// Capture a session summary — what was done, what decisions were made, and what to do next.
| name | capture |
| description | Capture a session summary — what was done, what decisions were made, and what to do next. |
| argument-hint | [session topic] |
| category | utility |
| version | 2.0.0 |
| user-invocable | true |
Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.
Capture the current session's work into a persistent summary. This creates a record that survives session boundaries — future commands can reference what happened here.
Read the conversation history and identify:
Generate a session summary file at .maestro/sessions/{date}_{topic}.md:
# Session: {topic}
Date: {YYYY-MM-DD}
## Commands Run
- /diagnose → Score: 18/25
- /fortify → Added retry logic to API handlers
- /evaluate → Verified with 3 test scenarios
## Decisions
- Chose retry-with-backoff over circuit breaker (simpler, sufficient for current load)
- Kept synchronous error handling (async not justified yet)
## Files Changed
- `src/api/handler.ts` — added retry wrapper
- `src/middleware/auth.ts` — added input validation
- `tests/api.test.ts` — new test file
## Open Issues
- Rate limiting not yet implemented (deferred to next session)
## Next Steps
1. Run `/guard` to add rate limiting
2. Run `/evaluate` with adversarial test cases
Also append a decision entry to .maestro/decisions.jsonl recording this capture.
.maestro/sessions/After capturing, your next session should start with /recap to restore context, then proceed with the next steps listed above.
NEVER:
Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.