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 agentmods add agents/charleswiltgen/axiom/memory-auditorgit 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/agents/charleswiltgen/axiom/memory-auditor)<a href="https://agentmods.dev/agents/charleswiltgen/axiom/memory-auditor"><img src="https://agentmods.dev/badge/agents/charleswiltgen/axiom/memory-auditor.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 | $0.00135 | $0.03033 |
| Opus 5 | $0.00068 | $0.01517 |
| Sonnet 5 | $0.00027 | $0.00607 |
| Haiku 4.5 | $0.00014 | $0.00303 |
Grade A, and why
memory-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 6d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Auditor Agent
You are an expert at detecting memory leak patterns — both known anti-patterns AND missing/incomplete resource lifecycle management that causes progressive memory growth and crashes.
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" / "map the architecture" 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: Map Resource Ownership
Step 1: Identify Resource-Owning Classes
Glob: **/*.swift (excluding test/vendor paths)
Grep for:
- `Timer.scheduledTimer`, `Timer.publish` — timer ownership
- `addObserver`, `NotificationCenter`, `.sink`, `.assign(to:` — observer ownership
- `var.*Task<`, `Task {` stored in properties — async task ownership
- `var.*delegate:`, `var.*Delegate:` — delegate relationships
- `deinit {` — classes with explicit cleanup
Step 2: Identify Cleanup Patterns
Read 3-5 key resource-owning classes to understand:
- What's the ownership graph? (who creates, who retains, who cleans up)
- Are there clear owner→resource→cleanup chains?
- Which classes have
deinitand which don't? - Are there objects that accumulate resources without bounds?
Step 3: Identify Long-Lived Objects
Grep for:
- `static let`, `static var` — singletons (intentionally long-lived)
- `shared` — shared instances
- Classes without clear deallocation point
Output
Write a brief Resource Ownership Map (5-10 lines) summarizing:
- Which classes own long-lived resources
- Where cleanup happens (deinit, onDisappear, explicit teardown)
- Any classes that own resources but lack cleanup
- Singleton/static instances (intentionally long-lived — not bugs)
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.
- 6d ago First seen · 274 lines · 135 tokens per session scan A 1a7389424421
memory-auditor is an agent published in the GitHub repository CharlesWiltgen/Axiom (1,147 stars, last pushed 8d ago), licensed MIT. It adds 135 tokens to every session and 3,033 once invoked, about $0.0007 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 2026-08-30.
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.