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 commands/niels-emmer/myace/experimentgit clone --depth 1 https://github.com/niels-emmer/myaceWrote 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/commands/niels-emmer/myace/experiment)<a href="https://agentmods.dev/commands/niels-emmer/myace/experiment"><img src="https://agentmods.dev/badge/commands/niels-emmer/myace/experiment.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 5.1 | $0.00021 | $0.00191 |
| Opus 5 | $0.00010 | $0.00096 |
| Sonnet 5 | $0.00004 | $0.00038 |
| Haiku 4.5 | $0.00002 | $0.00019 |
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 2d 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.
What it actually says
- Create experiment notebook with a descriptive name following the run naming convention.
- Initialize experiment tracker connection and log the environment (git hash, dependency versions).
- Set all random seeds and log them.
- Define success criteria: target metric(s), baseline to beat, minimum improvement threshold.
- Load and validate data using the data-validation skill's checklist.
- Implement the initial approach, logging all parameters before training.
- Run and log results. Compare against baseline.
- Write a short summary of what was tried, what worked, and what didn't.
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.
- 2d ago First seen · 15 lines · 21 tokens per session scan A 754441068745
experiment is a command published in the GitHub repository niels-emmer/myace (1 stars, last pushed 4d ago), licensed MIT. It adds 21 tokens to every session and 191 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 2026-09-03.
Other commands, from other repositories
infer
Command "infer" from xiaobei930/cc-best, covering /infer - 模型推理, 适用场景, 通用推理流程, 1. 环境检查 and gpu 检查.
critique
Audit DESIGN.md against the actual code. Writes DESIGN.md.critique.md adjacent to DESIGN.md with five audit passes structural / drift / accessibility / completeness / consistency. Each finding has a stable id and a /mddesign:fix suggestion.
doctor
Deep diagnostic. Checks every MDDesign dependency, verifies hooks fire, validates the lint toolchain, tests scratch directory write, and prints a fix-it for every failure. Use when something is broken or before publishing to confirm a clean install.
analyst-init
Before executing this command, CONFIRM these files exist and are readable.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.