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 Dankosik/go-service-template-rest --skill go-performancegit clone --depth 1 https://github.com/Dankosik/go-service-template-restWrote 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/dankosik/go-service-template-rest/go-performance)<a href="https://agentmods.dev/skills/dankosik/go-service-template-rest/go-performance"><img src="https://agentmods.dev/badge/skills/dankosik/go-service-template-rest/go-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/dankosik/go-service-template-rest/go-performance"><img src="https://agentmods.dev/badge/skills/dankosik/go-service-template-rest/go-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 | $0.00033 | $0.00512 |
| Opus 5 | $0.00016 | $0.00256 |
| Sonnet 5 | $0.00007 | $0.00102 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
go-performance 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 2d 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.
Go Performance
Performance decisions and optimizations use different evidence loops:
decision: accepted workload -> multiplier or ceiling -> viable mechanisms -> structural acceptance boundary -> planned measurement
optimization: accepted budget -> comparable baseline -> attribution -> smallest change -> comparable delta
A budget without a unit, percentile, and owner is a mood. Before implementation, reject mechanisms whose amplification or ceiling cannot satisfy the accepted envelope. After implementation, claim an improvement only from comparable measurements under that workload.
For a delegated Decision or Review, or when the active artifact requires its
result interface, load the
shared specialist contract.
Ground every changed mechanism or claim in accepted workloads, SLOs, execution
paths, measurements, and rollout constraints. For comparing mechanisms,
interacting hot paths, or a decision/review handoff, record
PerformancePath{workload, budget, unit, percentile_or_capacity, multiplier, mechanism, baseline, attribution, delta, owner, proof} per path. A single local
claim can use the existing benchmark result and task artifact; record only the
fields applicable to the selected evidence loop.
Benchmarking owns proof level, workload identity, comparable evidence, and completion policy. Load one matching leaf for capture. Read that owner for measurement; the references below cover decisions.
Choose The Branch
- Decision — load amplification and scaling
for a scale-sensitive mechanism and runtime limits
for memory, GC,
GOMAXPROCS, pools, or admission. Cover the workload, multiplier or ceiling, mechanism, dominant complexity, planned proof, and reopen condition. - Review — load the review selector for the measured risk and place every affected hot path in the finding envelope.
What ships with it
5 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.
- 2d ago Changed · +5 lines f1aaf35da629
- 10d ago First seen · 44 lines · 33 tokens per session scan A 801b8f22c15d
go-performance is a skill published in the GitHub repository Dankosik/go-service-template-rest (7 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 512 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 2026-08-31.
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