技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 410 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：可疑 · OpenClaw ：可疑
Package：percept-ambient
安全扫描（ClawHub）
- VirusTotal ：可疑
- OpenClaw ：可疑
OpenClaw 评估
The skill's stated purpose (always-on ambient capture) matches its instructions, but it omits important operational details, resource/credential needs, and concrete privacy/security controls — a privacy- and attack-surface risk that should be audited before installing.
目的
Name and description (continuous ambient capture / knowledge graph) align with the SKILL.md. It correctly references complementary skills (percept-listen, percept-summarize) needed to capture and summarize audio. However, the skill claims components and runtime services (LanceDB, NVIDIA NIM embeddings, FTS5, local dashboard on port 8960) without declaring required binaries, credentials, or host requirements (GPU, model access, storage). Those …
说明范围
Instructions direct passive, continuous capture of conversations and building of searchable transcripts and entity graphs — a significant privacy action. The SKILL.md describes assembling context packets and serving a dashboard, but provides no enforcement or explicit steps for obtaining user consent, access controls for the dashboard/API, transcript retention/encryption, or how 'no audio stored' is guaranteed. As an instruction-only skill, th…
安装机制
No install spec (instruction-only), which reduces direct supply-chain risk from this package itself. But the instructions rely on external components (LanceDB, NVIDIA NIM embeddings, FTS5, percept-listen/summarize skills) and a GitHub project link; those components may require downloads, GPU support, or network access that are not documented here. The lack of an install spec leaves those potentially risky installs out-of-band and unvetted.
证书
Registry metadata lists no required env vars or credentials, yet SKILL.md references NVIDIA NIM embeddings (likely requiring model access, a server, or credentials) and a local HTTP dashboard. The skill will write transcripts and vectors to local storage (SQLite + LanceDB) but does not declare config paths, encryption settings, or retention parameters beyond saying 'TTL auto-purge (configurable)'. That mismatch between claimed operations and d…
持久
Although always:false, the skill's purpose is continuous background listening and context accumulation — a persistent capability with high privacy impact. Autonomous invocation is allowed (platform default), meaning an agent could run this continuously without repeated explicit user prompts. There are no explicit safeguards in the instructions to limit when or how long listening runs, or to require per-session consent.
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Percept Ambient」。简介：Continuously captures and summarizes ambient conversations to build a local kno…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/jarvis563/percept-ambient/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
# percept-ambient
Ambient intelligence mode — continuous context awareness without explicit commands.
## What it does
Runs in the background, building a knowledge graph of conversations, entities, and relationships over time. Your agent passively learns context from ambient speech — who you talk to, what projects are active, what decisions were made — without needing explicit commands.
## When to use
- User wants always-on context awareness
- Agent needs background knowledge from daily conversations
- User asks "what do you know about [person/project]?" based on overheard context
## Requirements
- **percept-listen** skill installed and running
- **percept-summarize** skill installed (for entity extraction)
## How it works
1. All conversations are continuously captured and summarized
2. Entities (people, companies, projects, topics) extracted automatically
3. Relationships mapped between entities (works_on, client_of, mentioned_with)
4. Context packets assembled on demand for any agent action
5. Full-text search (FTS5) + vector search (LanceDB) for retrieval
## Context packets
When your agent needs context, Percept assembles a Context Packet:
```json
{
"recent_conversations": [...],
"resolved_entities": [...],
"relationships": [...],
"relevant_history": [...]
}
```
This gives the agent rich situational awareness without loading entire conversation histories.
## Vector search
Semantic search over utterances using NVIDIA NIM embeddings (primary) with all-MiniLM-L6-v2 as offline fallback. Stored in LanceDB (local, zero-infra).
```bash
# Search via dashboard (port 8960) or API
curl localhost:8960/api/search?q=project+deadline&mode=hybrid
```
## Privacy controls
- All data stored locally in SQLite + LanceDB
- TTL auto-purge (configurable retention periods)
- No audio stored — only transcripts
- Dashboard → Settings → Privacy for granular controls
## Real-time dashboard
Monitor ambient intelligence at `http://localhost:8960`:
- Live conversation feed
- Entity graph visualization
- Search across all conversations
- Analytics and usage stats
## Links
- **GitHub:** https://github.com/GetPercept/percept