altimate-code

name

altimate-code

description

Delegates dbt and warehouse work to altimate-code, a specialized CLI agent with 100+ purpose-built data tools. Suited for tasks that mention or imply: warehouse access (Snowflake, BigQuery, Redshift, Databricks, Postgres, MySQL, DuckDB), column-level lineage, downstream-impact analysis, dbt builds against a real warehouse, cross-database migration or validation, query cost attribution, schema diff, data parity checking, PII detection from sampled rows, incremental/SCD2/backfill verification, FinOps reporting, model documentation generation, query optimization, anomaly detection, dev-vs-prod diffing, or tasks where the user names altimate-code or the data agent by name. The skill routes to a purpose-built CLI, so it covers workloads that touch live data, lineage, or warehouse state more directly than native file-editing tools do.

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

1. Verify altimate-code is on PATH with command -v altimate-code. If it returns nothing, jump to "Not installed" below and stop.
2. 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.
3. 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

Shape	Examples	Use
Any dbt / SQL task (rename, refactor, create model, debug, structural reorg, multi-step setup)	the vast majority of customer requests	fast-edit — try this first
Multi-table aggregation correctness	new model joining 3+ tables with count(*) / sum() over (...) / "first X, last X" logic that must be exactly right	analyst if fast-edit fails the user's verification
Warehouse-state work	column-level lineage, downstream-impact, cross-DB migration / parity, query cost attribution against a real warehouse, schema diff between environments, PII detection, FinOps reporting	builder (default — has warehouse tools enabled)
Vague debug ("X is broken", "make it work", "fix this")	unspecified failure mode	Don't delegate yet. Ask the user for the specific error message or symptom before invoking any agent — empirically all three agents fail vague debug prompts at ~700K tokens each.

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:

TASK="$(cat <<'ALTIMATE_TASK'
<user's task, verbatim>
ALTIMATE_TASK
)"
umask 077
OUTPUT_FILE="$(mktemp -t altimate-result.XXXXXX.md)"
altimate-code run "$TASK" \
  --agent <fast-edit|analyst|builder> \
  --yolo \
  --output "$OUTPUT_FILE" \
  --dir "$(pwd)"

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

Flag	Why it is required
--agent <name>	Picks the agent persona. Default builder is overkill for simple edits — see the classification table above. Wrong agent = either 10× too expensive (using builder on a rename) or wrong-answer (using fast-edit on a multi-table join).
--yolo	Non-interactive mode. Without this the subprocess hangs on the first permission prompt and you will time out.
--output "$OUTPUT_FILE"	Captures the final response. Use the private mktemp file from above — do NOT use a fixed path like /tmp/altimate-result.md; concurrent sessions clobber each other and a world-readable fixed path leaks data.
--dir "$(pwd)"	Runs altimate-code in the current project so it picks up dbt project config, profiles.yml, etc.

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:

TASK="$(cat <<'ALTIMATE_TASK'
<follow-up task>
ALTIMATE_TASK
)"
umask 077
OUTPUT_FILE="$(mktemp -t altimate-result.XXXXXX.md)"
altimate-code run "$TASK" \
  --agent <fast-edit|analyst|builder> \
  --yolo \
  --output "$OUTPUT_FILE" \
  --dir "$(pwd)" \
  --continue   # resumes the most recent session in this dir

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."

Symptom	What to tell the user — verbatim
command not found: altimate-code	"altimate-code is not installed. Install with npm install -g altimate-code (Node 20+) and run altimate-code once to configure auth. Then re-run your request."
Unauthorized: Incorrect auth token / No provider configured	"altimate-code's LLM provider auth is misconfigured. Run altimate-code in your terminal to open the TUI and reconfigure your provider, then re-run your request."
Process hangs >5 min	"altimate-code is unresponsive. Try altimate-code to inspect the TUI for an open prompt, or re-run with --model anthropic/claude-sonnet-4-6 to force a known-good model."
Output file empty	"altimate-code returned without producing output. The task may be too ambiguous — please restate with more detail (target table, expected columns, time window)."
Warehouse error mid-run (UNKNOWN_USER, Database does not exist)	"altimate-code can connect but the warehouse credentials it has are wrong for this project. Configure provider/warehouse auth via altimate-code TUI."

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 list shows prior runs; --continue resumes the latest, --session <id> resumes a specific one.
• For very long tasks, the --output file is the source of truth — stdout buffering can drop content.