log-experiment

log-experiment is a skill for Claude Code, Codex from Dogacel/auto-gpu-kernel. It costs 33 tokens per session (500 once invoked), scanned A, original, Apache-2.0.

An experiment-recording skill for optimization work. It saves the tested kernel, benchmark log, results, and lessons in a numbered experiment folder and updates the summary index.

In plain words
What is it for?
Use it after every benchmark to record the code tested, pass and error results, timing measurements, runner details, and conclusions.
Why use it?
It prevents benchmark attempts, including failures, from being lost or overwritten. Keeping the conditions and results together makes later comparisons possible.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it after every benchmark to record the code tested, pass and error results, timing measurements, runner details, and conclusions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dogacel/auto-gpu-kernel/log-experiment
Install

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.

Any agent
npx skills add Dogacel/auto-gpu-kernel --skill log-experiment
Clone the repo
git clone --depth 1 https://github.com/Dogacel/auto-gpu-kernel

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for log-experiment

README.md
[![agentmods](https://agentmods.dev/badge/skills/dogacel/auto-gpu-kernel/log-experiment/github.svg)](https://agentmods.dev/skills/dogacel/auto-gpu-kernel/log-experiment)
Your own site
<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.

agentmods 80×15 button for log-experiment

Your own site · 80×15
<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>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 500 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.00500
Opus 5 $0.00016 $0.00250
Sonnet 5 $0.00007 $0.00100
Haiku 4.5 $0.00003 $0.00050

Measured 2d ago against content hash d714e7dce189, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-14, from the pricing page.

Security

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 2d 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.

kopt/assets/skills/log-experiment/SKILL.md · 51 lines

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.md exists without result.md → use exp_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

  1. Copy the kernel (the entry_point file from config.toml) into the folder, same filename.
  2. Copy the benchmark log to bench.log in the folder.
  3. 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`.
  1. 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.

Changes

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.

  1. 2d ago First seen · 51 lines · 33 tokens per session scan A d714e7dce189

Subscribe to this mod's changes

log-experiment is a skill published in the GitHub repository Dogacel/auto-gpu-kernel (161 stars, last pushed 2d ago), licensed Apache-2.0. It adds 33 tokens to every session and 500 once invoked, about $0.0002 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 2026-09-13.

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