spark-recipe-shared-inbox-status

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Translated

Review shared inbox health: open vs. done items, unassigned work, and per-member assignment breakdown.

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npx skill4agent add readdle/spark-cli-skills spark-recipe-shared-inbox-status

Tags

Translated version includes tags in frontmatter
shared-inbox-monitoringspark-cliteam-assignment-trackinginbox-healthproductivity-tools

SKILL.md Content

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Recipe: Shared Inbox Status

Review the health of one or more shared inboxes - open items, completed work, unassigned emails, and how assignments are distributed across team members.
Prerequisite: Read the 
use-spark
base skill for command reference and filter syntax.
Access level required: read-only.

Steps

Step 1: Discover shared inboxes

bash
spark accounts
Note each shared inbox address and the team it belongs to.

Step 2: Check open items

For each shared inbox:
bash
spark emails shared@co.com:Inbox --filter "is:shared_inbox_open"
These are active items that still need attention.

Step 3: Check unassigned items

bash
spark emails shared@co.com:Inbox --filter "assigned_to:unassigned"
Unassigned items are falling through the cracks - no one owns them yet.

Step 4: Review per-member assignments

bash
spark team "Team Name"
The assignment summary shows how work is distributed. For a deeper look at a specific member's load:
bash
spark emails shared@co.com:Inbox --filter "assigned_to:alice@co.com"
spark emails shared@co.com:Inbox --filter "assigned_to:bob@co.com"

Step 5: Check completed items

bash
spark emails shared@co.com:Inbox --filter "is:shared_inbox_done"
Review recently completed items for a sense of throughput.

Step 6: Present the status

Report per shared inbox:
  • Open: N items awaiting action
  • Unassigned: M items with no owner
  • Per member: assignment counts (flag anyone with significantly more or fewer)
  • Done: K items completed recently
If there are multiple shared inboxes, present each separately, then a combined summary.

Tips

  • Run this daily for active shared inboxes, weekly for quieter ones.
  • Unassigned items are the most actionable finding - they represent work that nobody is looking at.
  • Combine per-member counts with 
    recipe-team-workload
    for a fuller picture of who has bandwidth.
  • If a shared inbox has high open count but low unassigned count, work is distributed but not being closed - look for stale assignments.
  • Use 
    spark thread <id>
    to read any item that looks stale or unusual.
  • For teams with multiple shared inboxes, the total unassigned count across all inboxes is the key metric.