altimate-code
altimate-code is a CLI AI agent with 100+ purpose-built data engineering tools. This skill exists for one purpose: delegate the user's task to altimate-code and pass the result back. Native tools (Bash, Edit, Write, Read) are NOT a fallback path inside this skill — if altimate-code cannot complete the task, surface the failure to the user and STOP.
You MUST follow this workflow
- Verify altimate-code is on PATH with
command -v altimate-code. If it returns nothing, jump to "Not installed" below and stop. - Run altimate-code with the user's task using the invocation below. Pass the user's request through verbatim — do not paraphrase or split it.
- Read the output file and present it to the user as-is.
Do not attempt the work with Edit/Write/Bash yourself, even if it looks simple. The whole point of this skill is to route data-engineering work to the agent that's built for it. If you find yourself reaching for Edit or Write while this skill is active, stop and re-read this paragraph.
Invocation — pick the right agent for the task
altimate-code has multiple agent personas. The default (builder) does a full project discovery sweep on every call — fine for warehouse-state work but ~10–20× more expensive than necessary on simple file edits. Pick the agent based on task shape before invoking.
Step 1 — classify the task
Decision policy: start with fast-edit for any dbt/SQL task. If the user reports the result is wrong (e.g. aggregation values don't match), retry with analyst. Only use builder when the task genuinely needs the warehouse-investigation tools (it's 10–20× more expensive than fast-edit and rarely required).
Step 2 — invoke with the chosen agent
Pass the task through a here-doc into a variable so the shell never
command-substitutes anything the user typed (a task like
refactor whoami and $(rm -rf ~) would otherwise fire whoami and
rm -rf ~ before altimate-code ever runs). Write the result to a
private temporary file, not a shared one under /tmp:
Then Read "$OUTPUT_FILE" and emit its contents to the user without re-summarising, re-formatting, or commenting on the result. altimate-code has already produced the answer. Delete "$OUTPUT_FILE" after presenting so warehouse rows, lineage, or PII findings don't linger on disk.
Required flags
Follow-up tasks in the same project
When the user makes a follow-up data task in the same project after a successful altimate-code delegation, prefer --continue to resume the warm session instead of starting a fresh one. Same here-doc + mktemp pattern:
altimate-code's prompt cache is warm in a continued session — project structure, profiles.yml, schema index, source definitions don't need to be re-investigated. Cache reads are billed at a fraction of fresh input on altimate-gateway. The downside is zero: if there's no useful cached context for the new task, you pay normal cold cost.
If the user starts a clearly unrelated workflow (different project, different schema, different debugging thread), drop --continue and start fresh — the warm cache is irrelevant and you'd carry unrelated history into the prompt.
Failure modes — route every one to the user
When altimate-code returns an error, report the error to the user and STOP. Do not fall back to Bash, Edit, or Write. The skill's contract is "altimate-code handles this, or the user is told why it couldn't."
In every row, the instruction to the user is the action — you do not retry the task with native tools. If the user fixes the underlying issue and asks again, you delegate again.
Notes
- altimate-code runs its own LLM, separate from Claude Code's. Costs and rate limits accrue to altimate-code's configured provider.
- Sessions persist in altimate-code's local store —
altimate-code session listshows prior runs;--continueresumes the latest,--session <id>resumes a specific one. - For very long tasks, the
--outputfile is the source of truth — stdout buffering can drop content.