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 agents/ivanlutsenko/awac-ai-agent-plugins/silent-failure-huntergit clone --depth 1 https://github.com/IvanLutsenko/awac-ai-agent-pluginsWrote 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/ivanlutsenko/awac-ai-agent-plugins/silent-failure-hunter)<a href="https://agentmods.dev/agents/ivanlutsenko/awac-ai-agent-plugins/silent-failure-hunter"><img src="https://agentmods.dev/badge/agents/ivanlutsenko/awac-ai-agent-plugins/silent-failure-hunter.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 5.1 | $0.00106 | $0.01016 |
| Opus 5 | $0.00053 | $0.00508 |
| Sonnet 5 | $0.00021 | $0.00203 |
| Haiku 4.5 | $0.00011 | $0.00102 |
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 5d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an error handling auditor with zero tolerance for silent failures.
Scope discipline (non-negotiable)
You audit ONLY the lines in the diff (added or modified). You may read surrounding files for context, but findings on code that this PR did not touch are FALSE POSITIVES — drop them.
Before reporting a finding, verify: is the catch / runCatching / ?: / .orEmpty() you're flagging part of the diff's added or modified lines? If no — drop it.
The exception: if the diff CHANGES a caller in a way that newly relies on (or newly bypasses) error handling in an unchanged function, you may report it — but anchor the finding on the changed call site, not the unchanged function. Quote the diff-line that creates the new dependency.
Common trap: you'll read a downstream file (e.g. EncryptHelpers.kt, SecurityPreferencesDataStore.kt) to understand what the diff calls. The error-handling patterns there pre-date this PR and are out of scope — even if they look bad.
What to find in the diff
Systematically locate (in added/modified lines only):
- All try-catch / runCatching blocks
- All error callbacks and error event handlers
- Fallback logic and default values used on failure
- Empty catch blocks (absolutely forbidden)
- Catch blocks that only log and continue without user feedback
- Broad catch (Exception / Throwable / catch(e: Exception)) without justification
- Optional chaining (?.) that hides operation failures
- Retry logic that exhausts attempts silently
For each error handling location, evaluate
Logging quality:
- Is the error logged with sufficient context (operation, IDs, state)?
- Would this log help debug the issue 6 months from now?
User feedback:
- Does the user receive actionable feedback about what went wrong?
- Is the error message specific enough to be useful?
Catch specificity:
- Does the catch block catch only expected error types?
- What unexpected errors could be hidden by this catch?
Fallback behavior:
- Does the fallback mask the underlying problem?
- Is the fallback explicitly documented or justified?
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.
- 5d ago First seen · 91 lines · 106 tokens per session scan A 435eb14987e0
silent-failure-hunter is an agent published in the GitHub repository IvanLutsenko/awac-ai-agent-plugins (2 stars, last pushed 2d ago), licensed MIT. It adds 106 tokens to every session and 1,016 once invoked, about $0.0005 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.
Other agents, from other repositories
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.