cargo-analytics

name	cargo-analytics
description	Download workflow run results, export segment data, and monitor run metrics using the Cargo CLI. Use when the user wants run metrics, error rates, data export, or download results for their Cargo workspace. For billing and credit usage, use the cargo-billing skill instead. For explaining WHY a run failed or a batch has errors, use the cargo-diagnostics skill instead.
version	1.4.2
compatibility	Requires @cargo-ai/cli (npm) and a Cargo account (browser sign-in via --oauth, or an API token)
homepage	https://github.com/getcargohq/cargo-skills
metadata	{"author":"getcargo","openclaw":{"requires":{"bins":["cargo-ai"]},"install":[{"kind":"node","package":"@cargo-ai/cli@latest","bins":["cargo-ai"]}],"homepage":"https://github.com/getcargohq/cargo-skills"}}

Cargo CLI — Analytics

Measurement and export: monitoring run metrics, downloading run and batch results, and exporting segment data.

See references/response-shapes.md for full JSON response structures. See references/troubleshooting.md for common errors and how to fix them. See references/examples/run-analytics.md for run metrics and error monitoring. See references/examples/exports.md for data export and download examples. For billing, usage metrics, and subscription: use the cargo-billing skill.

Scope — measure and export, not explain

This skill answers "what happened" and "give me the data": metrics, counts, downloads, exports. The moment the question becomes "why" — why did this run fail, why is the output wrong or empty, which root cause explains these errors, why is this play so expensive — switch to the cargo-diagnostics skill; its runbooks sequence the raw surfaces into a diagnosis.

The question sounds like…	Load
"What's the error rate?" / "How many runs failed this week?" / "Export the results / segment"	this skill
"Why did this run fail?" / "Run succeeded but the output looks wrong"	cargo-diagnostics → references/run-trace.md
"Why does this batch have errors? Which node keeps failing, and is it one cause or many?"	cargo-diagnostics → references/batch-error-sweep.md
"Why is this play so expensive? Where do the credits go?"	cargo-diagnostics → references/play-optimize-credits.md

The two skills chain naturally: analytics detects (error rate spiked, batch reports failures), diagnostics explains (18 of 20 failures share one root cause), then analytics retrieves the clean results once the cause is fixed and the runs re-executed.

Prerequisites

See ../cargo/references/prerequisites.md for install, login (--oauth / --token), JSON output conventions, and error shapes. Verify the session with cargo-ai whoami before running any of the commands below.

Discover resources first

Most analytics commands require UUIDs. Discover them before querying.

cargo-ai orchestration play list            # all plays (name, workflowUuid)
cargo-ai orchestration tool list            # all tools (name, workflowUuid)
cargo-ai orchestration workflow list        # all workflows (uuid only — no name)
cargo-ai ai agent list                     # all agents (uuid, name)
cargo-ai connection connector list          # all connectors (uuid, name, integrationSlug)
cargo-ai storage model list                # all models (uuid, name, slug)

Quick reference

cargo-ai orchestration run get-metrics --workflow-uuid <uuid>
cargo-ai orchestration run download --workflow-uuid <uuid> --is-finished
cargo-ai orchestration run count --workflow-uuid <uuid> --statuses error
cargo-ai orchestration query execute "SELECT status, count() FROM runs GROUP BY status"
cargo-ai segmentation segment download --model-uuid <uuid> --filter '{"conjonction":"and","groups":[]}'

Picking the right command:

• run get-metrics / run count — workflow-scoped, predefined aggregations. Best when you already have a workflowUuid.
• orchestration query execute — ad-hoc SQL across the entire workspace (runs, batches, spans, records). Best for cross-workflow analytics, per-node breakdowns, and time-series.
• run download / run download-outputs — per-record output retrieval.
• segment download / storage query execute — storage data (Companies, Contacts, …).

Workflow run metrics

Aggregated metrics for workflow runs (success/error rates, credits per node).

# Metrics for a workflow
cargo-ai orchestration run get-metrics --workflow-uuid <uuid>

# Scoped to a release, batch, or date range
cargo-ai orchestration run get-metrics --workflow-uuid <uuid> --release-uuid <uuid>
cargo-ai orchestration run get-metrics --workflow-uuid <uuid> --batch-uuid <uuid>
cargo-ai orchestration run get-metrics --workflow-uuid <uuid> \
  --created-after <start-date> --created-before <end-date>

Run count

Count runs matching specific criteria — useful for monitoring.

cargo-ai orchestration run count --workflow-uuid <uuid> --statuses error
cargo-ai orchestration run count --workflow-uuid <uuid> --is-finished \
  --created-after <start-date> --created-before <end-date>
cargo-ai orchestration run count --workflow-uuid <uuid> --batch-uuid <uuid>

Supports: --statuses, --batch-uuid, --release-uuid, --is-finished, --created-after, --created-before, --record-id, --record-title.

For cross-workflow analytics or shapes that run count doesn't expose (per-node failure breakdowns, p95 durations, error rate over time), use orchestration query execute — see the Ad-hoc execution analytics section.

Ad-hoc execution analytics (orchestration query)

Run SQL against orchestration runtime tables — runs, batches, spans, records — for analytics that the canned metrics commands don't cover. Tables are referenced without a schema prefix; workspace scoping is automatic. See cargo-orchestration/references/examples/queries.md for schemas and limits.

