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 Haohaha-11/Paper-Writing --skill experiments-writergit clone --depth 1 https://github.com/Haohaha-11/Paper-WritingWrote 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/haohaha-11/paper-writing/experiments-writer)<a href="https://agentmods.dev/skills/haohaha-11/paper-writing/experiments-writer"><img src="https://agentmods.dev/badge/skills/haohaha-11/paper-writing/experiments-writer/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/haohaha-11/paper-writing/experiments-writer"><img src="https://agentmods.dev/badge/skills/haohaha-11/paper-writing/experiments-writer.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 | $0.00000 | $0.00634 |
| Opus 5 | $0.00000 | $0.00317 |
| Sonnet 5 | $0.00000 | $0.00127 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
experiments-writer 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 10d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiments Writer
Purpose
Draft or revise the Experiments section so that the evidence directly answers the paper's claims. Experiments should be a proof plan, not a collection of convenient results.
When To Use
- Planning experiments before submission.
- Revising an experiments section after reviewer-style feedback.
- Checking baseline, metric, dataset, and ablation coverage.
- Preparing a venue-specific empirical story.
Inputs
Required:
- claim-evidence matrix;
- datasets and splits;
- baselines;
- metrics;
- implementation and training protocol;
- main results;
- ablations.
Optional:
- statistical tests;
- seed variance;
- qualitative examples;
- failure cases;
- compute budget.
Procedure
- Map each main claim to an experiment or analysis.
- Define datasets, splits, metrics, and evaluation protocol before reporting numbers.
- Justify baselines and include recent target-venue competitors where applicable.
- Present main results first, then ablations, robustness, efficiency, and failure analysis.
- Explain what each table or figure proves.
- Separate empirical findings from speculation.
- Mark missing evidence that should weaken claims.
Rubric
| Dimension | Strong | Weak |
|---|---|---|
| Claim coverage | Every main claim has evidence | Results do not answer claims |
| Baselines | Recent and relevant | Convenient or outdated only |
| Protocol | Datasets/splits/metrics clear | Evaluation setup ambiguous |
| Ablations | Test key method choices | Cosmetic or missing |
| Statistics | Variance/significance when needed | Single number overinterpreted |
| Failure analysis | Honest limits | Only best-case results |
Venue Adaptation
- ICLR/NeurIPS/ICML: include ablations, reproducibility details, and robustness/generalization checks.
- CVPR/ECCV: include strong visual baselines, qualitative examples, and failure cases.
- AAAI: make the experimental story understandable across AI subfields.
- TMI: specify modality, cohort, split, external validation, reader/clinical protocol, and privacy constraints where applicable.
- arXiv: include full experimental detail and public artifacts when possible.
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.
- 10d ago First seen · 103 lines · 0 tokens per session scan A f9703bed6a48
experiments-writer is a skill published in the GitHub repository Haohaha-11/Paper-Writing (3 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 634 tokens. 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 2026-08-31.
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