據 AlphaMoat 統計,截至 2026-09,Ollama Memory Embeddings 累計下載 3,025 次、獲 6 個星標,在全部 5,078 個 AI 技能中排名第 3,712 位,在「AI 智慧體」分類(969 個)中排名第 747 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 90 | 3010 | 5 | - |
| SkillHub ↗ | 119 | 3025 | 6 | — |
各平台的計數口徑不同,統計時間不一致。
它可檢查 Ollama 是否已安裝且可連線,讓使用者選擇 embedding 模型,並可選擇匯入已有本地 embedding GGUF。它隻影響 memory-search 的 embedding 生成,不改變聊天或 completion 路由。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 3025 | — | 6 |
| 2026-08 | 2835 | +6.0% | 6 |
| 2026-07 | 2675 | +13.0% | 6 |
| 2026-06 | 2367 | +8.4% | 6 |
| 2026-05 | 2184 | — | 5 |
| 2026-03 | 1661 | — | 5 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
--- slug: ollama-memory-embeddings version: "1.0.4" display_name: Ollama Memory Embeddings displayName: Ollama Memory Embeddings name: ollama-memory-embeddings description: > Configure OpenClaw memory search to use Ollama as the embeddings server (OpenAI-compatible /v1/embeddings) instead of the built-in node-llama-cpp local GGUF loading. Includes interactive model selection and optional import
of an existing local embedding GGUF into Ollama. --- # Ollama Memory Embeddings This skill configures OpenClaw memory search to use Ollama as the **embeddings server** via its OpenAI-compatible `/v1/embeddings` endpoint. > **Embeddings only.** This skill does not affect chat/completions routing — > it only changes how memory-search embedding vectors are generated. ## What it does - Installs this skill under `~/.openclaw/skills/ollama-memory-embeddings` - Verifies Ollama is installed and reachable - Lets the user choose an embedding model: - `embeddinggemma` (default — closest to OpenClaw built-in) - `nomic-embed-text` (strong quality, efficient) - `all-minilm` (smallest/fastest) - `mxbai-embed-large` (highest quality, larger) - Optionally imports an existing local embedding GGUF into Ollama via `ollama create` (currently detects embeddinggemma, nomic-embed, all-minilm, and mxbai-embed GGUFs in known cache directories) - Normalizes model names (handles `:latest` tag automatically) - Updates `agents.defaults.memorySearch` in OpenClaw config (surgical — only touches keys this skill owns): - `provider = "openai"` - `model = <selected model>:latest` - `remote.baseUrl = "http://127.0.0.1:11434/v1/"` - `remote.apiKey = "ollama"` (required by client, ignored by Ollama) - Performs a post-write config sanity check (reads back and validates JSON) - Optionally restarts the OpenClaw gateway (with detection of available restart methods: `openclaw gateway restart`, systemd, launchd) - Optional memory reindex during install (`openclaw memory index --force --verbose`) - Runs a two-step verification: 1. Checks model exists in `ollama list` 2. Calls the embeddings endpoint and validates the response - Adds an idempotent drift-enforcement command (`enforce.sh`) - Adds optional config drift auto-healing watchdog (`watchdog.sh`) ## Install ```bash bash ~/.openclaw/skills/ollama-memory-embeddings/install.sh ``` From this repository: ```bash bash skills/ollama-memory-embeddings/install.sh ``` ## Non-interactive usage ```bash bash ~/.openclaw/skills/ollama-memory-embeddings/install.sh \ --non-interactive \ --model embeddinggemma \ --reindex-memory auto ``` Bulletproof setup (install watchdog): ```bash bash ~/.openclaw/skills/ollama-memory-embeddings/install.sh \ --non-interactive \ --model embeddinggemma \ --reindex-memory auto \ --install-watchdog \ --watchdog-interval 60 ``` > **Note:** In non-interactive mode, `--import-local-gguf auto` is treated as > `no` (safe default). Use `--import-local-gguf yes` to explicitly opt in. Options: - `--model <id>`: one of `embeddinggemma`, `nomic-embed-text`, `all-minilm`, `mxbai-embed-large` - `--import-local-gguf <auto|yes|no>`: default `no` (safer default; opt in with `yes`) - `--import-model-name <name>`: default `embeddinggemma-local` - `--restart-gateway <yes|no>`: default `no` (restart only when explicitly requested) - `--skip-restart`: deprecated alias for `--restart-gateway no` - `--openclaw-config <path>`: config file path override - `--install-watchdog`: install launchd drift auto-heal watchdog (macOS) - `--watchdog-interval <sec>`: watchdog interval (default 60) - `--reindex-memory <auto|yes|no>`: memory rebuild mode (default `auto`) - `--dry-run`: print planned changes and commands; make no modifications ## Verify ```bash ~/.openclaw/skills/ollama-memory-embeddings/verify.sh ``` Use `--verbose` to dump raw API response on failure: ```bash ~/.openclaw/skills/ollama-memory-embeddings/verify.sh --verbose ``` ## Drift enforcement and auto-heal Manually enforce desired state (safe to run repeatedly): ```bash ~/.openclaw/skills/ollama-memory-embeddings/enforce.sh \ --model embeddinggemma \ --openclaw-config ~/.openclaw/openclaw.json ``` Check for drift only: ```bash ~/.openclaw/skills/ollama-memory-embeddings/enforce.sh \ --check-only \ --model embeddinggemma ``` Run watchdog once (check + heal): ```bash ~/.openclaw/skills/ollama-memory-embeddings/watchdog.sh \ --once \ --model embeddinggemma ``` Install watchdog via launchd (macOS): ```bash ~/.openclaw/skills/ollama-memory-embeddings/watchdog.sh \ --install-launchd \ --model embeddinggemma \ --interval-sec 60 ``` ## GGUF detection scope The installer searches for embedding GGUFs matching these patterns in known cache directories (`~/.node-llama-cpp/models`, `~/.cache/node-llama-cpp/models`, `~/.cache/openclaw/models`): - `*embeddinggemma*.gguf` - `*nomic-embed*.gguf` - `*all-minilm*.gguf` - `*mxbai-embed*.gguf` Other embedding GGUFs are not auto-detected. You can always import manually: ```bash ollama create my-model -f /path/to/Modelfile ``` ## Notes - This does not modify OpenClaw package code. It only updates user config. - A timestamped backup of config is written before changes. - If no local GGUF exists, install proceeds by pulling the selected model from Ollama. - Model names are normalized with `:latest` tag for consistent Ollama interaction. - If embedding model changes, rebuild/re-embed existing memory vectors to avoid retrieval mismatch across incompatible vector spaces. - With `--reindex-memory auto`, installer reindexes only when the effective embedding fingerprint changed (`provider`, `model`, `baseUrl`, `apiKey presence`). - Drift checks require a non-empty apiKey but do not require a literal `"ollama"` value. - Config backups are created only when a write is needed. - Legacy schema fallback is supported: if `agents.defaults.memorySearch` is absent, the enforcer reads known legacy paths and mirrors writes to preserve compatibility.
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。