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.
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.
npx skills add LilMGenius/paperthin --skill modelchkgit clone --depth 1 https://github.com/LilMGenius/paperthinWrote 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.
[](https://agentmods.dev/skills/lilmgenius/paperthin/modelchk)<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.
<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>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- modelchk — 100% identical
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:
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.
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.
- 1mo ago First seen · 78 lines · 74 tokens per session scan A e8c04bfd3203
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.
Other skills, from other repositories
mm-setup
A setup guide that personalizes a shared mm system for one user. It asks for details such as your name, work area, tools, language, and Obsidian vault location, then saves them in private configuration files.
mm-update
A guided updater for the mm system, which is installed by copying a Git repository rather than through a package manager. It downloads the latest version, registers its skills again, and tells you to restart Claude Code.
mm-instructions
A generator for ready-to-paste instructions for a Claude.ai project. It reads project details and fills a template with the project's name, technology, planning information, and current work context.
mm-projects
A read-only overview of all registered projects stored in Obsidian notes. It shows each project's status, type, current GSD phase, recent activity, and number of open questions.
mm
A dispatcher that provides short /mm commands for project setup, session recovery, project overviews, and workflow checks. It maps these aliases to longer system commands.
goal-loop
Drive a verifiable goal to done with factory-built agents, visible and held-out evidence, and a cross-provider judge.