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luci-memory

作者 memories-ai-official · GitHub ↗ · v1.0.0 · MIT-0
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在 OpenClaw 中安装
/install luci-memories
功能描述
Search personal video memory — media content (videos, images, keyframes, transcripts) and portrait data (traits, events, relationships, speeches). Use when t...
使用说明 (SKILL.md)

luci-memory

Setup

Requires an MEMORIES_AI_KEY. On first use, if no key is found, the script will error and ask for one.

When the user provides their key, save it to {baseDir}/.env:

MEMORIES_AI_KEY=sk-their-key-here

After that, everything just works — the key is loaded automatically from .env on every run.

Timezone

All timestamps in Luci-memory are stored and returned in UTC. Skill output labels them with " UTC" so this is unambiguous. The user's local timezone is in USER.md (e.g. Asia/Shanghai). You are responsible for converting in both directions:

  1. Reading results. When presenting captured_time to the user, convert from UTC to the user's local timezone. Never show raw UTC labels to the user.

  2. Writing filters. --after and --before are interpreted as UTC. If the user says relative dates like "yesterday" or "this morning", convert their local-time intent to a UTC range before passing the dates.

Example (user in Asia/Shanghai, UTC+8, asks "what did I do yesterday" on 2026-04-08):

  • Local intent: 2026-04-07 00:00 → 2026-04-08 00:00 (Asia/Shanghai)
  • UTC range to pass: --after 2026-04-06T16:00:00 --before 2026-04-07T16:00:00

If USER.md has no timezone and the user uses relative dates, ask them first.

Unified search across personal media and portrait data from the Luci-memory API.

The user's videos go through two processing pipelines that produce different data:

  • Media content (personal): video summaries, audio transcripts, visual transcripts, keyframes, images
  • People & knowledge (portrait): traits, events with participants, relationships, speeches attributed to speakers

When to use

  • User asks to find or search videos, images, or photos
  • User asks what was said or shown in a video
  • User asks to list recent videos or images
  • User asks about media at a specific location or time
  • User asks about traits, personality, hobbies, interests
  • User asks what events happened, or events involving specific people
  • User asks about relationships between people
  • User asks about what someone said
  • User mentions "luci memory" or wants to use their video memory

Choosing the right type

  • About content (what happened, what was said/shown, find media) → use media types (search_video, query_audio, etc.)
  • About people (who, traits, relationships, named individuals) → use portrait types (traits, events, speeches, etc.)
  • Ambiguous questions like "What happened with Alice last week?" → use both: portrait types to identify the person and events, media types to get detailed video content and transcripts.
  • Person name fallback: Portrait data only exists for people who have appeared in at least 5 videos AND been named by the user in the app. If a portrait query by person name returns no results, fall back to media types — search video summaries, audio transcripts, or visual transcripts for mentions of that name instead.

Relevance guidelines

  • There is no rerank process — retrieved results may contain items irrelevant to the user's actual intent.
  • Always verify relevance: after receiving results, check each item against the user's original query. Only present results that are relevant. Discard anything that doesn't match.
  • Refine and retry: if results seem off or too broad, retry with a more specific query, narrower date range, or additional filters. Do not just dump low-quality results to the user.
  • Ask the user: if the query is ambiguous or too vague to produce good results, ask the user for more specific conditions before searching. It is better to clarify than to return noise. Do this no more than 1 time.

No hallucination — ground every claim in retrieved data

  • Never fabricate what the user did, said, or experienced. Every detail in your answer must come from actual search results.
  • Multi-step retrieval: for questions like "what did I do and say at XXX", do NOT answer from a single broad search. Follow this pattern:
    1. Locate: search broadly (search_video, search_events) to find relevant video_ids or event_ids.
    2. Retrieve: once you have IDs, prefer query_audio / query_visual with --video-ids to get complete transcripts. You can also use search_audio / search_visual scoped to those video IDs to find specific moments — use both flexibly as needed.
  • Do not stuff keywords into search queries. Each semantic search query should be a short, coherent natural-language query, rather than stacking multiple possible words. You are encouraged to try different ones and query various times though.
  • If data is missing, say so. Do not fill gaps with plausible-sounding guesses. "I couldn't find transcript data for that video" is always better than making something up.

How to invoke

Note: --after / --before are UTC. Convert from the user's local timezone first (see Timezone section above).

