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版本数
在 OpenClaw 中安装
/install openclaw-glasses
功能描述
Bilingual search-layer skill for OpenClaw that turns ordinary web lookup into multi-source retrieval, intent-aware ranking, adaptive weighting, thread-pullin...
安全使用建议
This skill appears to implement the claimed multi-source search features, but it omits critical metadata about credentials and local file access. Before installing or running it: (1) Ask the publisher for provenance (homepage, owner identity) and for a declared list of required env vars/config paths. (2) Do not run it with real secrets present — remove or rotate any GitHub token or LLM key you don't want the skill to see. (3) If you want to test it, run in an isolated environment (container or VM) and provide only least-privilege API keys (or dummy keys). (4) Inspect ~/.openclaw/credentials/search.json and ~/.git-credentials for sensitive data; prefer explicit env var configuration over left-behind credential files. (5) If you accept the skill, require the author to update the skill metadata to list required env vars (GROK_API_KEY/GROK_API_URL/GROK_MODEL, optional GITHUB_TOKEN) and to document what data is sent to external LLM/APIs. If you cannot verify the source or do not want network/credential exposure, do not install or run it.
功能分析
Type: OpenClaw Skill
Name: openclaw-glasses
Version: 0.1.2
The skill bundle provides an advanced search and recursive research layer, but it is classified as suspicious due to high-risk credential-seeking behavior in `scripts/fetch_thread.py`. This script attempts to read the user's `~/.git-credentials` file to extract GitHub tokens, which is a sensitive action that crosses security boundaries, even if intended for API rate-limit management. Additionally, the bundle performs recursive web crawling and LLM-based link scoring across multiple platforms (GitHub, Reddit, HN, V2EX), which, while aligned with the stated purpose of 'deep research,' involves broad network access and the handling of API keys from various providers in `scripts/search.py` and `scripts/relevance_gate.py`.
能力评估
Purpose & Capability
The code implements a multi-source, intent-aware search layer consistent with the skill name/description (scripts for aggregation, thread-pulling, relevance gating and intent guides). However the manifest declares no required env vars or config paths while the code expects provider credentials and may use GitHub tokens and a local OpenClaw credentials file — the capability is plausible but the declared metadata is incomplete.
Instruction Scope
Runtime instructions call the included Python scripts (search.py, chain_tracker.py, fetch_thread.py, relevance_gate.py). Those scripts perform network requests, call external LLM endpoints, and explicitly read local credential locations (e.g., relevance_gate._load_creds reads ~/.openclaw/credentials/search.json; fetch_thread._find_github_token reads GITHUB_TOKEN/GH_TOKEN and ~/.git-credentials). The SKILL.md does not list these specific file accesses or env vars. Scripts also 'fail open' in some cases (returning candidates when LLM unavailable), increasing silent network activity. The agent running these scripts could therefore read local tokens and send data to external APIs.
Install Mechanism
There is no install spec (no downloads), which reduces supply-chain risk. But the package includes multiple Python scripts that require the 'requests' library (search.py exits if requests missing). Because there is no install step, users may be surprised by runtime failures or by the code executing without explicit declared dependencies. No external binary downloads or obscure URLs were observed.
Credentials
Registry metadata declares no required env vars or config paths, yet the code expects and will use: GROK_API_KEY / GROK_API_URL / GROK_MODEL (relevance_gate._load_creds and env overrides); a credentials JSON at ~/.openclaw/credentials/search.json; and GitHub tokens via GITHUB_TOKEN, GH_TOKEN, or ~/.git-credentials (fetch_thread._find_github_token). Those are reasonable for a multi-source search skill, but they are not declared in the skill metadata and grant access to potentially sensitive secrets (LLM API keys, GitHub tokens).
Persistence & Privilege
The skill is not marked always:true and does not attempt to modify other skills or system-wide agent configuration. Its behavior is limited to fetching web content, calling LLM APIs, and writing output files if asked (chain_tracker can write results.json). Autonomous invocation is enabled by default (normal for skills) but does not on its own change this assessment.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install openclaw-glasses - 安装完成后，直接呼叫该 Skill 的名称或使用
/openclaw-glasses触发 - 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
v0.1.2
Refine the public description to better reflect the original search-layer concept plus the enhanced multi-source, adaptive-weight, Chinese-search, thread-pulling, and finance-aware realtime features. No secrets embedded.
v0.1.1
Refine the public-facing copy with a cleaner bilingual summary and clearer examples. No secrets embedded.
v0.1.0
Initial public release: multi-source search, intent-aware routing, adaptive weighting, Chinese-query optimization, and finance-aware realtime prioritization. Sanitized: no embedded secrets.
元数据
常见问题
OpenClaw Glasses (多源搜索+意图感知+权重自适) 是什么？
Bilingual search-layer skill for OpenClaw that turns ordinary web lookup into multi-source retrieval, intent-aware ranking, adaptive weighting, thread-pullin... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 275 次。
如何安装 OpenClaw Glasses (多源搜索+意图感知+权重自适)？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install openclaw-glasses」即可一键安装，无需额外配置。
OpenClaw Glasses (多源搜索+意图感知+权重自适) 是免费的吗？
是的，OpenClaw Glasses (多源搜索+意图感知+权重自适) 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
OpenClaw Glasses (多源搜索+意图感知+权重自适) 支持哪些平台？
OpenClaw Glasses (多源搜索+意图感知+权重自适) 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 OpenClaw Glasses (多源搜索+意图感知+权重自适)？
由 wewehg（@wewehg）开发并维护，当前版本 v0.1.2。
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