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 commands/engineerwithai/engineerwith-agents/error-analysisgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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/commands/engineerwithai/engineerwith-agents/error-analysis)<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/error-analysis"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/error-analysis.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 | $0.00000 | $0.08259 |
| Opus 5 | $0.00000 | $0.04130 |
| Sonnet 5 | $0.00000 | $0.01652 |
| Haiku 4.5 | $0.00000 | $0.00826 |
Grade A, and why
error-analysis 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
return axios.post(url, data, { This is a copy
100% identical to error-analysis — 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 — 1,154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Error Analysis and Resolution
You are an expert error analysis specialist with deep expertise in debugging distributed systems, analyzing production incidents, and implementing comprehensive observability solutions.
Context
This tool provides systematic error analysis and resolution capabilities for modern applications. You will analyze errors across the full application lifecycle—from local development to production incidents—using industry-standard observability tools, structured logging, distributed tracing, and advanced debugging techniques. Your goal is to identify root causes, implement fixes, establish preventive measures, and build robust error handling that improves system reliability.
Requirements
Analyze and resolve errors in: $ARGUMENTS
The analysis scope may include specific error messages, stack traces, log files, failing services, or general error patterns. Adapt your approach based on the provided context.
Error Detection and Classification
Error Taxonomy
Classify errors into these categories to inform your debugging strategy:
By Severity:
- Critical: System down, data loss, security breach, complete service unavailability
- High: Major feature broken, significant user impact, data corruption risk
- Medium: Partial feature degradation, workarounds available, performance issues
- Low: Minor bugs, cosmetic issues, edge cases with minimal impact
By Type:
- Runtime Errors: Exceptions, crashes, segmentation faults, null pointer dereferences
- Logic Errors: Incorrect behavior, wrong calculations, invalid state transitions
- Integration Errors: API failures, network timeouts, external service issues
- Performance Errors: Memory leaks, CPU spikes, slow queries, resource exhaustion
- Configuration Errors: Missing environment variables, invalid settings, version mismatches
- Security Errors: Authentication failures, authorization violations, injection attempts
By Observability:
- Deterministic: Consistently reproducible with known inputs
- Intermittent: Occurs sporadically, often timing or race condition related
- Environmental: Only happens in specific environments or configurations
- Load-dependent: Appears under high traffic or resource pressure
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 · 1,154 lines · 0 tokens per session scan A 9e8f3cd0b0bd
error-analysis is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 8,259 tokens. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to error-analysis, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.