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 Dogacel/auto-gpu-kernel --skill log-experimentgit clone --depth 1 https://github.com/Dogacel/auto-gpu-kernelWrote 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/dogacel/auto-gpu-kernel/log-experiment)<a href="https://agentmods.dev/skills/dogacel/auto-gpu-kernel/log-experiment"><img src="https://agentmods.dev/badge/skills/dogacel/auto-gpu-kernel/log-experiment/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/dogacel/auto-gpu-kernel/log-experiment"><img src="https://agentmods.dev/badge/skills/dogacel/auto-gpu-kernel/log-experiment.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00033 | $0.00500 |
| Opus 5.5 | $0.00013 | $0.00200 |
| Sonnet 5.5 | $0.00007 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
log-experiment 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.
What it actually says
log-experiment
Log the most recent experiment. Never skip — failures are as valuable as wins.
Pick folder
List experiments/exp_*/. Let N = highest number.
- If
exp_N/plan.mdexists withoutresult.md→ useexp_N/. - Else → create
exp_(N+1)/. - No folders yet →
exp_1/.
Never overwrite an existing result.md. If you'd have to, stop and ask the user.
Write artifacts
- Copy the kernel (the
entry_pointfile fromconfig.toml) into the folder, same filename. - Copy the benchmark log to
bench.login the folder. - Write
result.md:
# Experiment N — YYYY-MM-DD
**Description:** what changed, why. Reference `plan.md` when implementing one.
**Runner:** <backend> / <gpu> (from config.toml — results are only comparable within one backend)
## Results
- Pass: X/Y
- Kernel latency (ms): small=S.SSS / large=L.LLL / overall=O.OOO (min / mean / median / max)
- Reference latency (ms): R.RRR
- Max abs err: X.XXe-X | Max rel err: X.XXe-X
- Mode: quick | stride N | full (| ab-vs-exp_K if A/B)
## Learnings
What was learned. What to try or avoid next. If durable cross-experiment insight, also append one line to `experiments/LESSONS.md`.
- Append to
experiments/summary.md(create with header row if missing):
| Exp | Date | Description | Latency | Ref | Pass | Backend | Notes |
|---|---|---|---|---|---|---|---|
| N | YYYY-MM-DD | one phrase | O.OOO ms | R.RRR ms | X/Y | modal/B200 | Δ% vs prior best, "new best" / "regression" / "ablation" |
Keep Notes terse. Detail lives in result.md.
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 · 51 lines · 33 tokens per session scan A d714e7dce189
log-experiment is a skill published in the GitHub repository Dogacel/auto-gpu-kernel (317 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 500 once invoked, about $0.0001 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 2026-09-13.
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