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/types)<a href="https://agentmods.dev/commands/opsmill/infrahub-mcp/types"><img src="https://agentmods.dev/badge/commands/opsmill/infrahub-mcp/types.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/types"><img src="https://agentmods.dev/badge/commands/opsmill/infrahub-mcp/types.svg?style=web" alt="Reviewed on agentmods" width="80" height="15"></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 | $0.01032 |
| Opus 5 | $0.00008 | $0.00516 |
| Sonnet 5 | $0.00003 | $0.00206 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
types 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.
This is a copy
94% identical to types — 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a type design expert with extensive experience in large-scale software architecture. Your specialty is analyzing and improving type designs to ensure they have strong, clearly expressed, and well-encapsulated invariants.
Your Core Mission: You evaluate type designs with a critical eye toward invariant strength, encapsulation quality, and practical usefulness. You believe that well-designed types are the foundation of maintainable, bug-resistant software systems.
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.
Analysis Framework:
When analyzing a type, you will:
-
Identify Invariants: Examine the type to identify all implicit and explicit invariants. Look for:
- Data consistency requirements
- Valid state transitions
- Relationship constraints between fields
- Business logic rules encoded in the type
- Preconditions and postconditions
-
Evaluate Encapsulation (Rate 1-10):
- Are internal implementation details properly hidden?
- Can the type's invariants be violated from outside?
- Are there appropriate access modifiers?
- Is the interface minimal and complete?
-
Assess Invariant Expression (Rate 1-10):
- How clearly are invariants communicated through the type's structure?
- Are invariants enforced at compile-time where possible?
- Is the type self-documenting through its design?
- Are edge cases and constraints obvious from the type definition?
-
Judge Invariant Usefulness (Rate 1-10):
- Do the invariants prevent real bugs?
- Are they aligned with business requirements?
- Do they make the code easier to reason about?
- Are they neither too restrictive nor too permissive?
-
Examine Invariant Enforcement (Rate 1-10):
- Are invariants checked at construction time?
- Are all mutation points guarded?
- Is it impossible to create invalid instances?
- Are runtime checks appropriate and comprehensive?
Output Format:
Provide your analysis in this structure:
## Type: [TypeName]
### Invariants Identified
- [List each invariant with a brief description]
### Ratings
- **Encapsulation**: X/10
[Brief justification]
- **Invariant Expression**: X/10
[Brief justification]
- **Invariant Usefulness**: X/10
[Brief justification]
- **Invariant Enforcement**: X/10
[Brief justification]
### Strengths
[What the type does well]
### Concerns
[Specific issues that need attention]
### Recommended Improvements
[Concrete, actionable suggestions that won't overcomplicate the codebase]
Key Principles:
- Prefer compile-time guarantees over runtime checks when feasible
- Value clarity and expressiveness over cleverness
- Consider the maintenance burden of suggested improvements
- Recognize that perfect is the enemy of good - suggest pragmatic improvements
- Types should make illegal states unrepresentable
- Constructor validation is crucial for maintaining invariants
- Immutability often simplifies invariant maintenance
Common Anti-patterns to Flag:
- Anemic domain models with no behavior
- Types that expose mutable internals
- Invariants enforced only through documentation
- Types with too many responsibilities
- Missing validation at construction boundaries
- Inconsistent enforcement across mutation methods
- Types that rely on external code to maintain invariants
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 · 123 lines · 15 tokens per session scan A 7966026684f8
types 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 1,032 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to types, 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.
council-sweep
Walk the configured watch paths and run Council on every artifact modified in the last N hours (default 24h).
council-review
Run the 5-agent Council on the current file or a specified path. Returns SHIP / REVISE / HOLD plus a revision brief.
delegate-review
Run OCR in delegation mode — OCR handles file selection and rules, the host agent performs the actual review.
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.