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 rules/luxvil/ai-coding-rules/73-error-handlinggit clone --depth 1 https://github.com/Luxvil/ai-coding-rulesWhat 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 | $0.00281 |
| Opus 5 | $0.00000 | $0.00140 |
| Sonnet 5 | $0.00000 | $0.00056 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
73-error-handling 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 yesterday.
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.
This is a copy
100% identical to 73-error-handling — 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.
What it actually says
Error Handling Rules
Philosophy
- Fail fast, fail loud
- Errors are values (not just exceptions)
- Never swallow errors silently
- Log with context
Patterns
TypeScript/JavaScript
// Prefer Result types for expected failures
type Result<T, E> = { ok: true; value: T } | { ok: false; error: E };
// Use try-catch for unexpected failures
try {
await riskyOperation();
} catch (error) {
logger.error('Operation failed', { error, context });
throw new AppError('USER_FRIENDLY_MESSAGE', { cause: error });
}
Python
# Use specific exceptions
try:
risky_operation()
except SpecificError as e:
logger.error("Operation failed", exc_info=True)
raise AppError("User message") from e
Best Practices
- Create custom error classes
- Include error codes for programmatic handling
- Separate user-facing from developer messages
- Add correlation IDs for tracing
- Never expose stack traces to users
- Log at appropriate levels (error vs warn vs info)
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.
- yesterday First seen · 49 lines · 0 tokens per session scan A e64c37a30f54
73-error-handling is a cursor rule published in the GitHub repository Luxvil/ai-coding-rules (3 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 281 tokens. A static security scan graded it A with 0 findings. It is 100% identical to 73-error-handling, differing in 0 lines, and is treated as a copy.
Other cursor rules, from other repositories
exam-answer-format
Guidelines for writing exam-style answers in a practical, conversational style with definitions first followed by real-world examples.
lecture-reference-linking
Guidelines for including course materials lists and inline references when writing exam answers or documentation that references course materials.
short-answer-version
Guidelines for creating short/concise versions of detailed answers.
documentation-formatting
Guidelines for formatting markdown documentation to improve readability and scannability.
mermaid-diagrams
Guidelines for adding Mermaid diagrams to exam answers and documentation with automatic SVG generation support.
human-writing-style
Rules for writing naturally and authentically - avoid robotic AI tone in documentation, code comments, error messages, and explanations. Write like a human colleague, not a customer service bot.