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/altimateai/altimate-code/trainnpx skills add AltimateAI/altimate-code --skill traingit clone --depth 1 https://github.com/AltimateAI/altimate-codeWhat 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 | $0.00015 | $0.00469 |
| Opus 5 | $0.00008 | $0.00234 |
| Sonnet 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 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
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.
- 2d ago First seen · 52 lines · 15 tokens per session scan A 9e4073de9ad3
train is a skill published in the GitHub repository AltimateAI/altimate-code (803 stars, last pushed 2d ago), licensed MIT. It adds 15 tokens to every session and 469 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.
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