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Agent skill

Horse Telemetry Observability

by HashLoad in HashLoad/horse

Guidelines for registering, executing, and optimizing native high-precision telemetry hooks (AddOnTelemetry) in the Horse Web Framework.

MITAuto-check passedDevOps & Cloud

Install Horse Telemetry Observability

skills CLI
$ npx skills add HashLoad/horse --skill horse-telemetry-observability -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HashLoad/horse horse-telemetry-observability --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HashLoad/horse.git skills-src && mkdir -p .claude/skills && cp -r skills-src/doc/skills/horse-telemetry-observability .claude/skills/horse-telemetry-observability && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
horse-telemetry-observability
GitHub stars
1.4k
Token cost
~854 tokens
SKILL.md length
239 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Guidelines for registering, executing, and optimizing native high-precision telemetry hooks (AddOnTelemetry) in the Horse Web Framework.

  • Works in 2 steps: Global Registration → Multi-Instance Registration
  • Tasks that involve Observability
  • SKILL.md covers Native Telemetry Hook…, Registering the Telemetry Hook and Design Safeguards & AI Best…
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Horse Telemetry Observability is an agent skill from HashLoad/horse. Guidelines for registering, executing, and optimizing native high-precision telemetry hooks (AddOnTelemetry) in the Horse Web Framework.

Its SKILL.md is about 850 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Observability. The repository describes itself as: Fast, opinionated, minimalist web framework for Delphi. The licence is MIT.

When your agent uses it

  • Tasks that involve Observability

Example prompts

  • “Use the horse-telemetry-observability skill to guideline for registering, executing, and optimizing native high-precision telemetry hooks…”
  • “/horse-telemetry-observability”

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Global Registration
  2. Multi-Instance Registration

What it can do on your machine

Read from SKILL.md and the folder at commit d4351a5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are pascal).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Horse Telemetry Observability loads about 854 tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 239 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~854

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HashLoad/horse at commit d4351a5, republished under its MIT licence (© HashLoad). 239 words, ~854 tokens.

Download SKILL.mdSave it as .claude/skills/horse-telemetry-observability/SKILL.md (or your agent's skills folder).
name
horse-telemetry-observability
description
Guidelines for registering, executing, and optimizing native high-precision telemetry hooks (AddOnTelemetry) in the Horse Web Framework.

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:

pascal
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:

pascal
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:

pascal
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;

Design Safeguards & AI Best Practices

  1. Catch Internal Exceptions: Always wrap the code inside custom telemetry callbacks with a try-except block (or rely on Horse's native try-except boundary) to ensure failures during metrics collecting (like database logging or APM networking errors) never interrupt the HTTP response loop or crash the socket execution thread.
  2. Zero-Allocation Logging: To maintain Horse's zero-allocation characteristics, avoid dynamic heap allocations (such as concatenating strings or creating new logger objects) inside the telemetry callback. Prefer reusing static buffers or writing to stack-allocated variables.
  3. Multi-Instance Port Resolution: Always use Req.RawWebRequest.ServerPort if you need to determine the active port of the incoming request dynamically inside the telemetry handler.
  4. FPC/Lazarus Compatibility: In FPC (Lazarus), the callback type is a standard procedural pointer. Do not use inline anonymous methods (procedure begin end) when compiling libraries or handlers for Lazarus.

© HashLoad, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in doc/skills/horse-telemetry-observability of HashLoad/horse.

Open the folder on GitHubat commit d4351a5

Compare with similar skills

Horse Telemetry Observability next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Horse Telemetry Observability compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Horse Telemetry Observability this skillHashLoad/horse1.4k—~854Automated safety check: PassMIT
Vercel Optimize Auditvercel-labs/agent-skills32k9 repos~4.3kAutomated safety check: PassNone
Kubeshark Installerkubeshark/kubeshark12k—~3.6kAutomated safety check: NotesApache-2.0
Kubeshark KFL2 Filter Referencekubeshark/kubeshark12k—~3.6kAutomated safety check: PassApache-2.0
KubeSphere ServiceMesh Managerkubesphere/kubesphere17k—~2.4kAutomated safety check: PassCustom licence
Kubernetes Network Root Cause Analysiskubeshark/kubeshark12k—~5.3kAutomated safety check: PassApache-2.0

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More from HashLoad/horse

All 23 skills in this repo

  • Horse App Structure

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    Guide for setting up Horse applications, bootstrap program (.dpr), basic console initialization, and registering modules.

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  • Horse Files Streams

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  • Horse Grpc

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Categories

Questions about Horse Telemetry Observability

What does Horse Telemetry Observability do?

Guidelines for registering, executing, and optimizing native high-precision telemetry hooks (AddOnTelemetry) in the Horse Web Framework. Horse Telemetry Observability is an agent skill from HashLoad/horse. Guidelines for registering, executing, and optimizing native high-precision telemetry hooks (AddOnTelemetry) in the Horse Web Framework.

When should I use Horse Telemetry Observability?

Horse Telemetry Observability fits situations like: tasks that involve Observability.

How do I install Horse Telemetry Observability in Claude Code?

Run `npx skills add HashLoad/horse --skill horse-telemetry-observability -a claude-code`. Or copy the skill folder (doc/skills/horse-telemetry-observability in HashLoad/horse) into .claude/skills/horse-telemetry-observability in your project. Claude Code loads it when a task matches its description.

How do I install Horse Telemetry Observability in Codex?

Run `npx skills add HashLoad/horse --skill horse-telemetry-observability -a codex`. Or copy the skill folder (doc/skills/horse-telemetry-observability in HashLoad/horse) into .agents/skills/horse-telemetry-observability in your project. Codex loads it when a task matches its description.

Can I use Horse Telemetry Observability in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HashLoad/horse --skill horse-telemetry-observability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/horse-telemetry-observability, .gemini/skills/horse-telemetry-observability, .github/skills/horse-telemetry-observability and .opencode/skills/horse-telemetry-observability in your project.

What does Horse Telemetry Observability need to run?

SKILL.md names no scripts, command-line tools or credentials: Horse Telemetry Observability is instructions for the agent only.

Does Horse Telemetry Observability access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Horse Telemetry Observability safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Horse Telemetry Observability use?

Horse Telemetry Observability is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Horse Telemetry Observability use?

About 854 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Horse Telemetry Observability?

Skills that share tags, products or a category with Horse Telemetry Observability: Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars), Kubeshark Installer (kubeshark/kubeshark, 12k stars), Kubeshark KFL2 Filter Reference (kubeshark/kubeshark, 12k stars) and KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Horse Telemetry Observability?

HashLoad (a GitHub organization) maintains it in HashLoad/horse, which has 1,377 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 7, 2026.

Source: HashLoad/horse on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.