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 hdu-ailab/EasyResearch --skill experimentgit clone --depth 1 https://github.com/hdu-ailab/EasyResearchWrote 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/hdu-ailab/easyresearch/experiment)<a href="https://agentmods.dev/skills/hdu-ailab/easyresearch/experiment"><img src="https://agentmods.dev/badge/skills/hdu-ailab/easyresearch/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/hdu-ailab/easyresearch/experiment"><img src="https://agentmods.dev/badge/skills/hdu-ailab/easyresearch/experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 221 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00171 | $0.04713 |
| Opus 5.5 | $0.00068 | $0.01885 |
| Sonnet 5.5 | $0.00034 | $0.00943 |
| Haiku 4.5 | $0.00017 | $0.00471 |
Grade A, and why
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 12d 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 — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Workspace
Purpose
Use this skill to constrain research experiment workspaces and enforce a paper-driven experimental process. The goal is to find a model that is either clearly stronger than baselines or more innovative while remaining experimentally defensible.
Most practical model innovations are not invented from nothing. They are discovered by reading papers, identifying what each component is useful for, systematically recombining components from different papers, and validating which combinations actually work.
Do not jump directly to a proposed model. Read papers first, build baselines first, then try component combinations.
In the bundled paper workflow, select exactly one root from the dispatch before
editing: exact-cwd experiments/ for local execution or the Research
Assistant-created and marker-verified exact-cwd experiment_ssh/ mount for SSH
execution. Do not mix or mirror the two roots.
Required Directory Layout
Create or maintain this structure under the user-specified experiment root:
<experiment-root>/
.venv/
src/
external/
baselines/
<baseline_name>/
repo/
.venv/
benchmarks/
<benchmark_name>/
repo/
.venv/
outputs/
results/
logs/
datasets/
experiment-record.md
For local mode, <experiment-root> is experiments/. For SSH mode, it is only
experiment_ssh/, which is the local view of the configured remote project
root. An explicitly supplied existing user layout may replace the local-mode
root, but it never replaces the verified bundled SSH mount.
Directory rules:
.venv/: main experiment environment managed byuv; use it only for this project's own experiment code, routers, models, analysis, plotting, and lightweight adapters.src/: source code written for this project, including data loading, models, training, evaluation, adapters, analysis, and utilities.external/: all external author repositories, official benchmark repositories, and their isolated environments. Do not place cloned third-party repos directly undersrc/.external/baselines/<baseline_name>/repo/: checkout or symlink to the author-maintained baseline repository.external/baselines/<baseline_name>/.venv/: isolateduvenvironment for that baseline only.external/benchmarks/<benchmark_name>/repo/: checkout or symlink to the official benchmark/evaluator repository.external/benchmarks/<benchmark_name>/.venv/: isolateduvenvironment for that benchmark only.outputs/: all raw outputs from experiment scripts, including failed trials, temporary models, trial models, and formal model runs.results/: clean copy of formal experiment outputs only. Use this directory later for paper writing, figures, statistical analysis, and tables.logs/: command logs, runtime logs, error traces, and environment snapshots.datasets/: raw or processed datasets, plus dataset manifests when useful.experiment-record.md: human-readable experiment progress record. Update it after every experiment run.
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.
- 12d ago Changed · +3 lines 9fcc9cd61f0c
- 1mo ago First seen · 406 lines · 171 tokens per session scan A 55c8b9df833c
experiment is a skill published in the GitHub repository hdu-ailab/EasyResearch (14 stars, last pushed today), licensed MIT. It adds 171 tokens to every session and 4,713 once invoked, about $0.0007 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 2026-08-30.
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