| 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
- 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
| 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
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 the host agent'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.