Returning Images/Keyframes to User

Image and keyframe results include bucket and blob fields. To send an image in chat, fetch the bytes from the Luci-memory image proxy endpoint, then forward via OpenClaw:

  1. Download the image bytes from the proxy endpoint:
    curl -sL -o /path/to/workspace/image.jpg \
      "https://skills.memories.ai/luci-memory/personal/image?bucket=\x3Cbucket>&blob=\x3Cblob>"
    
  2. Send via OpenClaw message CLI:
    openclaw message send --channel \x3Cchannel> --target \x3Cchat_id> --media /path/to/workspace/image.jpg --message "caption"
    
  3. Cleanup the file after sending:
    rm /path/to/workspace/image.jpg
    

⚠️ Always quote the URL — it contains & and the blob may have / characters. ⚠️ Do NOT use /tmp or paths outside the workspace — some tools block external paths. ⚠️ The image tool only analyzes images — it cannot send them to the user. Use openclaw message send --media instead.

============ Media content (personal) ============

--- Video ---

bash {baseDir}/run.sh --query "cooking in kitchen" --type search_video bash {baseDir}/run.sh --query "what did I do" --type search_video --location "Heze" bash {baseDir}/run.sh --query "meeting" --type search_video --after 2025-12-01 --before 2026-01-01 bash {baseDir}/run.sh --type query_video bash {baseDir}/run.sh --type query_video --location "Suzhou" --after 2025-12-01

--- Image ---

bash {baseDir}/run.sh --query "sunset" --type search_image bash {baseDir}/run.sh --query "food" --type search_image --location "Beijing" bash {baseDir}/run.sh --type query_image

--- Audio Transcripts (what was said) ---

bash {baseDir}/run.sh --query "talking about work" --type search_audio bash {baseDir}/run.sh --query "budget" --type search_audio --video-ids VI123,VI456 bash {baseDir}/run.sh --type query_audio --video-ids VI123,VI456

--- Visual Transcripts (what was shown) ---

bash {baseDir}/run.sh --query "walking in park" --type search_visual bash {baseDir}/run.sh --type query_visual --video-ids VI123,VI456

--- Keyframes ---

bash {baseDir}/run.sh --query "person waving" --type search_keyframe bash {baseDir}/run.sh --type query_keyframe --video-ids VI123,VI456

============ People & knowledge (portrait) ============

--- Traits ---

bash {baseDir}/run.sh --type traits bash {baseDir}/run.sh --type traits --person "Alice" bash {baseDir}/run.sh --query "outdoor activities" --type search_traits

--- Events ---

bash {baseDir}/run.sh --type events bash {baseDir}/run.sh --type events --person "Alice" bash {baseDir}/run.sh --type events --person "Alice,Bob" bash {baseDir}/run.sh --type events --after 2025-12-01 --before 2026-01-01 bash {baseDir}/run.sh --query "cooking in kitchen" --type search_events bash {baseDir}/run.sh --query "meeting" --type search_events --person "Bob" --after 2025-12-01

--- Relationships ---

bash {baseDir}/run.sh --type relationships bash {baseDir}/run.sh --type relationships --person "Alice"

--- Speeches ---

bash {baseDir}/run.sh --type speeches bash {baseDir}/run.sh --type speeches --person "Alice" bash {baseDir}/run.sh --type speeches --event-ids EVT123,EVT456 bash {baseDir}/run.sh --type speeches --person "Alice" --event-ids EVT123


## Parameters

| Flag | Short | Description |
|------|-------|-------------|
| `--query` | `-q` | Search term (required for `search_*` types) |
| `--type` | `-t` | Operation type (default: `search_video`) |
| `--top-k` | `-k` | Max results (default: 10) |
| `--location` | `-l` | Filter by location name, geocoded via Google Maps (e.g. "Suzhou") |
| `--after` | | Only results after this date (`YYYY-MM-DD` or `YYYY-MM-DDTHH:MM:SS`) |
| `--before` | | Only results before this date |
| `--video-ids` | | Comma-separated video IDs (media types) |
| `--person` | `-p` | Filter by person name(s), comma-separated (portrait types). Use `user` for self. |
| `--event-ids` | | Comma-separated event IDs (portrait types) |

## Image bytes

Image and keyframe results return `bucket` and `blob` (no signed URLs). To get the actual image bytes, hit the proxy endpoint — see "Returning Images/Keyframes to User" above. The endpoint streams the JPEG bytes directly with no expiration or auth on the client side.

