From 30 Minutes to 8: How LLM-Mode Reflect Works

This is part thirteen in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part ten covered the full improve pipeline — all five phases and how they connect. Part fourteen covers what 48 runs per day looks like in practice, including hardware benchmarks and the reliability bugs that surface at that frequency. The reflect pass inside akm improve has three execution modes. Most installs are still running the slowest one. Agent mode — the original — spawns an opencode or claude subprocess for each reflect call. The subprocess starts cold, acquires a session, assembles context, makes its LLM call, and exits. That cold-start overhead is real: each call takes approximately 30 seconds on a quiet machine. Run akm improve against a 69-ref stash and the reflect phase alone costs about 35 minutes. SDK mode eliminated the subprocess. The reflect call runs in-process, cutting per-call latency to 10–15 seconds. A 69-ref run drops to 12–17 minutes — better, but still bounded by round-trip overhead that the reflect task does not actually need. LLM mode removes the round trip entirely. The context for reflect is statically pre-assembled — no live tool calls, no file reads, no external context needed. A direct HTTP call to the LLM endpoint is sufficient, and it costs 6–10 seconds per call. A 69-ref run completes in 8–10 minutes. Mode Per-call latency 69-ref run agent (CLI subprocess) ~30s ~35 min sdk (in-process) ~10–15s ~12–17 min llm (direct HTTP) ~6–10s ~8–10 min The 3–4× end-to-end improvement is from eliminating overhead that was never necessary for what reflect does. Why Reflect Does Not Need an Agent The reflect pass takes a stash asset, examines its current content, and proposes a refined version. The inputs are fixed before the pass starts: the asset text, its metadata, and the improvement prompt. Nothing changes mid-call. No files need to be opened. No search queries need to fire. No external context needs to be pulled