# Error rate across the workspace in the last day
cargo-ai orchestration query execute \
  "SELECT countIf(status='error') / count() AS error_rate FROM runs WHERE created_at > now() - INTERVAL 1 DAY"

# Failed runs per workflow this week
cargo-ai orchestration query execute \
  "SELECT workflow_uuid, count() AS errors FROM runs WHERE status='error' AND created_at > now() - INTERVAL 7 DAY GROUP BY workflow_uuid ORDER BY errors DESC"

# Per-node failure counts (last 24h)
cargo-ai orchestration query execute \
  "SELECT node_slug, count() AS failures FROM spans WHERE execution_status='error' AND execution_started_at > now() - INTERVAL 1 DAY GROUP BY node_slug ORDER BY failures DESC"

# Credit spend by workflow this month
cargo-ai orchestration query execute \
  "SELECT workflow_uuid, sum(credits_used_count) AS credits FROM batches WHERE created_at >= toStartOfMonth(now()) GROUP BY workflow_uuid ORDER BY credits DESC"

Read-only and capped: 30s execution time, 10 000 result rows, 10 000 000 rows scanned. Narrow with a created_at/execution_started_at predicate to stay under the row-scan cap.

Downloading run results

Two distinct commands — pick the right one for the job.

run download — full run records (metadata + per-node runContext)

Returns each run as a JSON object with status, timing, executions, and runContext.<nodeSlug> containing per-node outputs. Best for debugging or when you need the full execution history.

# All finished runs
cargo-ai orchestration run download --workflow-uuid <uuid> --is-finished

# Date range
cargo-ai orchestration run download --workflow-uuid <uuid> \
  --created-after <start-date> --created-before <end-date>

# Specific statuses
cargo-ai orchestration run download --workflow-uuid <uuid> --statuses success,error

# From a specific batch
cargo-ai orchestration run download --workflow-uuid <uuid> --batch-uuid <uuid>

run download-outputs — output of a specific node (CSV/JSON via signed URL)

This is the canonical way to get action results out of the platform. Maps to API POST /v1/orchestration/runs/download-outputs. Returns {"url": "..."} — a signed URL to a CSV (default) or JSON file containing only the output node's data with input/output context. Faster and cheaper than downloading whole run records when you only need the result.

# Required: --workflow-uuid + --output-node-slug
cargo-ai orchestration run download-outputs \
  --workflow-uuid <uuid> \
  --output-node-slug <slug> \
  --format json \
  --is-finished

# Filter by batch + status
cargo-ai orchestration run download-outputs \
  --workflow-uuid <uuid> \
  --output-node-slug <slug> \
  --batch-uuid <uuid> \
  --statuses finished

To find the output-node-slug: cargo-ai orchestration release get <release-uuid> → look at nodes[].slug. The terminal output node is typically named output or end.

Downloading batch results

cargo-ai orchestration batch download --uuid <batch-uuid> --output-node-slug <node-slug>

To find the output-node-slug: run cargo-ai orchestration release get <release-uuid> (get the release UUID from the batch) and look at nodes[].slug.

Handling partial batch failures

A batch with status: "success" can still contain individual run failures. Always inspect the batch for errors before treating results as complete.

Step 1 — Check the batch summary:

cargo-ai orchestration batch get <batch-uuid>
# → .runsCount          = total records submitted
# → .executedRunsCount  = records that reached a terminal state (success or error)
# → .failedRunsCount    = records that errored

Step 2 — Count and download the failed runs:

cargo-ai orchestration run count \
  --workflow-uuid <uuid> \
  --batch-uuid <batch-uuid> \
  --statuses error

cargo-ai orchestration run download \
  --workflow-uuid <uuid> \
  --batch-uuid <batch-uuid> \
  --statuses error

Step 3 — Diagnose. Working out why they failed — grouping failures by root cause, picking exemplar runs, reading runContext — is the cargo-diagnostics skill's job: load ../cargo-diagnostics/references/batch-error-sweep.md and feed it the batch UUID.

Step 4 — Re-run only the failed records:

After the diagnosis and fixing the underlying issue (connector credentials, bad input data, rate limits):

# Extract record IDs from the failed run download, then:
cargo-ai orchestration batch create \
  --workflow-uuid <uuid> \
  --data '{"kind":"recordIds","recordIds":["id1","id2","id3"]}'

Filtering by node output slug:

To download only a specific node's output from a batch (e.g. just the enrichment node, not the full run):

# 1. Get the release UUID from the batch
cargo-ai orchestration batch get <batch-uuid>
# → .releaseUuid

# 2. Find the node slug
cargo-ai orchestration release get <release-uuid>
# → nodes[].slug

# 3. Download that node's output
cargo-ai orchestration batch download \
  --uuid <batch-uuid> \
  --output-node-slug <node-slug>

Segment data export

Filter JSON uses conjonction (not conjunction) — this is intentional. See the cargo-orchestration skill's references/filter-syntax.md for the full filter syntax.

# Full export (all records)
cargo-ai segmentation segment download \
  --model-uuid <uuid> \
  --filter '{"conjonction":"and","groups":[]}'

# With sorting and limit
cargo-ai segmentation segment download \
  --model-uuid <uuid> \
  --filter '{"conjonction":"and","groups":[]}' \
  --sort '[{"columnSlug":"created_at","kind":"desc"}]' \
  --limit 1000

IMPORTANT: segment download requires --model-uuid, not --segment-uuid. Get the modelUuid from segment list.

For live paginated queries with enrichment, use segmentation segment fetch from the cargo-orchestration skill.

Help

Every command supports --help:

cargo-ai billing usage get-metrics --help
cargo-ai orchestration run download --help
cargo-ai segmentation segment download --help