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/tune-kernels/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/tune-kernels)<a href="https://agentmods.dev/skills/cloudrift-ai/emmy/tune-kernels"><img src="https://agentmods.dev/badge/skills/cloudrift-ai/emmy/tune-kernels.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00075 | $0.04943 |
| Opus 5 | $0.00037 | $0.02472 |
| Sonnet 5 | $0.00015 | $0.00989 |
| Haiku 4.5 | $0.00007 | $0.00494 |
Grade A, and why
tune-kernels 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 yesterday.
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 — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tune Emmy kernels
Use one golden YAML format throughout the workflow. Keep its two trust levels separate:
- A working golden is an untracked experiment artifact. Each structural target contains a
realizationsarray; each realization has named dimensionbindings, explicit registered inputpins, and optional knob proposals, measurements, orrankingfeedback written byemmy tune. - A canonical golden is reviewed deploy evidence under
recipes/<model>/golden/, with one file per exact GPU model and compute capability. Every realization requires an explicit knobs mapping (empty for a forkless anchor) and paired positive deployableemmy_us/reference_usmeasurements with a namedreference_backend. Never write search feedback into a canonical file. Model-agnostic hardware goldens remain underemmy/compiler/pipeline/search/goldens/.
pins and knobs are both registered knob mappings with different times of application: pins constrains candidate
enumeration, while knobs records the winner measured inside that regime. FAST_MATH has no special YAML field; write
it under pins exactly like any other input knob.
emmy tune --golden PATH rejects canonical repository paths because it updates its input. Copy a canonical file to
a fresh _tune/<run>/working.yaml first. Do not commit a trace-created working golden automatically; leave that
decision to the author or agent after validation.
--golden PATH is one flag on run, compile, tune, serve and eval golden: the golden YAML whose measured
rows are the golden evidence that command deploys from, instead of the repository's per-card goldens. There is one
deploy mechanism, the measured-evidence pick: a golden row is a measured row in the same index the tune DB feeds,
never a separate authoritative tier. --realization NAME (run, compile, tune) selects one realization by
exact name or an unambiguous substring; --strict-evidence (run, compile, serve) raises instead of deploying
a prediction when a kernel has no measured evidence.
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.
- yesterday Changed · +14 lines b7ef6d12045b
- 6d ago First seen · 341 lines · 75 tokens per session scan A 874c8a36bf9b
tune-kernels is a skill published in the GitHub repository cloudrift-ai/emmy (80 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 4,943 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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