altimate-code

altimate-code

altimate-code is a CLI AI agent that ships with native data engineering tools. This skill delegates work to it via its non-interactive run mode and presents the result back to the user.

Prerequisite Check — ALWAYS DO THIS FIRST

Before invoking altimate-code, verify it is installed and on PATH:

command -v altimate-code

If the command returns nothing (exit code 1), STOP and tell the user this exact message — do not proceed:

altimate-code is not installed. Install it with:

npm install -g altimate-code

Requires Node.js 20+. Docs: https://docs.altimate.sh · Source: https://github.com/AltimateAI/altimate-code · npm: https://www.npmjs.com/package/altimate-code

After installing, run altimate-code once to configure it — this launches the TUI where you set up your LLM provider auth and warehouse connections. Then re-run your request and I'll delegate it.

Do not attempt to install altimate-code on the user's behalf — they may want a specific version, a different package manager (e.g. pnpm/yarn global), or to opt out entirely. Surface the command and let them decide.

If command -v fails but the user says it is installed, suggest checking npm bin -g is on PATH, or running npm config get prefix to find the global install location.

How to Invoke

altimate-code run is non-interactive — it takes a message, executes the task, prints the final result to stdout, and exits.

Minimal invocation:

altimate-code run "<task description>" --yolo

Recommended invocation — captures the final response to a file and runs in the right directory:

altimate-code run "<task description>" \
  --yolo \
  --output /tmp/altimate-result.md \
  --dir "$(pwd)"

Then read /tmp/altimate-result.md and pass it straight back to the user.

Key flags

Flag	When to use
--yolo	Required for non-interactive — auto-approves tool calls. Without this it hangs on the first permission prompt.
--output <path>	Write the final assistant response to a file. Use .md or .txt.
--dir <path>	Run the agent in a specific directory (e.g. a dbt project root). Defaults to cwd.
--model provider/model	Override the model. Useful for fast/cheap exploration.
--format json	Emit raw JSON events instead of formatted output. Use only when post-processing programmatically.
--continue / --session <id>	Continue a previous altimate-code session.

Example invocations

Find expensive queries in Snowflake:

altimate-code run "Find the top 10 most expensive queries from the last 7 days in Snowflake and explain why each is slow." \
  --yolo --output /tmp/expensive.md

Generate column-level lineage for a dbt model:

altimate-code run "Show column-level lineage for the dim_customers model, including upstream sources and downstream consumers." \
  --yolo --dir "$(pwd)" --output /tmp/lineage.md

Profile a table:

altimate-code run "Profile the events table — row count, null distribution per column, cardinality, and top 5 values for low-cardinality columns." \
  --yolo --output /tmp/profile.md

Presenting the Result

Read the output file with the Read tool and pass the content through to the user as-is. Do not re-summarize, re-format, or interpret — altimate-code has already produced the answer.

Failure Modes

Symptom	Likely cause	Fix
altimate-code: command not found	Not installed or not on PATH	Run npm install -g altimate-code (Node 20+). If installed but not found, check npm bin -g is on PATH. See https://docs.altimate.sh
Hangs after starting	Missing --yolo, waiting on a permission prompt	Re-run with --yolo
Output is empty	Task too vague, agent gave up	Re-run with a more specific prompt
"No provider configured"	LLM provider creds missing	Run altimate-code providers to set up auth
Warehouse errors mid-run	DB credentials not configured for altimate-code	Configure provider/warehouse auth in ~/.config/opencode/ or via env vars

Notes

• altimate-code runs its own LLM, separate from Claude Code's. Cost and rate limits accrue against altimate-code's configured provider, not Claude Code's.
• Sessions persist in altimate-code's local store — use altimate-code session list to find prior runs and --continue to resume.
• For long-running tasks, prefer --output <file> over scraping stdout.