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/punt-labs/beadleWrote 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/punt-labs/beadle/silent-failure-hunter)<a href="https://agentmods.dev/agents/punt-labs/beadle/silent-failure-hunter"><img src="https://agentmods.dev/badge/agents/punt-labs/beadle/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/punt-labs/beadle/silent-failure-hunter"><img src="https://agentmods.dev/badge/agents/punt-labs/beadle/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 | $0.00308 | $0.01669 |
| Opus 5.5 | $0.00123 | $0.00668 |
| Sonnet 5.5 | $0.00062 | $0.00334 |
| Haiku 4.5 | $0.00031 | $0.00167 |
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 today.
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
94% identical to silent-failure-hunter — 19 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 — 134 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, following this repo's own logging conventions?
- Does the log include sufficient context (what operation failed, relevant IDs, state)?
- Does the project have an error-ID or error-code scheme for tracking (check this repo's CLAUDE.md); if so, is it used here?
- 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.
- today First seen · 134 lines · 0 tokens per session scan A 41afc234f28f
silent-failure-hunter is an agent published in the GitHub repository punt-labs/beadle (3 stars, last pushed today), licensed MIT. It adds 308 tokens to every session and 1,669 once invoked, about $0.0012 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to silent-failure-hunter, differing in 19 lines, and is treated as a copy.
Other agents, from other repositories
silent-failure-hunter
Use this agent when reviewing code changes in a pull request to identify silent failures, inadequate error handling, and inappropriate fallback behavior. This agent should be invoked proactively after completing a logical chunk of work that involves error handling, catch blocks, fallback logic, or any code that could…
streaming-reviewer
Streaming / event-driven pre-implementation reviewer. Outputs threat model TM-{slug}.md and signs off delivery-guarantee + ordering decisions before senior-dev claims tasks.
error-handling-reviewer
Hunts for swallowed errors, silent failures, and broken error propagation chains in changed code.
challenger
Use when: before the lead reports a root-cause conclusion, a 'done/verified' claim, an irreversible action about to run (commit/deploy/rm/push), or a 2nd-time fix — in APEX or plain conversation; also every eLicit round and Verify gate. Do NOT use for: code correctness/lint/types/API usage (sniper's job), or as a veto…
bug-detector
Detects correctness bugs, logic errors, edge cases, API misuse, and error handling issues in code changes.
ia-performance-oracle
Analyzes code for performance bottlenecks, algorithmic complexity, database queries, memory usage, and scalability. Use after implementing features or when performance concerns arise.