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Run the full CSV bulk-import workflow from the command line: upload and analyze the file, create the job with a column mapping, poll until the job reaches a terminal state, and pull the rejected rows if any landed. The recipe composes every leaf in path import.
Prerequisites: authenticated CLI, a CSV file with a header row, and a channel ID to import the contacts into. Find a channel ID with path channel list.

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Steps

1

Analyze the CSV

talkvalue path import analyze --file ./contacts.csv --json
Uploads the file, returns a fileKey, the headers, a preview, and the server’s suggested column mapping. Note the fileKey and the column indices. You need both for the next step.
2

Capture the fileKey

FILE_KEY=$(talkvalue path import analyze --file ./contacts.csv --json | jq -r '.data.fileKey')
echo "$FILE_KEY"
Saves the key in a shell variable so the rest of the script can reference it without re-uploading.
3

Create the import job

SOURCE_CHANNEL_ID=7

JOB_ID=$(
  talkvalue path import create \
    --file-key "$FILE_KEY" \
    --source-id "$SOURCE_CHANNEL_ID" \
    --mode UPDATE \
    --mapping 0:EMAIL \
    --mapping 1:FIRST_NAME \
    --mapping 2:LAST_NAME \
    --json \
  | jq -r '.data.importJobId'
)
echo "Job: $JOB_ID"
Returns immediately with the importJobId. The job runs in the background. Your terminal is free for the polling loop below.--mode UPDATE overwrites fields on existing contacts matched by email. Switch to --mode SKIP if you want to leave existing records untouched and treat collisions as duplicates.
4

Poll until the job reaches a terminal state

while :; do
  STATUS=$(talkvalue path import get "$JOB_ID" --json | jq -r '.data.status')
  echo "  status: $STATUS"
  case "$STATUS" in
    COMPLETED|PARTIAL_SUCCESS|FAILED) break ;;
  esac
  sleep 5
done
The job moves through PENDING → RUNNING → one of COMPLETED, PARTIAL_SUCCESS, or FAILED. The loop exits on any terminal state. Tighten or relax sleep based on import size.
5

Export rejected rows when there are failures

FAILED=$(talkvalue path import get "$JOB_ID" --json | jq -r '.data.failedCount')
if [ "$FAILED" -gt 0 ]; then
  talkvalue path import failed-export "$JOB_ID" > "failed-$JOB_ID.csv"
  echo "$FAILED rejected row(s) written to failed-$JOB_ID.csv"
else
  echo "No failures."
fi
Pulls the full rejected-row CSV only when the job had failures. The output header is rowNum,errorCode,errorMessage,rawValue. Fix the rows, re-run the import on the corrected file, and the failure count drops.

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Variants

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Treat duplicates as silent skips

talkvalue path import create \
  --file-key "$FILE_KEY" \
  --source-id "$SOURCE_CHANNEL_ID" \
  --mode SKIP \
  --mapping 0:EMAIL --mapping 1:NAME --mapping 2:COMPANY_NAME \
  --json
SKIP is the right mode for a weekly registrant export when you want every new email but you don’t want to overwrite manual edits made in the dashboard.

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Group failures by error code before fixing

talkvalue path import failed-export "$JOB_ID" \
  | tail -n +2 \
  | awk -F',' '{print $2}' \
  | sort | uniq -c | sort -rn
Counts failures by errorCode so you can prioritize the fix that unlocks the most rows.

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One-shot script

#!/usr/bin/env bash
set -euo pipefail

FILE="$1"
SOURCE_CHANNEL_ID="$2"

FILE_KEY=$(talkvalue path import analyze --file "$FILE" --json | jq -r '.data.fileKey')
JOB_ID=$(
  talkvalue path import create \
    --file-key "$FILE_KEY" \
    --source-id "$SOURCE_CHANNEL_ID" \
    --mode UPDATE \
    --mapping 0:EMAIL --mapping 1:FIRST_NAME --mapping 2:LAST_NAME \
    --json | jq -r '.data.importJobId'
)

while :; do
  STATUS=$(talkvalue path import get "$JOB_ID" --json | jq -r '.data.status')
  case "$STATUS" in COMPLETED|PARTIAL_SUCCESS|FAILED) break ;; esac
  sleep 5
done

FAILED=$(talkvalue path import get "$JOB_ID" --json | jq -r '.data.failedCount')
[ "$FAILED" -gt 0 ] && talkvalue path import failed-export "$JOB_ID" > "failed-$JOB_ID.csv"
echo "Final status: $STATUS, $FAILED failure(s)"
Drop into import-csv.sh, then run ./import-csv.sh ./contacts.csv 7.

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Tips

  • Valid mapping target fields: EMAIL, FIRST_NAME, LAST_NAME, NAME, PHONE, JOB_TITLE, COMPANY_NAME, ADDRESS, LINKEDIN_URL, X_URL, JOINED_AT. The full reference lives at Column mapping.
  • Re-importing failed-$JOB_ID.csv after corrections clears the failure count. It does not double-import the successful rows from the first run.
  • Each import create queues a separate job. If you re-run with the same fileKey you get a second job ID, not an overwrite of the first.
  • Need to inspect a job later? Every import is listed by import list and individual jobs by import get <id>.

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See also