train

A training helper that reads a style guide, review checklist, coding standard, or similar document and turns its contents into rules, standards, and glossary entries for an AI teammate.

In plain words
What is it for?
Use it to teach an AI teammate naming rules, formatting requirements, prohibited patterns, review criteria, architecture standards, and internal terms from a file, URL, or pasted text.
Why use it?
It saves you from repeatedly explaining team conventions and makes the extracted guidance ready for confirmation and storage.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/altimateai/altimate-code/train
Any agent
npx skills add AltimateAI/altimate-code --skill train
Clone the repo
git clone --depth 1 https://github.com/AltimateAI/altimate-code

Made for: Claude Code, Codex.

Per session 15 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 469 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 9e4073de9ad3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

.opencode/skills/train/SKILL.md · 52 lines

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

  1. 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
  2. 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
  3. 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")
  4. 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
  5. Save via training_save: Save each item using the training_save tool. 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: project unless 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)
Changes

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.

  1. 2d ago First seen · 52 lines · 15 tokens per session scan A 9e4073de9ad3

Subscribe to this mod's changes

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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