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 mohitmishra786/low-level-dev-skills --skill lldbgit clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skillsWrote 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/mohitmishra786/low-level-dev-skills/lldb)<a href="https://agentmods.dev/skills/mohitmishra786/low-level-dev-skills/lldb"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/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/mohitmishra786/low-level-dev-skills/lldb"><img src="https://agentmods.dev/badge/skills/mohitmishra786/low-level-dev-skills/lldb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00099 | $0.01709 |
| Opus 5 | $0.00049 | $0.00855 |
| Sonnet 5 | $0.00020 | $0.00342 |
| Haiku 4.5 | $0.00010 | $0.00171 |
Grade A, and why
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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLDB
Purpose
Guide agents through LLDB sessions and map existing GDB knowledge to LLDB. Covers command differences, Apple specifics, Python scripting, and IDE integration.
Triggers
- "I'm on macOS and need to debug a C++ program"
- "How does LLDB differ from GDB?"
- "How do I do [GDB command] in LLDB?"
- "LLDB shows
<unavailable>for variables" - "How do I use LLDB in VS Code?"
- "How do I write an LLDB Python script?"
Workflow
1. Start LLDB
lldb ./prog # load binary
lldb ./prog -- arg1 arg2 # with arguments
lldb -p 12345 # attach to PID
lldb -c core.1234 # load core dump
lldb ./prog core.1234 # binary + core
2. GDB → LLDB command map
Source: https://lldb.llvm.org/use/map.html
| GDB | LLDB | Notes |
|---|---|---|
run [args] |
process launch [args] / r |
|
continue |
process continue / c |
|
next |
thread step-over / n |
|
step |
thread step-in / s |
|
nexti |
thread step-inst-over / ni |
|
stepi |
thread step-inst / si |
|
finish |
thread step-out / finish |
|
break main |
breakpoint set -n main / b main |
|
break file.c:42 |
breakpoint set -f file.c -l 42 / b file.c:42 |
|
break *0x400abc |
breakpoint set -a 0x400abc / b -a 0x400abc |
|
watch x |
watchpoint set variable x / wa s v x |
|
print x |
frame variable x / p x |
|
print/x x |
p/x x |
|
info locals |
frame variable / fr v |
|
info args |
frame variable --arguments |
|
backtrace |
thread backtrace / bt |
|
frame N |
frame select N / f N |
|
info threads |
thread list |
|
thread N |
thread select N |
|
thread apply all bt |
thread backtrace all |
|
x/10wx addr |
memory read -s4 -fx -c10 addr / x/10xw addr |
|
set var = 42 |
expression var = 42 / expr var = 42 |
|
quit |
quit / q |
3. Breakpoints
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.
- 9d ago First seen · 229 lines · 99 tokens per session scan A bcf7ab626afa
lldb is a skill published in the GitHub repository mohitmishra786/low-level-dev-skills (198 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 1,709 once invoked, about $0.0005 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 skills, from other repositories
debugging
Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.
log-analyzer
Parse agent log files to identify error patterns, rate limit hits, timeout clusters, tool failures, and component-level error counts. Produces a structured anomaly report. Cron-compatible — silent if no issues, alert digest if anomalies found. Also computes per-tool failure rates from a Hermes profile state.db…
error-handler
Design error handling, structured logging, and observability with OpenTelemetry (traces, metrics, logs), error classification, recovery patterns (retry with jitter, circuit breaker, bulkhead, timeout), error budgets/SLOs with burn rate alerts, and production incident triage. Use when user asks to implement error…
performance-profiler
Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile…
scientific-debugging
A method for debugging software by observing the problem, forming possible explanations, running small experiments, and then fixing and checking the result.
diagnosing-ml-failures
Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance…