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/opsmill/infrahub-mcpWrote 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/commands/opsmill/infrahub-mcp/errors)<a href="https://agentmods.dev/commands/opsmill/infrahub-mcp/errors"><img src="https://agentmods.dev/badge/commands/opsmill/infrahub-mcp/errors/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/commands/opsmill/infrahub-mcp/errors"><img src="https://agentmods.dev/badge/commands/opsmill/infrahub-mcp/errors.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 | $0.00015 | $0.01537 |
| Opus 5.5 | $0.00006 | $0.00615 |
| Sonnet 5.5 | $0.00003 | $0.00307 |
| Haiku 4.5 | $0.00002 | $0.00154 |
Grade A, and why
errors 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 1mo 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
94% identical to errors — 9 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 — 143 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.
Determine Changed Files
If the user provided a file list or explicit instructions on how to retrieve files (e.g., only staged, only unstaged, a specific folder, etc.), follow those instructions directly.
Otherwise, fall back to the default: execute the {SCRIPT} with --json to detect changed files. The script automatically picks the best detection mode:
- Mode A (feature branch): diffs the current branch against the default branch (
main/master) from the merge-base, plus any staged and unstaged changes.- Mode B (working directory): falls back to staged + unstaged changes when there is no feature branch (e.g., working directly on the default branch).
JSON output:
{"branch", "default_branch", "mode", "changed_files": [...]}Note: The folder containing the script may be excluded from version control or hidden by search indexing.
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 error handling constructs (try-catch, try-except, rescue, Result types, error returns, 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 null-safe operators (optional chaining, safe navigation, null coalescing) that might hide errors
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.
- 1mo ago First seen · 143 lines · 15 tokens per session scan A f9c0cf2557ec
errors is a command published in the GitHub repository opsmill/infrahub-mcp (10 stars, last pushed yesterday), licensed Apache-2.0. It adds 15 tokens to every session and 1,537 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to errors, differing in 9 lines, and is treated as a copy.
Other commands, from other repositories
review
A guided command for reviewing code changes by examining the current Git diff, the record of edits between versions. It checks quality, bugs, security, performance, and maintainability.
code-quality
Code review and bug prediction — find quality issues, style violations, and likely bugs.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.