acupof-ai

archive-experience

Use this skill when archiving docs/experience/wins or docs/experience/errors entries — when the entry count approaches the hygiene cap (wins 790, errors 296), during the weekly hygiene sweep, or when an entry is fully superseded. It enforces future-value classification (not age or quota), oldest-first zero-inbound-reference discovery, the frozen-archive seal via scripts/archive_experience.py, and the rule that sealed entries never change.

arle-upstream-runtime-scan

Use this skill when ckl asks to ground an ARLE serving/runtime, model-path, benchmark, capacity, Qwen3.5/DeepSeek, scheduler, paged_kv, MLX, autograd, or OPD decision in upstream SGLang/vLLM/TensorRT-LLM evidence before changing local code. It distills BBuf AI-Infra Auto Driven SKILLS into an ARLE-specific source-survey workflow without symlinking or vendoring those repositories.

find-simplifications

Use when asked to find dead code, unused flags, speculative generality, or simplification candidates in ARLE — "what can we delete", "simplification sweep", "flag deletion wave", or auditing a surface for over-built machinery. Turns a broad "find things to simplify" request into evidence-backed deletions or wins/errors entries. Prefer a few well-proven candidates over a pile of thin guesses.

tmux-agent-control

Use this skill when ckl asks to inspect, queue work for, interrupt, spawn, replace, or otherwise drive another coding-agent CLI running inside tmux. Covers ARLE's known-safe tmux path for Codex/Claude Code delegation, including session discovery, capture-pane status checks, Enter semantics, long-brief buffer paste, queue-vs-immediate behavior, and don't-send-to-yourself safety.

understand-until-simple

Use this skill BEFORE writing code for any task you're tempted to call "hard / tough / complex", any "why is X slow / where's the bottleneck", any "should I optimize Y", any concurrency/perf/kernel/scheduler investigation, or any time you catch yourself hand-waving a root cause. It is the pre-implementation understanding GATE — decompose to the atomic (line/kernel/buffer) level, get MEASURED evidence, let measurement correct your hypotheses, until the problem is SIMPLE and quantified. Only then write code. If it still feels hard, you haven't decomposed enough.