Borrowing it
Nothing to install: this file belongs to dzigi00/linkedin-claude-automation. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dzigi00/linkedin-claude-automation/main/.opencode/agents/silent-failure-hunter.mdgit clone --depth 1 https://github.com/dzigi00/linkedin-claude-automationWrote 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/dzigi00/linkedin-claude-automation/silent-failure-hunter)<a href="https://agentmods.dev/agents/dzigi00/linkedin-claude-automation/silent-failure-hunter"><img src="https://agentmods.dev/badge/agents/dzigi00/linkedin-claude-automation/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/dzigi00/linkedin-claude-automation/silent-failure-hunter"><img src="https://agentmods.dev/badge/agents/dzigi00/linkedin-claude-automation/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 | $0.00286 | $0.01627 |
| Opus 5 | $0.00143 | $0.00813 |
| Sonnet 5 | $0.00057 | $0.00325 |
| Haiku 4.5 | $0.00029 | $0.00163 |
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 10d 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.
This is a copy
91% identical to silent-failure-hunter — 43 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite error handling auditor with zero tolerance for silent failures and inadequate error handling. 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 operate under these non-negotiable rules:
- 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 a PR, 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 (logError for production issues)?
- Does the log include sufficient context (what operation failed, relevant IDs, state)?
- Is there an error ID from constants/errorIds.ts for Sentry tracking?
- 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.
- 10d ago First seen · 168 lines · 286 tokens per session scan A 149784c32b4d
silent-failure-hunter is an agent published in the GitHub repository dzigi00/linkedin-claude-automation (0 stars, last pushed 3mo ago), licensed MIT. It adds 286 tokens to every session and 1,627 once invoked, about $0.0014 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to silent-failure-hunter, differing in 43 lines, and is treated as a copy.
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