
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/lilmgenius/paperthin --skill modelchk- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads modelchk automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
fastfor local, mechanical, reversible work with cheap, complete verification.standardfor ordinary repo-grounded reasoning, multi-step drafting, normal coding, and conventional documentation or skill work.frontierfor 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
- Frame the exact work unit being sized: task, artifact, review, rerun, or plan.
- 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.
- Read off the capability tier: the cheapest class whose ceiling covers the work's judgment and risk.
- Read off the reasoning effort: default it to track the tier (
fast→glance,standard→measured,frontier→thorough, reservingexhaustivefor 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. - Report both coordinates, one shared rationale,
move up if...andmove down if...triggers for each dial, and the proof surface — the verification the work still needs regardless of tier or effort. - 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 isglance/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 stayfast+thoroughwhen 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
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.
FAQ
What does the modelchk 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.
How do I install modelchk?
Run npx skills add https://github.com/lilmgenius/paperthin --skill modelchk in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/modelchk/ (personal) or .claude/skills/modelchk/ (project).
Which AI agents can use modelchk?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is modelchk safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains modelchk?
It is published by lilmgenius in lilmgenius/paperthin. It has 1,199 recorded installs.
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