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 ComeOnOliver/skillshub --skill axiom-lldbgit clone --depth 1 https://github.com/ComeOnOliver/skillshubWrote 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/comeonoliver/skillshub/axiom-lldb)<a href="https://agentmods.dev/skills/comeonoliver/skillshub/axiom-lldb"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-lldb/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/comeonoliver/skillshub/axiom-lldb"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-lldb.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 | $0.00035 | $0.05293 |
| Opus 5 | $0.00017 | $0.02646 |
| Sonnet 5 | $0.00007 | $0.01059 |
| Haiku 4.5 | $0.00003 | $0.00529 |
Grade A, and why
axiom-lldb 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 9d 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 — 633 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLDB Debugging
Interactive debugging with LLDB. The debugger freezes time so you can interrogate your running app — inspect variables, evaluate expressions, navigate threads, and understand exactly why something went wrong.
Core insight: "LLDB is useless" really means "I don't know which command to use for Swift types." This is a knowledge-gap problem, not a tool problem.
Red Flags — Check This Skill When
| Symptom | This Skill Applies |
|---|---|
| Need to inspect a variable at runtime | Yes — breakpoint + inspect |
| Crash you can reproduce locally | Yes — breakpoint before crash site |
| Wrong value at runtime but code looks correct | Yes — step through and inspect |
| Need to understand thread state during hang | Yes — pause + thread backtrace |
po doesn't work / shows garbage |
Yes — Playbook 3 has alternatives |
| Crash log analyzed, need to reproduce | Yes — set breakpoints from crash context |
| Need to test a fix without rebuilding | Yes — expression evaluation |
| Want to break on all exceptions | Yes — exception breakpoints |
| App feels slow but responsive | No — use axiom-performance-profiling |
| Memory grows over time | No — use axiom-memory-debugging first |
| App completely frozen | Maybe — use axiom-hang-diagnostics first, then LLDB for thread inspection |
| Crash in production, no local repro | No — use axiom-testflight-triage first |
LLDB vs Other Tools
digraph tool_selection {
"What do you need?" [shape=diamond];
"axiom-testflight-triage" [shape=box];
"axiom-hang-diagnostics" [shape=box];
"axiom-memory-debugging" [shape=box];
"axiom-performance-profiling" [shape=box];
"LLDB (this skill)" [shape=box, style=bold];
"What do you need?" -> "axiom-testflight-triage" [label="Crash log from field,\ncan't reproduce locally"];
"What do you need?" -> "axiom-hang-diagnostics" [label="App frozen,\nneed diagnosis approach"];
"What do you need?" -> "axiom-memory-debugging" [label="Memory growing,\nneed leak pattern"];
"What do you need?" -> "axiom-performance-profiling" [label="Need to measure\nCPU/memory over time"];
"What do you need?" -> "LLDB (this skill)" [label="Need to inspect state\nat a specific moment"];
}
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.
- 9d ago First seen · 633 lines · 35 tokens per session scan A bcfe4476a242
axiom-lldb is a skill published in the GitHub repository ComeOnOliver/skillshub (63 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 5,293 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 2026-09-03.
Other skills, from other repositories
cmd_flutter_build
Fix Dart analyzer errors and Flutter build failures incrementally. Invokes the dart-build-resolver agent for minimal, surgical fixes.
cmd_gradle_build
Fix Gradle build errors for Android and KMP projects.
agent-introspection-debugging
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
swift-protocol-di-testing
Protocol-based dependency injection for testable Swift code — mock file system, network, and external APIs using focused protocols and Swift Testing.
swift-actor-persistence
Thread-safe data persistence in Swift using actors — in-memory cache with file-backed storage, eliminating data races by design.
firebase-crashlytics
Use when implementing crash reporting, capturing fatal/non-fatal errors, recording isolate/async exceptions, customizing reports, or uploading obfuscated symbols.