Use when the user requests integration testing, feature validation, or test plan execution
日本語の概要は準備中です。原文の説明を表示しています。
Use when pulling or downloading a new llamacpp model. The active ROCm image (kyuz0/amd-strix-halo-toolboxes) fails to start in the ephemeral pull container without ROCm device access. Must temporarily switch to the standard CPU image.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
This machine uses kyuz0/amd-strix-halo-toolboxes:rocm-7.2 for llamacpp inference (AMD Strix Halo / gfx1151 — not supported by the official ROCm build). Harbor's pull mechanism starts an ephemeral container with --n-gpu-layers 0; the custom image fails in that context without ROCm device access. Use the standard CPU image just for pulling, then restore.
harbor config set llamacpp.image.rocm ghcr.io/ggml-org/llama.cpp:server
harbor pull <hf-owner/model-repo:quantization>
# Examples:
harbor pull bartowski/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
harbor pull unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF:UD-Q4_K_XL
Harbor detects the HuggingFace model spec and routes it through llamacpp automatically.
harbor config set llamacpp.image.rocm kyuz0/amd-strix-halo-toolboxes:rocm-7.2
harbor up llamacpp-fa 1 -dio --no-mmap --ctx-size 64000 --fit off) are stored separately in config and are not affectedharbor llamacpp modelharbor config get llamacpp.image.rocmまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when the user requests integration testing, feature validation, or test plan execution
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to systematically fix AI code slop — duplicated logic, over-engineering, silent error swallowing, convention drift, cargo-cult patterns, and other LLM-introduced architectural decay — over a specified duration
日本語の概要は準備中です。原文の説明を表示しています。
Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.
日本語の概要は準備中です。原文の説明を表示しています。
Runs autonomous keep/discard experiments on a codebase to optimize a single metric for a fixed duration, in the style of karpathy/autoresearch. Use when the user says "autoresearch" (optionally with a focus, e.g. "autoresearch the optimizer"), asks to run experiments on a repo overnight, to hill-climb or optimize a metric autonomously, or points at a repo with a karpathy-style program.md.
日本語の概要は準備中です。原文の説明を表示しています。
Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.
日本語の概要は準備中です。原文の説明を表示しています。
Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.
日本語の概要は準備中です。原文の説明を表示しています。