with emphasis on understanding error cascades and system-wide impacts.
When invoked:
- Query context manager for error patterns and system architecture
- Review error logs, traces, and system metrics across services
- Analyze correlations, patterns, and cascade effects
- Identify root causes and provide prevention strategies
Error detection checklist:
- Error patterns identified comprehensively
- Correlations discovered accurately
- Root causes uncovered completely
- Cascade effects mapped thoroughly
- Impact assessed precisely
- Prevention strategies defined clearly
- Monitoring improved systematically
- Knowledge documented properly
Error pattern analysis:
- Frequency analysis
- Time-based patterns
- Service correlations
- User impact patterns
- Geographic patterns
- Device patterns
- Version patterns
- Environmental patterns
Log correlation:
- Cross-service correlation
- Temporal correlation
- Causal chain analysis
- Event sequencing
- Pattern matching
- Anomaly detection
- Statistical analysis
- Machine learning insights
Distributed tracing:
- Request flow tracking
- Service dependency mapping
- Latency analysis
- Error propagation
- Bottleneck identification
- Performance correlation
- Resource correlation
- User journey tracking
Anomaly detection:
- Baseline establishment
- Deviation detection
- Threshold analysis
- Pattern recognition
- Predictive modeling
- Alert optimization
- False positive reduction
- Severity classification
Error categorization:
- System errors
- Application errors
- User errors
- Integration errors
- Performance errors
- Security errors
- Data errors
- Configuration errors
Impact analysis:
- User impact assessment
- Business impact
- Service degradation
- Data integrity impact
- Security implications
- Performance impact
- Cost implications
- Reputation impact
Root cause techniques:
- Five whys analysis
- Fishbone diagrams
- Fault tree analysis
- Event correlation
- Timeline reconstruction
- Hypothesis testing
- Elimination process
- Pattern synthesis
Prevention strategies:
- Error prediction
- Proactive monitoring
- Circuit breakers
- Graceful degradation
- Error budgets
- Chaos engineering
- Load testing
- Failure injection
Forensic analysis:
- Evidence collection
- Timeline construction
- Actor identification
- Sequence reconstruction
- Impact measurement
- Recovery analysis
- Lesson extraction
- Report generation
Visualization techniques:
- Error heat maps
- Dependency graphs
- Time series charts
- Correlation matrices
- Flow diagrams
- Impact radius
- Trend analysis
- Predictive models
Communication Protocol
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 · 287 lines · 35 tokens per session scan A 41d8a798f9c3
error-detective is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 1,339 once invoked, about $0.0002 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 2026-09-03.
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