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 ericrisco/rsc-harness --skill open-weightsgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/open-weights)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/open-weights"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/open-weights/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/ericrisco/rsc-harness/open-weights"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/open-weights.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00146 | $0.04580 |
| Opus 5.5 | $0.00058 | $0.01832 |
| Sonnet 5.5 | $0.00029 | $0.00916 |
| Haiku 4.5 | $0.00015 | $0.00458 |
Grade A, and why
open-weights 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.
How it starts
The opening of the file, as written. The whole thing — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open weights — pick the model, then clear the license
This is the CHOICE layer. Given a task, a box, and a shipping constraint, it tells you which open model to reach for and whether the license lets you ship it. It does not download, run, serve, or fine-tune anything — it feeds the skills that do (see Related skills).
The one rule (read before you name a model)
Model names and their licenses change monthly. Never state a license from memory — open the model card and confirm it, every time. This file was authored against cards live in mid-2026; by the time you read it, versions have shipped and terms have moved. So:
- Treat every specific version/license line below as [verify the card], not as settled fact.
- State the license class (OSI-open vs custom-community vs restricted), then send the reader to
the actual
LICENSE/ terms page for the exact model + size they intend to ship. - The decision framework and the class taxonomy are the durable parts. The version list is the perishable part. Weight your trust accordingly.
Real, verified-mid-2026 examples of why this rule exists — all confirmed against the source, and all things that were different at my Jan-2026 cutoff:
- Gemma 4 moved to Apache-2.0, while Gemma 1–3 stay on the custom Gemma Terms of Use (confirmed on ai.google.dev/gemma/terms, accessed 2026-07-18). Same brand, opposite license class depending on version.
- Codestral 2 was relicensed Apache-2.0 (per Mistral, Apr 2026), while the original Codestral stays non-production (MNPL). Same name, opposite shippability depending on release.
- DeepSeek-R1 is MIT, but the original DeepSeek-V3 weights ship under a custom DeepSeek License Agreement with OpenRAIL-style use restrictions (confirmed on the V3 LICENSE-MODEL), not MIT. Same family, different license per model.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 240 lines · 146 tokens per session scan A e628511e0b4a
open-weights is a skill published in the GitHub repository ericrisco/rsc-harness (239 stars, last pushed yesterday), licensed MIT. It adds 146 tokens to every session and 4,580 once invoked, about $0.0006 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 2026-09-03.
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