modelchk

modelchk is a skill for Claude Code, Codex 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 CodeCodex

Part of the paperthin plugin — 28 skills shipped together

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.

agentmods
npx agentmods add skills/lilmgenius/paperthin/modelchk
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, Codex.

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.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.svg" alt="Measured on agentmods" 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. Scan, not verified.
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 $0.00074 $0.01406
Opus 5 $0.00037 $0.00703
Sonnet 5 $0.00015 $0.00281
Haiku 4.5 $0.00007 $0.00141

Measured 4d ago against content hash e8c04bfd3203, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 4d 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.

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 (fastglance, standardmeasured, frontierthorough, 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. 4d 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 (1,054 stars, last pushed 16d ago), licensed MIT. It adds 74 tokens to every session and 1,406 once invoked, about $0.0004 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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