Agent patterns
Format SPAWN REQUEST messages to launch parallel agents, generate structured agent status reports, and define communication protocols within the sprint system. Use when the user needs to coordinate multiple agents, format spawn requests, produce agent reports, or establish inter-agent communication patterns.From its SKILL.md
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SKILL.md
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Agent Patterns
Coordinate multi-agent workflows using structured communication protocols. Covers spawn requests, status reports, and inter-agent messaging within the sprint system.
Spawn Request Format
When launching a parallel agent, use this structure:
## SPAWN REQUEST
- **Agent ID**: <unique-id>
- **Role**: <role-name>
- **Task**: <one-line description>
- **Context**: <relevant files or state>
- **Expected Output**: <deliverable format>
- **Timeout**: <max duration>
Agent Report Format
Each agent reports back using:
## AGENT REPORT
- **Agent ID**: <id>
- **Status**: completed | failed | blocked
- **Summary**: <one-line result>
- **Output**: <deliverable or file path>
- **Issues**: <blockers or warnings, if any>
- **Duration**: <time taken>
Coordination Workflow
- Define tasks — break work into independent units suitable for parallel execution
- Spawn agents — send SPAWN REQUEST for each task with clear context
- Monitor status — track agent reports as they complete
- Handle failures — retry failed agents or reassign blocked tasks
- Merge results — combine agent outputs into final deliverable, resolving conflicts
Communication Rules
- Agents communicate through structured reports, not free-form text
- Each message includes agent ID for traceability
- Blocked agents must report immediately rather than waiting silently
- The coordinator agent reviews all reports before merging
Error Handling
See references/errors.md for common failure modes and recovery steps.
Examples
See references/examples.md for complete spawn-and-report workflows.
What ships with it: 3 files
904 B alongside SKILL.md
references/
- errors.md165 B
- examples.md67 B
- ui-test-report.md672 B
Gives 0 of the 12 instructions most context ai engineering skills give in 358 tokens
Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06
- Dispatch a fresh subagent for each taskin 76 of 1328, across 59 files
- Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
- Dispatch a final code reviewer after all tasksin 38 of 1328, across 26 files
- Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
- Use the least powerful model capable of the taskin 33 of 1328, across 26 files
- Create a TodoWrite list for all tasksin 32 of 1328, across 22 files
- Perform a task review after each implementationin 31 of 1328, across 24 files
- Extract all tasks and context from the planin 29 of 1328, across 20 files
- Provide full task text to subagentsin 28 of 1328, across 20 files
- Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
- Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
- Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files
Said here and by no other author read
- send spawn requests for each task
- track agent reports as they complete
- reassign blocked tasks
- combine agent outputs into final deliverable
- resolve conflicts during merge
- include agent ID in every message
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.