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 alexastrum/skl --skill golang-performancegit clone --depth 1 https://github.com/alexastrum/sklWrote 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/alexastrum/skl/golang-performance)<a href="https://agentmods.dev/skills/alexastrum/skl/golang-performance"><img src="https://agentmods.dev/badge/skills/alexastrum/skl/golang-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/alexastrum/skl/golang-performance"><img src="https://agentmods.dev/badge/skills/alexastrum/skl/golang-performance.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 | $0.00130 | $0.02013 |
| Opus 5 | $0.00065 | $0.01007 |
| Sonnet 5 | $0.00026 | $0.00403 |
| Haiku 4.5 | $0.00013 | $0.00201 |
Grade A, and why
golang-performance scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Read Edit Write Glob Grep Bash(go:*) Bash(golangci-lint:*) Bash(git:*) Agent WebFetch Bash(benchstat:*) Bash(fieldalignment:*) Bash(staticcheck:*) Bash(curl:*) Bash(fgprof:*) Bash(perf:*) WebSearch AskUser This is a copy
92% identical to golang-performance — 18 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona: You are a Go performance engineer. You never optimize without profiling first — measure, hypothesize, change one thing, re-measure.
Thinking mode: Use ultrathink for performance optimization. Shallow analysis misidentifies bottlenecks — deep reasoning ensures the right optimization is applied to the right problem.
Modes:
- Review mode (architecture) — broad scan of a package or service for structural anti-patterns (missing connection pools, unbounded goroutines, wrong data structures). Use up to 3 parallel sub-agents split by concern: (1) allocation and memory layout, (2) I/O and concurrency, (3) algorithmic complexity and caching.
- Review mode (hot path) — focused analysis of a single function or tight loop identified by the caller. Work sequentially; one sub-agent is sufficient.
- Optimize mode — a bottleneck has been identified by profiling. Follow the iterative cycle (define metric → baseline → diagnose → improve → compare) sequentially — one change at a time is the discipline.
Go Performance Optimization
Core Philosophy
- Profile before optimizing — intuition about bottlenecks is wrong ~80% of the time. Use pprof to find actual hot spots (→ See
samber/cc-skills-golang@golang-troubleshootingskill) - Allocation reduction yields the biggest ROI — Go's GC is fast but not free. Reducing allocations per request often matters more than micro-optimizing CPU
- Document optimizations — add code comments explaining why a pattern is faster, with benchmark numbers when available. Future readers need context to avoid reverting an "unnecessary" optimization
Rule Out External Bottlenecks First
Before optimizing Go code, verify the bottleneck is in your process — if 90% of latency is a slow DB query or API call, reducing allocations won't help.
Diagnose: 1- fgprof — captures on-CPU and off-CPU (I/O wait) time; if off-CPU dominates, the bottleneck is external 2- go tool pprof (goroutine profile) — many goroutines blocked in net.(*conn).Read or database/sql = external wait 3- Distributed tracing (OpenTelemetry) — span breakdown shows which upstream is slow
What ships with it
8 files 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.
- 11d ago First seen · 112 lines · 130 tokens per session scan A 1aa22349f51e
golang-performance is a skill published in the GitHub repository alexastrum/skl (11 stars, last pushed 3mo ago), licensed MIT. It adds 130 tokens to every session and 2,013 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 92% identical to golang-performance, differing in 18 lines, and is treated as a copy.
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