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 KhaledSaeed18/dotclaude --skill benchmark-reportinggit clone --depth 1 https://github.com/KhaledSaeed18/dotclaudeWrote 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/khaledsaeed18/dotclaude/benchmark-reporting)<a href="https://agentmods.dev/skills/khaledsaeed18/dotclaude/benchmark-reporting"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/benchmark-reporting/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/khaledsaeed18/dotclaude/benchmark-reporting"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/benchmark-reporting.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 | $0.00102 | $0.01103 |
| Opus 5.5 | $0.00041 | $0.00441 |
| Sonnet 5.5 | $0.00020 | $0.00221 |
| Haiku 4.5 | $0.00010 | $0.00110 |
Grade A, and why
benchmark-reporting 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 25d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A comparison is credible when the reader can see that every system had the same chance. Most rejected results chapters fail on fairness or on variance, not on the numbers themselves.
Fairness
- Same conditions: same hardware, same data splits, same preprocessing, same evaluation script, same budget (epochs, wall-clock, or tokens; state which). Numbers copied from other papers are labelled as such in the table and are not the headline comparison.
- Tuned baselines: each baseline gets the hyperparameter search the proposed method got, or its authors' recommended settings, stated. Record the search space and budget per system.
- Current baselines: the strongest published method for the task at thesis time, not a convenient old one. If the strongest is out of reach (compute, code), say so and compare with what is feasible.
- Same metric implementation: one evaluation script for all systems; metric definitions and any thresholds stated.
Variance
- Every number is a mean over ≥ 3 seeds (or runs, or folds) with SD or a 95% CI, and n stated in the caption. A single run is reported as such and not compared.
- Statistical comparison where it matters (
statistics-advisor): a paired test across seeds or folds, effect size, corrected for the number of comparisons. - Do not bold a "best" whose CI overlaps the runner-up; bold all that are statistically indistinguishable from the best, and say the rule in the caption.
Disclosure
A configuration section or appendix table per experiment: hardware (GPU model and count, CPU, RAM), software versions, dataset version and splits, hyperparameters per system, training budget, inference settings, total compute used (GPU-hours). Cite the commit hash from experiment-reproducibility.
Tables
Generated from results/*.csv by a script into tables/*.tex (or Markdown), never typed:
- Rows: systems; columns: metrics (or datasets); grouped columns for several datasets. Transpose when there are more metrics than systems.
- Units in headers; arrows or "higher is better" once in the caption; consistent decimals per column (usually 1 or 2); thousands separators.
- Mean ± SD in one cell (
92.3 ± 0.4) or mean with CI in a second line; n in the caption. - Best in bold by rule (see above); proposed system's row last or highlighted, not first.
- Missing cells explained in a footnote (did not converge, out of memory, not applicable) rather than left blank.
booktabsin LaTeX;siunitxScolumns align decimals.
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.
- 25d ago First seen · 65 lines · 102 tokens per session scan A ce406c701b9e
benchmark-reporting is a skill published in the GitHub repository KhaledSaeed18/dotclaude (5 stars, last pushed 2d ago), licensed MIT. It adds 102 tokens to every session and 1,103 once invoked, about $0.0004 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 2026-09-22.
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