Borrowing it
Nothing to install: this file belongs to AltimateAI/altimate-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AltimateAI/altimate-code/main/.opencode/skills/train/SKILL.mdgit clone --depth 1 https://github.com/AltimateAI/altimate-codeWrote 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/altimateai/altimate-code/train)<a href="https://agentmods.dev/skills/altimateai/altimate-code/train"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/train/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/altimateai/altimate-code/train"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/train.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.00469 |
| Opus 5.5 | $0.00006 | $0.00188 |
| Sonnet 5.5 | $0.00003 | $0.00094 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
train 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 1mo 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
Train
Purpose
Learn team standards and conventions from a document (style guide, review checklist, coding standards, etc.). Extracts actionable rules and saves them as training.
Workflow
-
Get the document: The user provides either:
- A file reference:
@docs/sql-style-guide.md - A URL: The full URL to fetch (use webfetch tool)
- Inline text: Pasted directly in the chat
- A file reference:
-
Read and analyze: Parse the document and extract:
- Specific, enforceable rules (naming, formatting, prohibited patterns)
- Review criteria and checklists
- Glossary terms and definitions
- Architectural standards
-
Categorize: Group findings by training kind:
rule— Specific do/don't rules (e.g., "Never use SELECT *")standard— Broader conventions (e.g., "SQL style guide compliance")glossary— Term definitions (e.g., "ARR = Annual Recurring Revenue")
-
Present summary: Show the user what you extracted:
- Number of rules, standards, and glossary terms found
- Preview of each item
- Ask for confirmation before saving
-
Save via training_save: Save each item using the
training_savetool. For documents with many rules, consolidate related rules into logical groups (e.g., "sql-naming-rules" with 5 rules, rather than 5 separate entries).
Important Guidelines
- Only extract ACTIONABLE items. Skip vague guidance like "write clean code."
- Consolidate related rules into single training entries to avoid clutter.
- Preserve the original wording when it's specific and clear.
- If the document is too large, focus on the most impactful rules.
- Always use
scope: projectunless the user specifies global. - Do NOT make any extra LLM calls — analysis happens in the normal conversation flow.
Usage Examples
/train @docs/sql-style-guide.md
/train https://wiki.company.com/data-team/review-checklist
/train (then paste content inline)
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.
- 1mo ago First seen · 52 lines · 15 tokens per session scan A 9e4073de9ad3
train is a skill published in the GitHub repository AltimateAI/altimate-code (813 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 469 once invoked, about $0.0001 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.
Other skills, from other repositories
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
aeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard…
geopandas
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving…
hdf5-pde-data-loading
Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data…
markitdown
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
study-plan
A long-term study planner that divides learning into stages and adapts the schedule using practice results. It can also plan focused practice sessions for a specific number of questions or flashcards.