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 skills/simple-agent-lab/autotrainess/datanpx skills add simple-agent-lab/AutoTrainess --skill datagit clone --depth 1 https://github.com/simple-agent-lab/AutoTrainessWhat 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 | $0.00008 | $0.00288 |
| Opus 5 | $0.00004 | $0.00144 |
| Sonnet 5 | $0.00002 | $0.00058 |
| Haiku 4.5 | $0.00001 | $0.00029 |
Grade A, and why
data 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
data
Purpose
Prepare training data that addresses real problems exposed by previous training or evaluation, aligns with the benchmark evaluation interface, and is ready for downstream training.
Core principles
- Drive all data work from concrete problems found in previous training or evaluation.
- Prioritize alignment with the benchmark's real evaluation interface over broad or generic data expansion.
- Prefer the smallest effective dataset that addresses the current problems.
- Keep data sources, transformations, and synthetic generation traceable.
- Avoid benchmark leakage and contaminated data.
Workflow
- Read shared/conventions.md for shared rules.
- Run selection/stage.md to identify target data needs and initial source directions.
- Run construction/stage.md to turn those needs and directions into a benchmark-aligned training dataset.
- Run validation/stage.md for data validation before training.
- If validation finds construction issues, return to construction. If validation finds target-need or source-direction issues, return to selection.
Required outputs
- A final training dataset ready for downstream training.
- A concise dataset description covering target problems, data sources, sample format, known limitations, and validation status.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 30 lines · 8 tokens per session scan A 6a3b68582d4c
data is a skill published in the GitHub repository simple-agent-lab/AutoTrainess (21 stars, last pushed 29d ago), licensed MIT. It adds 8 tokens to every session and 288 once invoked, about $0.0000 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
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
publish-registry
Publish @agentos-software/ registry packages from AgentOS. Use whenever the user asks to publish or release registry software/agent packages.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
lazarus-group
Adversary-emulation profile for Lazarus Group (G0032, aka Hidden Cobra / Diamond Sleet / Labyrinth Chollima), a North Korean RGB-linked actor conducting espionage, destructive, and financially motivated operations.
sidewinder-rattlesnake
Adversary-emulation profile for SideWinder (G0121 / Rattlesnake / T-APT-04 / Razor Tiger), India's suspected state-sponsored cyber-espionage actor.