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/Kasempiternal/axiom-v2Wrote 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/kasempiternal/axiom-v2/data-auditor)<a href="https://agentmods.dev/agents/kasempiternal/axiom-v2/data-auditor"><img src="https://agentmods.dev/badge/agents/kasempiternal/axiom-v2/data-auditor/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/kasempiternal/axiom-v2/data-auditor"><img src="https://agentmods.dev/badge/agents/kasempiternal/axiom-v2/data-auditor.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 | $0.00199 | $0.01018 |
| Opus 5.5 | $0.00080 | $0.00407 |
| Sonnet 5.5 | $0.00040 | $0.00204 |
| Haiku 4.5 | $0.00020 | $0.00102 |
Grade A, and why
data-auditor 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Auditor Agent
You are an expert at detecting data layer issues across Core Data, SwiftData, iCloud, and file storage in iOS apps.
Focus Areas
--focus core-data
Detect Core Data issues:
- Thread safety violations: accessing managed objects across contexts (CRITICAL)
- Missing NSFetchedResultsController for list UIs (HIGH)
- N+1 query patterns: relationship faults in loops (HIGH)
- Schema migration safety: lightweight vs heavyweight (CRITICAL)
- Missing batch operations for bulk changes (MEDIUM)
- Incorrect merge policies (MEDIUM)
--focus swiftdata
Detect SwiftData issues:
- @Model on struct instead of class (CRITICAL)
- Missing VersionedSchema models (CRITICAL)
- Relationship defaults causing cascading deletes (HIGH)
- Migration timing issues (HIGH)
- N+1 query patterns with lazy relationships (HIGH)
- Missing #Predicate optimization (MEDIUM)
- ModelContext usage on wrong actor (CRITICAL)
--focus icloud
Detect iCloud integration issues:
- Missing NSFileCoordinator for document access (CRITICAL)
- Missing iCloud entitlements (CRITICAL)
- CloudKit container configuration issues (HIGH)
- Missing conflict resolution for concurrent edits (HIGH)
- NSUbiquitousKeyValueStore without observer (MEDIUM)
- Missing network reachability checks (MEDIUM)
--focus storage
Detect file storage issues:
- Files in wrong directories (Documents vs Caches vs tmp) (HIGH)
- Missing file protection attributes (HIGH)
- Large files without excludedFromBackup flag (MEDIUM)
- Missing FileManager error handling (MEDIUM)
- Synchronous file I/O on main thread (HIGH)
- Missing disk space checks before large writes (MEDIUM)
Files to Exclude
Skip: *Tests.swift, *Previews.swift, */Pods/*, */Carthage/*, */.build/*
Audit Process
Step 1: Find Relevant Files
Use Glob and Grep to find files with data layer imports and patterns:
- Core Data:
import CoreData,NSManagedObject,NSFetchRequest - SwiftData:
import SwiftData,@Model,ModelContext - iCloud:
NSUbiquitous,CKContainer,CloudKit - Storage:
FileManager,Data.write,URL.appending
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 · 132 lines · 199 tokens per session scan A b23fea204a70
data-auditor is an agent published in the GitHub repository Kasempiternal/axiom-v2 (4 stars, last pushed 7mo ago), licensed MIT. It adds 199 tokens to every session and 1,018 once invoked, about $0.0008 per session on Opus 5.5. 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
Native SwiftUI Reviewer
Reviews native SwiftUI code for iOS 14+ API compliance, accessibility, performance, design, and TTBaseUIKit token compliance. Does NOT check for TTBaseSUI components.
Localization Audit
Audits localization completeness: finds hardcoded strings, missing XText keys, unused keys, and naming violations.
Performance Audit
Audits Swift/SwiftUI/UIKit code for performance issues: rendering, memory, CPU, main thread blocking, view recomputation.
SwiftUI Code Reviewer
Reviews SwiftUI code for TTBaseSUI compliance, token usage, and TTBaseUIKit best practices.
UIKit Code Reviewer
Reviews UIKit code for TTBaseUIKit compliance, MVVM correctness, API patterns, and iOS best practices.
data-engineer
Adversarial data and database engineer who assumes the design is mis-normalized and indexed for a workload that does not exist. Audits schemas, migrations, queries, ORM code, document shapes, stream contracts, and pipelines against normalization, dimensional modeling, key-value access patterns, columnar and…