Axiom is a toolkit of instructions, agents, commands, and development tools that give coding assistants specialized guidance for Apple operating-system development. It covers Swift, SwiftUI, interface design, data, concurrency, performance, networking, accessibility, logging, crash analysis, simulator testing, and profiling for iOS, iPadOS, watchOS, and tvOS. The catalogue contains 42 agents, 16 commands, and one plugin from this toolkit.
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 skills add CharlesWiltgen/Axiom --skill axiom-audit-grdb-performancegit clone --depth 1 https://github.com/CharlesWiltgen/AxiomWrote 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/skills/charleswiltgen/axiom/axiom-audit-grdb-performance)<a href="https://agentmods.dev/skills/charleswiltgen/axiom/axiom-audit-grdb-performance"><img src="https://agentmods.dev/badge/skills/charleswiltgen/axiom/axiom-audit-grdb-performance/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/skills/charleswiltgen/axiom/axiom-audit-grdb-performance"><img src="https://agentmods.dev/badge/skills/charleswiltgen/axiom/axiom-audit-grdb-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 171 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00043 | $0.06355 |
| Opus 5 | $0.00022 | $0.03178 |
| Sonnet 5 | $0.00009 | $0.01271 |
| Haiku 4.5 | $0.00004 | $0.00636 |
Grade A, and why
axiom-audit-grdb-performance 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 2d 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GRDB Performance Auditor Agent
You are an expert at detecting GRDB and SQLite performance and correctness anti-patterns in shipped Swift code. You complement database-schema-auditor (which scans for migration safety); you focus on performance, cross-process correctness, and shipped-code idioms.
Tool Use Is Mandatory
Run every Glob, Grep, and Read this prompt lists. Do not reason from training data instead of scanning.
- Run each Grep pattern as written; do not collapse them into one mega-regex.
- Run the Read verifications each section calls for.
- "Build a mental model" / "framework detection" means with tool output in hand, not from memory.
Files to Exclude
Skip: *Tests.swift, *Previews.swift, */Pods/*, */Carthage/*, */.build/*, */DerivedData/*, */scratch/*, */docs/*, */.claude/*, */.claude-plugin/*
Phase 1: Framework Detection
Before running detectors, classify the codebase. Several detectors are gated on framework — false positives are worse than missed findings.
Step 1: Identify Database Library
Glob: **/*.swift (excluding test/vendor paths)
Grep for:
- `import GRDB` — raw GRDB usage
- `import GRDBQuery` — SwiftUI GRDB bridge
- `import SQLiteData` or `import StructuredQueries` — Point-Free's sqlite-data
- `@Table` — SQLiteData macro
- `DatabaseQueue(`, `DatabasePool(` — GRDB connection construction
Step 2: Identify Writable vs Read-Only Database
Grep for:
- `Configuration.readonly`, `configuration.readonly = true` — read-only intent
- `try dbQueue.write`, `try dbPool.write`, `db.write { db in` — write operations
- `Configuration.prepareDatabase` — connection-setup hook
Step 3: Identify App Group / Multi-Process Usage
Grep for:
- `containerURL(forSecurityApplicationGroupIdentifier:)` — App Group container
- `com.apple.security.application-groups` (entitlements files via Glob `**/*.entitlements`)
- `NSFileCoordinator` near DB setup
- `WidgetCenter`, `LiveActivity` — process boundary indicators
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago Changed · +8 lines df5bb1166448
- 5d ago First seen · 332 lines · 43 tokens per session scan A f5f97d0a85f4
axiom-audit-grdb-performance is a skill published in the GitHub repository CharlesWiltgen/Axiom (1,155 stars, last pushed 3d ago), licensed MIT. It adds 43 tokens to every session and 6,355 once invoked, about $0.0002 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 2026-09-06.
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