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Modelchk

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LilMGenius
modelchk

Size a task's run before spending it: the cheapest sufficient capability tier (fast, standard, frontier) and the reasoning effort within it, on a neutral scale that binds to whatever levels the model exposes. Use when work seems over- or under-powered, costly, ambiguous, or high-risk, or asks which model class and how much thinking is enough.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill namemodelchk
Stars
1.3K
Forks
118
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Modelchk 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/LilMGenius/paperthin/tree/main/skills/depth/modelchk
  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/LilMGenius/paperthin.git /tmp/paperthin
mkdir -p .claude/skills
cp -r /tmp/paperthin/skills/depth/modelchk .claude/skills/modelchk
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Modelchk 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 Modelchk 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 Modelchk 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.

Size the run before you spend it: how strong a model, and how hard it should think.

modelchk is read-only and advisory. From one assessment it sizes the two dials that set a model's per-run cognitive spend — capability tier and reasoning effort. It does not choose, route, switch, pin, spawn, set, or require any concrete model or level.

Goal

From a single risk-and-complexity read, recommend two coordinates.

Capability tier — the cheapest sufficient class of mind:

  • fast for local, mechanical, reversible work with cheap, complete verification.
  • standard for ordinary repo-grounded reasoning, multi-step drafting, normal coding, and conventional documentation or skill work.
  • frontier for architecture, high ambiguity, safety/security/privacy/data-loss risk, release-critical review, cross-domain scope, or work where one wrong assumption wastes a large run.

Reasoning effort — how hard that mind should deliberate. modelchk recommends the effort intent; resolving it to the active model's actual level — like choosing the model itself — is the executor's step, not this skill's. From least to most deliberation:

  • glance — minimal deliberation; take the direct path. (Resolves to the model's floor.)
  • measured — ordinary, everyday deliberation. (The model's default, or the middle of its ladder when no default is named.)
  • thorough — deliberate extra: work the alternatives and check the assumptions. (Above the everyday setting, short of the top.)
  • exhaustive — maximal deliberation; exhaust the search and re-check the work. (The model's ceiling.)

The two axes are orthogonal — a bounded-but-fiddly task can be fast + thorough, a quick expert call frontier + glance — yet in most work they move together, parting only when a cheap task needs hard thinking or a strong model needs only a quick call. Effort buys deliberation, never capability, and more of it is not more correct.

Workflow

  1. Frame the exact work unit being sized: task, artifact, review, rerun, or plan.
  2. Score risk and complexity once — this single read feeds both coordinates:
    • file, module, or ownership boundary crossing;
    • reversibility and blast radius;
    • safety, security, privacy, publishing, or data-loss risk;
    • novelty, ambiguity, and long-context synthesis load;
    • need for external research, adversarial review, or careful release sequencing;
    • cost of a wrong answer.
  3. Read off the capability tier: the cheapest class whose ceiling covers the work's judgment and risk.
  4. Read off the reasoning effort: default it to track the tier (fast→glance, standard→measured, frontier→thorough, reserving exhaustive for the hardest, highest-stakes work), then deviate where deliberation-hunger and capability-need part — raise it for ambiguity, long multi-step reasoning, or adversarial self-check on an otherwise cheap task; lower it for a bounded task under a strong model.
  5. Report both coordinates, one shared rationale, move up if... and move down if... triggers for each dial, and the proof surface — the verification the work still needs regardless of tier or effort.
  6. Stop.

Rules

  • Size the run's cognitive spend, nothing else. The two dials are which mind (capability tier) and how hard it thinks (reasoning effort). Orchestration dials — context budget, fan-out width, tool-permission scope — are a different reflex and stay out; sharing this one risk read does not pull them in.
  • Neutral language only: tier is fast/standard/frontier; effort is glance/measured/thorough/exhaustive, each an intent defined by a position on the active model's ladder — floor, default, above-default, ceiling — never a named vendor level.
  • When resolved, effort binds to positions, not levels: a model lacking an interior level collapses the rung to the nearest it offers, so the intent always maps to a real setting and never resolves out of range.
  • The default is the cheapest sufficient tier and the effort that meets the work, not the strongest of either.
  • Effort is deliberation budget, not capability and not answer length. Never raise it to buy capability — that is the tier's job.
  • No routing authority. This skill recommends two coordinates; the user, harness, or executor decides what runs and sets the actual level.
  • Do not name concrete model products, vendors, or versions in durable mechanism text.
  • Risk beats size: a one-file high-risk change can want frontier; a broad mechanical rename can stay fast + thorough when verification is complete.
  • Verification is separate. A stronger tier or a higher effort never replaces tests, review, command output, manual QA, or other proof surface.
  • Do not execute the task being sized, change configuration, call another model, or alter provider settings.
  • Do not override an explicit user tier or effort choice. Report the mismatch if one is visible.

Output

text
recommended_tier: fast|standard|frontier
recommended_effort: glance|measured|thorough|exhaustive
rationale: <one sentence, covering both dials>
move_up_if: <signals that would justify a stronger tier or higher effort>
move_down_if: <signals that would justify a cheaper tier or lower effort>
proof_surface: <verification still required, independent of tier and effort>

Verification

Before finishing, confirm the report:

  • names exactly one tier (fast/standard/frontier) and one effort (glance/measured/thorough/exhaustive);
  • states the cheapest sufficient tier and the effort that meets the work, not the strongest of either;
  • gives move-up and move-down triggers covering both dials;
  • names the proof surface;
  • makes no routing, switching, provider, vendor, product, version, or concrete-level claim, and names no orchestration dial beyond the two cognitive-spend ones.

Frequently asked questions

What does the Modelchk AI skill do?

Size a task's run before spending it: the cheapest sufficient capability tier (fast, standard, frontier) and the reasoning effort within it, on a neutral scale that binds to whatever levels the model exposes. Use when work seems over- or under-powered, costly, ambiguous, or high-risk, or asks which model class and how much thinking is enough.

Why use Modelchk on TypingMind?

Because you install it once and use it with any model. Modelchk 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 Modelchk in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LilMGenius/paperthin/tree/main/skills/depth/modelchk. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Modelchk?

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 Modelchk?

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

Is the Modelchk AI skill free?

Yes. It is published on GitHub by LilMGenius 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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