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 agentmods add skills/zakelfassi/skills-driven-development/experiment-lognpx skills add zakelfassi/skills-driven-development --skill experiment-loggit clone --depth 1 https://github.com/zakelfassi/skills-driven-developmentWrote 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/zakelfassi/skills-driven-development/experiment-log)<a href="https://agentmods.dev/skills/zakelfassi/skills-driven-development/experiment-log"><img src="https://agentmods.dev/badge/skills/zakelfassi/skills-driven-development/experiment-log.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 | $0.00054 | $0.00960 |
| Opus 5 | $0.00027 | $0.00480 |
| Sonnet 5 | $0.00011 | $0.00192 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
experiment-log 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 4d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Log
Record a training run's parameters, metrics, and artifact paths in the experiments ledger.
Inputs
- Experiment name (e.g.,
churn-v3-lgbm) - Run ID (auto-generated if omitted:
{name}-{YYYYMMDD-HHmmss}) - Parameters (key-value pairs, e.g.,
learning_rate=0.05 n_estimators=500) - Metrics (key-value pairs, e.g.,
auc=0.83 f1=0.71 precision=0.79 recall=0.64) - Artifact path (model checkpoint, serialized pipeline, etc.)
- Notes (free-form, optional)
Steps
-
Collect run metadata
import datetime, hashlib, json, os run_id = "{name}-" + datetime.datetime.utcnow().strftime("%Y%m%d-%H%M%S") git_sha = os.popen("git rev-parse --short HEAD").read().strip() dataset_version = open("data/.version").read().strip() # semver or hash -
Capture parameters and metrics If using a training script that outputs JSON:
python train.py --config config/{name}.yaml --output-metrics /tmp/metrics.jsonOtherwise, capture values directly from the trainer object:
params = model.get_params() metrics = {"auc": roc_auc_score(y_test, y_pred), "f1": f1_score(y_test, y_pred)} -
Save artifacts
import joblib artifact_path = f"artifacts/{run_id}/model.pkl" os.makedirs(os.path.dirname(artifact_path), exist_ok=True) joblib.dump(model, artifact_path) -
Append to the experiments ledger
scripts/log-experiment.sh \ --name "{name}" \ --run-id "{run_id}" \ --params '{"learning_rate": 0.05}' \ --metrics '{"auc": 0.83, "f1": 0.71}' \ --artifact "{artifact_path}" \ --notes "{notes}"The ledger is
experiments/log.csv— a flat CSV with one row per run. -
Verify the entry
tail -n 5 experiments/log.csvConfirm: run_id is unique, metrics are numeric, artifact path exists.
-
Compare with previous runs (optional)
python scripts/compare-runs.py --metric auc --top 5
What ships with it
1 file 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.
- 4d ago First seen · 92 lines · 54 tokens per session scan A 4ec240bd91ed
experiment-log is a skill published in the GitHub repository zakelfassi/skills-driven-development (18 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 960 once invoked, about $0.0003 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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