Borrowing it
Nothing to install: this file belongs to cloudrift-ai/emmy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cloudrift-ai/emmy/main/.agents/skills/discover-models/SKILL.mdgit clone --depth 1 https://github.com/cloudrift-ai/emmyWrote 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/cloudrift-ai/emmy/discover-models)<a href="https://agentmods.dev/skills/cloudrift-ai/emmy/discover-models"><img src="https://agentmods.dev/badge/skills/cloudrift-ai/emmy/discover-models.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.03632 |
| Opus 5 | $0.00039 | $0.01816 |
| Sonnet 5 | $0.00015 | $0.00726 |
| Haiku 4.5 | $0.00008 | $0.00363 |
Grade A, and why
discover-models 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 8d 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discover Models to Explore
Turn "what models are worth our GPU hours?" into either a concrete shortlist or a refresh of Emmy's recipe lifecycle. New open-weight models are filtered to the ones with real demand, then mapped to the GPU configurations they fit. Every recipe receives a 0-100 heat score for current discovery priority. Existing recipes are ranked by current community and serving value so the small maintained set stays focused, useful lower-priority recipes stay available on a best-effort basis, and only technically superseded or unusable models become obsolete.
Everything here is keyless and read-only: scripts/new_models.py hits public OpenRouter + HuggingFace
endpoints, and the rest is web search. No servers are touched. In automated lifecycle mode the skill returns a compact
selection; repository-owned workflow code restores existing onboarding data, derives the complete manifest, validates
it, and writes recipe tags and onboarding shells.
Automated rolling PR prerequisite
The discovery agent remains read-only in lifecycle mode. When an existing rolling discovery PR or unpaired discovery branch is present, workflow orchestration must complete these steps before it builds the recipe inventory or starts this skill:
- Fetch the latest
mainand the exact current remote head of the rolling branch. - Fail if the checked-out head no longer matches that remote head.
- Rebase the rolling branch onto
main; fail without applying lifecycle updates if the rebase conflicts. - Push a changed rebase with an exact
--force-with-leaseexpectation for the original remote head.
The workflow, not the discovery agent, owns this Git mutation. A lease failure means another writer advanced the branch, so the run must stop rather than overwrite it. Research and lifecycle classification begin only from the successfully rebased checkout.
Pipeline
HF/OpenRouter data ─┐
Reddit discussions ─┼→ reconcile exact models → score heat → VRAM fit calc → hardware → model matrix
OpenRouter/Arena ───┘ (parent agent) (0-100) (params×quant) (the deliverable)
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.
- 8d ago First seen · 244 lines · 77 tokens per session scan A 5678d44a6756
discover-models is a skill published in the GitHub repository cloudrift-ai/emmy (80 stars, last pushed today), licensed Apache-2.0. It adds 77 tokens to every session and 3,632 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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