TL;DR
A developer experimenting with a macOS tiling window manager used Claude to combine multiple AI skills and diagnose a performance problem. Merging an optimization agent with a hypothesis-driven debug skill revealed that Swift's JSON encoding of a ~120KB Response was the main IPC bottleneck.
What happened
While iterating on a tiling window manager for macOS, the author invoked an optimize-critical-path skill in Claude to investigate slow behavior in the focus command. Claude launched subagents but began making assumptions rather than measuring, so the author combined that optimizer with a custom debug skill (oberdebug) that follows a hypothesis-driven workflow. The merged approach added timing logs, dispatched exploration agents and ran CLI tests. Reproduction showed a focus operation around ~130ms (target <80ms) and a dump IPC path taking ~82ms (target <20ms). Detailed timing logs isolated JSON encoding as the dominant cost: encode_us measured about 54,720–60,198µs (55–60ms) for a ~122,699-byte payload, while task spawn and AnyCodable wrapping were negligible. After diagnosis, the optimize skill produced remediation suggestions. The author also experimented combining frontend-design and brainstorming skills to shape a blog’s design.
Why it matters
- Composing complementary AI skills can move a workflow from speculative fixes to evidence-driven diagnosis.
- Small, measured bottlenecks (like large JSON payloads) can dominate IPC latency and point to targeted optimizations.
- Layering design expertise into brainstorming changes how product questions are asked and options are generated.
- Automated subagents can accelerate exploration but risk thrashing without measurement and structured debugging.
Key facts
- Symptom: focus operation measured ~130ms; target was under 80ms.
- Reproduction: the issue was reproducible with the CLI command `thegrid focus left`.
- Observed dump IPC latency: ~82ms with a stated target <20ms.
- Root cause: Swift's JSONEncoder.encode() serializing the full Response structure took ~55–60ms per call.
- Measured encode_us range: 54,720–60,198 microseconds.
- Payload size at time of encoding: 122,699 bytes (about 120KB).
- Reported structure contained ~248 windows (part of the serialized state).
- Other phases were negligible: task spawn ~0.05ms, AnyCodable wrapping ~0.005ms.
- File location identified for the slow encode: grid-server/Sources/GridServer/SocketServer.swift:238.
What to watch next
- Design-informed brainstorming was used to shape the new blog’s aesthetic and features (confirmed in the source).
- Whether the optimize-critical-path skill will be changed to avoid serializing the entire Response for IPC — not confirmed in the source.
- If similar combined-skill workflows will be applied to other hotspots in the repository — not confirmed in the source.
Quick glossary
- IPC (Inter-Process Communication): Mechanisms that allow separate processes or components on the same machine to exchange data and requests.
- JSON encoding: The process of converting in-memory data structures into JSON text for storage or transmission.
- Tiling window manager: A window manager that arranges application windows in non-overlapping tiles to maximize screen usage.
- AI skill (in-agent): A modular capability or workflow that an AI assistant can invoke to perform specialized tasks such as debugging or design brainstorming.
Reader FAQ
Was the root cause of the slowdown identified?
Yes — timing showed JSON encoding of the Response struct consumed about 55–60ms, making it the primary bottleneck.
How was the issue reproduced?
The author reports the problem is reproducible using the CLI command `thegrid focus left`.
Did combining skills help find the problem?
Yes — pairing the optimize-critical-path skill with a hypothesis-driven debug skill (oberdebug) produced measurable evidence and a confirmed root cause.
Were fixes applied and verified?
not confirmed in the source
January 7, 2025 Emergent Behavior: When Skills Combine I ran across something interesting today. Claude can combine skills! You might be thinking, "huh?", here's what I mean. I've been vibe…
Sources
- Claude Code Emergent Behavior: When Skills Combine
- Claude Agent Skills: A First Principles Deep Dive
- The complete guide to Claude Code's newest feature “skills”
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