Horse is a lightweight web framework for Delphi and Lazarus programs, providing tools for building HTTP servers and APIs. It is for developers who need routing, request handling, middleware, streaming, WebSockets, and related server features in those languages. Its catalogue skills support working with the framework.
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 HashLoad/horse --skill horse-performance-tuninggit clone --depth 1 https://github.com/HashLoad/horseWrote 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/hashload/horse/horse-performance-tuning)<a href="https://agentmods.dev/skills/hashload/horse/horse-performance-tuning"><img src="https://agentmods.dev/badge/skills/hashload/horse/horse-performance-tuning/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/hashload/horse/horse-performance-tuning"><img src="https://agentmods.dev/badge/skills/hashload/horse/horse-performance-tuning.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 | $0.00026 | $0.00770 |
| Opus 5 | $0.00013 | $0.00385 |
| Sonnet 5 | $0.00005 | $0.00154 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
horse-performance-tuning 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Horse Performance Tuning
To exploit the raw speed and low latency of the Horse framework, write handlers that avoid CPU bottlenecks and memory allocation overhead.
1. Minimizing Heap Allocations
Memory allocations (creating objects, large arrays, or concatenating strings) require thread synchronization locks in the memory manager, which slows down concurrent execution under heavy loads.
- Avoid Repeated JSON Parsing: If you just need to proxy or return static JSON payloads, send them as raw string strings or static stream resources rather than creating and destroying
TJSONObjectinstances. - Avoid String Concatenation: In loops, never concatenate strings using the
+operator. UseTStringBuilderinstead to avoid repeatedly reallocating memory on the heap.
// Inefficient (creates hundreds of temporary heap strings)
for I := 1 to 1000 do
LResponseText := LResponseText + LData[I];
// Efficient
LBuilder := TStringBuilder.Create;
try
for I := 1 to 1000 do
LBuilder.Append(LData[I]);
Res.Send(LBuilder.ToString);
finally
LBuilder.Free;
end;
2. Fast Streaming for Large Payloads
When transferring large JSON strings, files, or reports, do not load the entire file contents into a string variable. Stream it directly to the socket chunk-by-chunk using Res.SendFile or Res.Download to maintain a low RAM profile.
- Bad: Loading a 100MB PDF into a
TStringListor byte array. - Good: Passing a
TFileStreamdirectly to the response (letting Horse stream it efficiently).
procedure ServeFileHandler(Req: THorseRequest; Res: THorseResponse);
var
LStream: TFileStream;
begin
LStream := TFileStream.Create('C:\data\largefile.zip', fmOpenRead or fmShareDenyWrite);
Res.Status(THTTPStatus.OK).SendFile(LStream, 'largefile.zip');
// Do NOT free LStream. Horse takes ownership of the stream.
end;
3. Selecting and Tuning the Transport Provider
The default Indy provider (Horse.Provider.Console) uses a thread-per-connection model. Under massive concurrency (thousands of connections), this model incurs thread-switching overhead.
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 · 69 lines · 26 tokens per session scan A ecd8539538b9
horse-performance-tuning is a skill published in the GitHub repository HashLoad/horse (1,373 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 770 once invoked, about $0.0001 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.
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