modelchk

modelchk is a skill for Claude Code from LilMGenius/paperthin. It costs 74 tokens per session (1,406 once invoked), scanned A, original, MIT.

A read-only guide for estimating how much model capability and reasoning effort a task needs before running it.

In plain words
What is it for?
Use it to assess a task and recommend a capability tier, such as fast, standard, or frontier, plus an appropriate reasoning level.
Why use it?
It helps avoid using more computing effort than a task needs while flagging work that is ambiguous, risky, or architecturally complex.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the paperthin plugin — 28 skills shipped together

Good fit Use it to assess a task and recommend a capability tier, such as fast, standard, or frontier, plus an appropriate reasoning level.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lilmgenius/paperthin/modelchk
About the project

Paperthin is a collection of low-level design patterns that coding agents can apply as repeatable checks and actions during software work. It is for people using agents such as Claude Code, Codex, Cursor, or other supported tools, and its catalogue entries package the patterns as skills, an instruction, and a plugin.

LilMGenius/paperthin · 1,329 stars · on GitHub · paperthin.ai

Install

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.

Any agent
npx skills add LilMGenius/paperthin --skill modelchk
Clone the repo
git clone --depth 1 https://github.com/LilMGenius/paperthin

Made for: Claude Code.

Or install paperthin, the plugin that ships this one along with the rest of its 28 skills.

Wrote 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.

agentmods badge for modelchk

README.md
[![agentmods](https://agentmods.dev/badge/skills/lilmgenius/paperthin/modelchk/github.svg)](https://agentmods.dev/skills/lilmgenius/paperthin/modelchk)
Your own site
<a href="https://agentmods.dev/skills/lilmgenius/paperthin/modelchk"><img src="https://agentmods.dev/badge/skills/lilmgenius/paperthin/modelchk/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.

agentmods 80×15 button for modelchk

Your own site · 80×15
<a href="https://agentmods.dev/skills/lilmgenius/paperthin/modelchk"><img src="https://agentmods.dev/badge/skills/lilmgenius/paperthin/modelchk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,406 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 20 Jul 2026
  • Snyk pass 20 Jul 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00074 $0.01406
Opus 5.5 $0.00030 $0.00562
Sonnet 5.5 $0.00015 $0.00281
Haiku 4.5 $0.00007 $0.00141

Measured 1mo ago against content hash e8c04bfd3203, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

modelchk 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 1mo 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/depth/modelchk/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 78 lines

Changes

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.

  1. 1mo ago First seen · 78 lines · 74 tokens per session scan A e8c04bfd3203

Subscribe to this mod's changes

modelchk is a skill published in the GitHub repository LilMGenius/paperthin (1,329 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 1,406 once invoked, about $0.0003 per session on Opus 5.5. 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.