re0-upgrade
Bring your installed paperthin skills up to the full current catalog in one step: retire…
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
$ npx -y skills add LilMGenius/paperthin --skill modelchk --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/modelchkContext preview
The summary Claude sees to decide when to auto-load this skill.
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
name: modelchk description: "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."
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.
From a single risk-and-complexity read, recommend two coordinates.
**Capability tier** — the cheapest sufficient class of mind:
**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:
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.
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:
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.
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>
Before finishing, confirm the report:
Turning old engineering wisdom into reflexes your agent reaches for on its own. On any agent | Claude Code, Codex, OpenCode, Antigravity, Copilot, Cursor, Grok-Build, Pi, Hermes, OpenClaw, etc.
Repo: LilMGenius/paperthin
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