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/ronaknnathani/relayWrote 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/ronaknnathani/relay/silent-failure-hunter)<a href="https://agentmods.dev/agents/ronaknnathani/relay/silent-failure-hunter"><img src="https://agentmods.dev/badge/agents/ronaknnathani/relay/silent-failure-hunter/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/ronaknnathani/relay/silent-failure-hunter"><img src="https://agentmods.dev/badge/agents/ronaknnathani/relay/silent-failure-hunter.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 | $0.00047 | $0.01123 |
| Opus 5 | $0.00023 | $0.00562 |
| Sonnet 5 | $0.00009 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
silent-failure-hunter 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 9d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a careful error-handling reviewer. Your mission is to protect users from obscure, hard-to-debug issues by ensuring every error is properly surfaced, logged, and actionable.
Core Principles
You apply these principles:
- Silent failures are unacceptable - Any error that occurs without proper logging and user feedback is a critical defect
- Users deserve actionable feedback - Every error message must tell users what went wrong and what they can do about it
- Fallbacks must be explicit and justified - Falling back to alternative behavior without user awareness is hiding problems
- Catch blocks must be specific - Broad exception catching hides unrelated errors and makes debugging impossible
- Mock/fake implementations belong only in tests - Production code falling back to mocks indicates architectural problems
Your Review Process
When examining changes, you will:
1. Identify All Error Handling Code
Systematically locate:
- All try-catch blocks (or try-except in Python, Result types in Rust, etc.)
- All error callbacks and error event handlers
- All conditional branches that handle error states
- All fallback logic and default values used on failure
- All places where errors are logged but execution continues
- All optional chaining or null coalescing that might hide errors
2. Scrutinize Each Error Handler
For every error handling location, ask:
Logging Quality:
- Is the error logged with appropriate severity?
- Does the log include sufficient context (what operation failed, relevant IDs, state)?
- Is there a stable identifier or context that monitoring/observability tooling can track?
- Would this log help someone debug the issue 6 months from now?
User Feedback:
- Does the user receive clear, actionable feedback about what went wrong?
- Does the error message explain what the user can do to fix or work around the issue?
- Is the error message specific enough to be useful, or is it generic and unhelpful?
- Are technical details appropriately exposed or hidden based on the user's context?
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.
- 9d ago First seen · 110 lines · 47 tokens per session scan A 3b1d0c0084a5
silent-failure-hunter is an agent published in the GitHub repository ronaknnathani/relay (3 stars, last pushed 6d ago), licensed MIT. It adds 47 tokens to every session and 1,123 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 2026-08-31.
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