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 j4flmao/agent-skills --skill concurrencygit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/concurrency)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/concurrency"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/concurrency/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/j4flmao/agent-skills/concurrency"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/concurrency.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00030 | $0.00715 |
| Opus 5 | $0.00015 | $0.00358 |
| Sonnet 5 | $0.00006 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
backend-concurrency-mechanics 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 8d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Concurrency Mechanics: Deep Architecture & Under-the-Hood Operations
1. Threading & OS Scheduling Context
Operating System threads (1:1 model) are scheduled directly by the kernel's scheduler (e.g., CFS in Linux). Thread context switching involves saving/restoring CPU registers, the program counter, and the stack pointer, incurring significant overhead (in the realm of 1-5 microseconds) and requiring user-to-kernel mode transitions. Thread stacks consume fixed memory (e.g., 1-8 MB), making a high-concurrency model fundamentally unscalable on commodity hardware due to the C10K problem.
2. Goroutines and M:N Scheduling
The M:N scheduler (like Go's) multiplexes M user-space threads (goroutines) onto N OS threads.
- P (Processor): Context for execution, maintaining a local run queue (LRQ).
- M (Machine): The OS thread executing user code.
- G (Goroutine): The user thread with dynamic stack sizing (starting at 2KB). The Go scheduler uses Work Stealing: if a P exhausts its LRQ, it randomly steals half the Gs from another P's LRQ. If an M blocks (e.g., synchronous syscall), the P detaches and binds to an idle M to continue executing other Gs, minimizing kernel context switches.
3. The Single-Threaded Event Loop (Reactor Pattern)
Node.js and Redis utilize a single-threaded Event Loop processing non-blocking I/O. The mechanics rely on OS-level multiplexing (epoll/kqueue).
- Call Stack: Executes synchronous V8 operations.
- Task Queue / Microtask Queue: Promises and callbacks. The loop constantly polls for completed I/O operations and enqueues callbacks. While CPU-bound tasks block the loop, I/O bound scalability is achieved via asynchronous continuation.
4. Synchronization: Race Conditions and Memory Barriers
A race condition is an unprotected non-atomic read-modify-write sequence.
Memory Barriers (Fences): Hardware instructions that enforce ordering constraints on memory operations. Modern CPUs employ Out-of-Order (OoO) execution and store buffers. A memory fence (e.g., mfence on x86) flushes store buffers to L1 cache and invalidates other cores' cache lines via cache coherence protocols (MESI), ensuring sequential consistency for atomic operations or mutex boundaries.
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.
- 8d ago First seen · 49 lines · 30 tokens per session scan A c2dfbbda0bcc
backend-concurrency-mechanics is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 5d ago), licensed MIT. It adds 30 tokens to every session and 715 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.
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