## Types reference

### Media search types (require `--query`)
| Type | What it searches | Supports |
|------|-----------------|----------|
| `search_video` | Video summaries by meaning | `--location`, `--after/before` |
| `search_image` | Image descriptions by meaning | `--location`, `--after/before` |
| `search_audio` | Audio transcripts by meaning | `--video-ids`, `--after/before` |
| `search_visual` | Visual transcripts by meaning | `--video-ids`, `--after/before` |
| `search_keyframe` | Keyframe images by meaning | `--video-ids`, `--after/before` |

### Media query types (list/filter)
| Type | What it returns | Requires | Supports |
|------|----------------|----------|----------|
| `query_video` | Recent videos | — | `--location`, `--after/before` |
| `query_image` | Recent images | — | `--location`, `--after/before` |
| `query_audio` | Audio transcripts for videos | `--video-ids` | `--after/before` |
| `query_visual` | Visual transcripts for videos | `--video-ids` | `--after/before` |
| `query_keyframe` | Keyframes for videos | `--video-ids` | `--after/before` |

### Portrait query types (list/filter)
| Type | What it returns | Supports |
|------|----------------|----------|
| `traits` | Personality traits, hobbies, interests | `--person` |
| `events` | Events with participants | `--person`, `--after/before`, `--event-ids` |
| `relationships` | How user relates to people | `--person` |
| `speeches` | What people said | `--person`, `--event-ids` |

### Portrait search types (semantic, require `--query`)
| Type | What it searches | Supports |
|------|-----------------|----------|
| `search_events` | Events by meaning | `--person`, `--after/before` |
| `search_traits` | Traits by meaning | — |
安全使用建议
No artifact-backed malicious behavior was found. Before installing, make sure you trust the publisher and Memories.ai, understand that a local .env file will store your API key, and avoid having the agent send private images or transcripts into shared chats unless you explicitly want that.
功能分析
Type: OpenClaw Skill Name: luci-memories Version: 1.0.0 The skill provides a legitimate interface for searching personal video memory and portrait data via the Luci-memory API. It includes functionality for managing an API key, resolving user IDs, and retrieving media content like transcripts and images. The code in `scripts/run.py` and instructions in `SKILL.md` are consistent with the stated purpose, and all network communication is directed to the service's own domains (memories.ai).
能力标签
requires-sensitive-credentials
能力评估
Purpose & Capability
The stated purpose and visible code align around searching personal media, transcripts, and portrait data, but the data categories are highly private.
Instruction Scope
The instructions emphasize relevance checking and grounding answers in retrieved data; image-return instructions involve downloading and sending private media and should stay user-directed.
Install Mechanism
There is no package install step and the wrapper runs Python locally, but the registry lists no source or homepage for provenance.
Credentials
The MEMORIES_AI_KEY credential and external Memories.ai API calls are expected for the stated integration, but they grant access to sensitive personal memory content.
Persistence & Privilege
The skill persists the API key in a local .env file and reloads it on later runs; no background process or self-persistence was shown.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install luci-memories
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /luci-memories 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
luci-memories 1.0.0 - Initial release: unified search across personal video media (videos, images, transcripts, keyframes) and portrait data (traits, events, relationships, speeches) - Provides clear guidance on when and how to use different query types (media vs. people/portrait) - Includes robust timezone conversion and query handling for user-local relative dates - Emphasizes strict relevance filtering and retrieval-based answering—no fabrication of user events or actions - Detailed instructions for retrieving and sending images via OpenClaw - Covers fallback procedures when person-based portrait data is unavailable
元数据
Slug luci-memories
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

luci-memory 是什么?

Search personal video memory — media content (videos, images, keyframes, transcripts) and portrait data (traits, events, relationships, speeches). Use when t... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 48 次。

如何安装 luci-memory?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install luci-memories」即可一键安装,无需额外配置。

luci-memory 是免费的吗?

是的,luci-memory 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

luci-memory 支持哪些平台?

luci-memory 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 luci-memory?

由 memories-ai-official(@memories-ai-official)开发并维护,当前版本 v1.0.0。

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