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 LiXin97/agora-lab --skill student-run-experimentgit clone --depth 1 https://github.com/LiXin97/agora-labWrote 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/lixin97/agora-lab/student-run-experiment)<a href="https://agentmods.dev/skills/lixin97/agora-lab/student-run-experiment"><img src="https://agentmods.dev/badge/skills/lixin97/agora-lab/student-run-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/lixin97/agora-lab/student-run-experiment"><img src="https://agentmods.dev/badge/skills/lixin97/agora-lab/student-run-experiment.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 | $0.00018 | $0.00662 |
| Opus 5 | $0.00009 | $0.00331 |
| Sonnet 5 | $0.00004 | $0.00132 |
| Haiku 4.5 | $0.00002 | $0.00066 |
Grade A, and why
student-run-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 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Student Run Experiment
Student-specific extensions
- Record artifact destinations before the run starts.
- Separate observations from interpretation in the results write-up.
- Summarize failures, not just wins.
- Update the Research task board task with the real artifact list after publishing results.
Purpose
Execute an experiment according to a published experiment plan, ensuring reproducibility and proper logging.
Workflow
- Read plan: Load the experiment plan from your canonical shared artifact directory:
{artifact_dir}/{student}/experiment_plan_{id}.md - Set up environment: Install dependencies, configure GPU, set seeds
- Implement: Write code in
workspace/ - Execute: Run training/evaluation with full logging
- Collect results: Gather metrics, generate figures
- Publish: Write results to your canonical shared artifact directory:
{artifact_dir}/{your-name}/experiment_results_{id}.md - Export reviewable evidence: Copy logs, figures, and any code/config snapshot needed for review into
{artifact_dir}/{your-name}/support/{id}/ - Update the Research task board:
bash ../../scripts/lab-kanban.sh -caller <your-name> -submit -id <ID> -artifacts "..."
Reproducibility Checklist
Before running, verify:
- Random seeds are fixed (Python, NumPy, PyTorch/TF)
- Dependencies are locked (
requirements.txtorenvironment.yml) - Hyperparameters are logged (config file or command-line args)
- Data loading is deterministic
- Git hash of code is recorded
- GPU/hardware info is logged
Results Format
# Experiment Results: {title}
## Setup
- **Plan**: {artifact_dir}/{student}/experiment_plan_{id}.md
- **Code snapshot**: {artifact_dir}/{your-name}/support/{id}/code/
- **Environment**: Python X.Y, PyTorch X.Y, CUDA X.Y
- **Hardware**: GPU type, count
- **Seeds**: [42, 123, 456]
- **Git hash**: ...
## Results
### Main Results
| Method | Metric1 | Metric2 | Metric3 |
|---|---|---|---|
### Ablation Results
| Variant | Metric1 | Metric2 |
|---|---|---|
## Analysis
Key observations and interpretation.
## Figures
- `figures/main_comparison.png`
- `figures/ablation.png`
## Logs
- `{artifact_dir}/{your-name}/support/{id}/run_001.log`
## Issues
Any problems encountered during execution.
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 · 82 lines · 18 tokens per session scan A 037ddc798ef5
student-run-experiment is a skill published in the GitHub repository LiXin97/agora-lab (49 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 662 once invoked, about $0.0001 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.
Other skills, from other repositories
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.
gh-file-issue
Use when filing a new Codewhale GitHub issue: turn a bug or idea into a well-formed, actionable issue with repro, acceptance criteria, labels, and milestone.
gh-treasure-hunt
Hunt the issue/PR queue for highest value-over-risk wins: clean focused community PRs, already-implemented issues to close, safe quick-fixes.
recording
Capture screen recordings and screenshots on any registered computer (macOS, Windows, Linux, HarmonyOS) and manage the recording library.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
plan
Turn a sufficiently understood task into an ordered implementation plan with dependencies and verification. Orchestrate Codewhale’s native plan state; do not build a parallel planner.