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Small Server Fit

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FAIRY123456789
small-server-fit

Assess whether a web application and its build and runtime plan fit a low-cost Linux VPS or Alibaba Cloud ECS before purchase, implementation, or deployment. Use when choosing an OS image, Python, Node.js, or Java versions, build location, worker count, swap strategy, or minimum CPU, RAM, and disk for a repository. Produce evidence-backed FIT, FIT WITH CHANGES, NOT FIT, or NOT MEASURED decisions; do not recommend purchasing from guessed capacity.

Overview

PublisherFAIRY123456789
Repositoryhuman-edge-agent-skills
Skill namesmall-server-fit
Stars
108
Forks
0
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by FAIRY123456789 on GitHub. Read the source before you install it.

Installation

Install the Small Server Fit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/FAIRY123456789/human-edge-agent-skills/tree/main/skills/small-server-fit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FAIRY123456789/human-edge-agent-skills.git /tmp/human-edge-agent-skills
mkdir -p .claude/skills
cp -r /tmp/human-edge-agent-skills/skills/small-server-fit .claude/skills/small-server-fit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Small Server Fit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Small Server Fit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Small Server Fit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Small Server Fit

Prevent avoidable deployment rework by checking application requirements against a measured server budget before committing to an image, runtime, or instance size.

Establish the two sides of the fit

Inspect the repository for:

  • frontend build tool and whether static assets can be built locally or in CI;
  • backend runtime and exact version constraints;
  • lockfiles, native extensions, browser binaries, machine-learning model artifacts, and serializer versions;
  • steady processes, intended worker count, background jobs, databases, uploads, logs, and temporary build space;
  • representative validation flow and concurrency expectation.

Obtain a current or candidate server profile containing OS image, architecture, vCPU, RAM, swap, disk and free space, available runtime versions, and platform services. Use references/profile-schema.md for the evidence contract.

If the host does not exist yet, mark runtime measurements NOT MEASURED and separate image compatibility from capacity claims. Do not treat a purchase date or marketing label as proof of installed runtime versions.

Make the fit decisions

Evaluate these independently:

  1. Operating-system and architecture fit: application binaries and native packages support the image and architecture.
  2. Runtime fit: an application-specific Python, Node.js, or Java runtime can be installed without replacing a system-owned runtime.
  3. Build fit: peak dependency installation and build memory and disk fit, or the build can move to a local machine or CI.
  4. Runtime memory fit: measured or bounded steady processes leave headroom for the OS, Nginx, SSH, logs, and short spikes.
  5. Persistence fit: state is outside immutable release directories and backups fit the disk or an external store.
  6. Operations fit: the service can restart, recover after reboot, expose only intended ports, and produce bounded logs.

Run scripts/evaluate_fit.py --profile <profile.json> when numeric evidence is available. The script calculates margins; it does not invent missing workload measurements.

Prefer changes in this order

  1. Build the SPA or Java artifact outside the small server.
  2. Isolate the application runtime instead of upgrading system binaries.
  3. Reduce the process or worker count when the workload and state model permit it.
  4. Move databases, object storage, or heavy build jobs to a managed or external service when justified.
  5. Add swap only as a bounded resilience measure, not as a substitute for required RAM.
  6. Increase the instance size when measured steady-state plus safety margin still does not fit.

Never recommend downgrading security, removing backups, disabling verification, or co-locating incompatible state merely to claim fit.

Output

Report each dimension as FIT, FIT WITH CHANGES, NOT FIT, or NOT MEASURED. Include:

  • the recommended OS image and runtime isolation method;
  • local or CI versus server build split;
  • process and worker budget;
  • RAM and disk arithmetic with source measurements;
  • model and native dependency compatibility risks;
  • the smallest safe next measurement or experiment;
  • a purchase recommendation only when evidence supports it.

Distinguish a confirmed fact, calculation, assumption, and recommendation. A useful result can be NOT MEASURED; false precision cannot.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Small Server Fit AI skill do?

Assess whether a web application and its build and runtime plan fit a low-cost Linux VPS or Alibaba Cloud ECS before purchase, implementation, or deployment. Use when choosing an OS image, Python, Node.js, or Java versions, build location, worker count, swap strategy, or minimum CPU, RAM, and disk for a repository. Produce evidence-backed FIT, FIT WITH CHANGES, NOT FIT, or NOT MEASURED decisions; do not recommend purchasing from guessed capacity.

Why use Small Server Fit on TypingMind?

Because you install it once and use it with any model. Small Server Fit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Small Server Fit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FAIRY123456789/human-edge-agent-skills/tree/main/skills/small-server-fit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Small Server Fit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Small Server Fit?

As many as you like. As long as a model supports skills, you can use Small Server Fit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Small Server Fit AI skill free?

Yes. It is published on GitHub by FAIRY123456789 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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