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spark-recipe-team-workload

spark-recipe-team-workload

github.com/readdleProductivity154324 installsUpdated 6d ago
Repository

About spark-recipe-team-workload

A Spark skill recipe for auditing how work is distributed across a team. It reviews member assignments, delegated and unassigned items, and shared-inbox open versus done status to help surface imbalances.

Key features

  1. 01Review team assignment overview
  2. 02Inspect per-member assignments
  3. 03Track delegated and unassigned items
  4. 04Compare shared-inbox open and done

Use cases

  1. 01Find overloaded or underassigned teammates
  2. 02Identify stale assignments and unowned work
  3. 03Review shared-inbox workload regularly

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/readdle/spark-cli-skills --skill spark-recipe-team-workload

After installing, type/spark-recipe-team-workloadin Claude Code to confirm it loaded.

Details

From the project README

Recipe: Team Workload

Deep audit of how work is distributed across a team. Surfaces per-member assignment loads, items you've delegated, unassigned work, and imbalances.

Prerequisite: Read the use-spark base skill for command reference and filter syntax.

Access level required: read-only.

Steps

Step 1: Get the team overview
spark team "Team Name"

Note the member list, shared inboxes, and the assignment summary. This gives you the high-level distribution.

Step 2: Review per-member assignments

For each team member, pull their current assignments:

spark emails --filter "assigned_to:[email protected]"
spark emails --filter "assigned_to:[email protected]"
spark emails --filter "assigned_to:[email protected]"

Note the count and scan subjects/dates to spot stale items (old assignments that may be stuck).

Step 3: Check what you've delegated
spark emails --filter "assigned_by:me"

These are items you assigned to others. Cross-reference with per-member lists to see which are still open.

Step 4: Find unassigned work
spark emails --filter "assigned_to:unassigned"

If the team uses shared inboxes, also check each one:

spark emails [email protected]:Inbox --filter "assigned_to:unassigned"
Step 5: Check open vs. done in shared inboxes
spark emails --filter "is:shared_inbox_open"
spark emails --filter "is:shared_inbox_done"

The ratio of open to done gives a sense of whether the team is keeping up.

Step 6: Present the workload report

Summarize:

  • Per member: assignment count and any notably old items
  • Imbalances: flag members with significantly more or fewer assignments than average
  • Delegated by you: N items still open, any that look overdue
  • Unassigned: M items with no owner
  • Throughput: open vs. done ratio across shared inboxes

Tips

  • Run this weekly to catch imbalances before they become problems.
  • The team command's assignment summary is a quick snapshot; the per-member emails queries give the full picture.
  • Stale assignments (older than 7-14 days) are worth flagging - they may be stuck or forgotten.
  • assigned_by:me is useful for managers and leads who delegate frequently.
  • Compare this week's numbers to last week's (if you track them) to spot trends.
  • Combine with recipe-shared-inbox-status for a shared-inbox-focused view, or use this recipe for the broader team picture.
  • For large teams, focus on outliers rather than reporting every member - highlight who's overloaded and who has bandwidth.

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