Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/readdle/spark-cli-skills --skill spark-recipe-team-workload- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads spark-recipe-team-workload automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:alice@co.com"
spark emails --filter "assigned_to:bob@co.com"
spark emails --filter "assigned_to:carol@co.com"
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 shared@co.com: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
teamcommand's assignment summary is a quick snapshot; the per-memberemailsqueries give the full picture. - Stale assignments (older than 7-14 days) are worth flagging - they may be stuck or forgotten.
assigned_by:meis 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-statusfor 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.
FAQ
What does the spark-recipe-team-workload skill do?
Audit team assignment distribution: per-member loads, delegated items, unassigned work, and workload imbalances.
How do I install spark-recipe-team-workload?
Run npx skills add https://github.com/readdle/spark-cli-skills --skill spark-recipe-team-workload in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/spark-recipe-team-workload/ (personal) or .claude/skills/spark-recipe-team-workload/ (project).
Which AI agents can use spark-recipe-team-workload?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is spark-recipe-team-workload safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains spark-recipe-team-workload?
It is published by readdle in readdle/spark-cli-skills. It has 323 recorded installs.
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