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-telemetry-observabilitygit 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-telemetry-observability)<a href="https://agentmods.dev/skills/hashload/horse/horse-telemetry-observability"><img src="https://agentmods.dev/badge/skills/hashload/horse/horse-telemetry-observability.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.00787 |
| Opus 5 | $0.00017 | $0.00394 |
| Sonnet 5 | $0.00007 | $0.00157 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
horse-telemetry-observability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Horse Telemetry & Observability
Native Telemetry Hook (AddOnTelemetry)
Horse provides a high-precision, native, and Zero-Allocation telemetry hook based on stack-allocated TStopwatch. This hook allows monitoring and logging the total processing latency of all requests passing through the server pipeline.
The telemetry callback is defined as follows:
THorseOnTelemetry = {$IF DEFINED(FPC)}procedure{$ELSE}reference to procedure{$ENDIF}(const ARequest: THorseRequest; const AResponse: THorseResponse; const AExecutionTimeMS: Double);
Registering the Telemetry Hook
1. Global Registration
For applications using the standard static THorse facade, register the telemetry hook globally during the bootstrap process:
uses
Horse, System.SysUtils;
begin
THorse.AddOnTelemetry(
procedure(const Req: THorseRequest; const Res: THorseResponse; const ExecutionTimeMS: Double)
begin
Writeln(Format('[Telemetry] %s %s - Status: %d - Latency: %.2f ms',
[Req.Method, Req.PathInfo, Res.Status, ExecutionTimeMS]));
end);
THorse.Get('/ping',
procedure(Req: THorseRequest; Res: THorseResponse)
begin
Res.Send('pong');
end);
THorse.Listen(9000);
end.
2. Multi-Instance Registration
For applications utilizing THorseInstance, register telemetry hooks directly on each instance. Telemetry is fully isolated per port and polymorphically resolved based on the incoming request port:
var
LInstance1, LInstance2: THorseInstance;
begin
LInstance1 := THorseInstance.Create;
LInstance1.AddOnTelemetry(
procedure(const Req: THorseRequest; const Res: THorseResponse; const ExecutionTimeMS: Double)
begin
Writeln(Format('[Instance 1 - Port %d] Latency: %.2f ms', [Req.RawWebRequest.ServerPort, ExecutionTimeMS]));
end);
LInstance2 := THorseInstance.Create;
LInstance2.AddOnTelemetry(
procedure(const Req: THorseRequest; const Res: THorseResponse; const ExecutionTimeMS: Double)
begin
Writeln(Format('[Instance 2 - Port %d] Latency: %.2f ms', [Req.RawWebRequest.ServerPort, ExecutionTimeMS]));
end);
end;
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 · 76 lines · 34 tokens per session scan A dcfa2de6069a
horse-telemetry-observability is a skill published in the GitHub repository HashLoad/horse (1,374 stars, last pushed 3d ago), licensed MIT. It adds 34 tokens to every session and 787 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 2026-08-30.
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