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usewhisperusewhisper 搜索

Agent Skill

usewhisper 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

25,949

周安装

1,060

GitHub Stars

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下载量

8,395
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:usewhisper(usewhisper 搜索)
来源仓库:https://github.com/alinxus/usewhisper
安装命令:
openclaw skills install usewhisper
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install usewhisper

简介

OpenClaw 的官方 Whisper Context 技能。通过增量压缩+缓存削减上下文令牌,并跨会话添加长期内存。

SKILL.md

name
whisper-context
version
0.1.0
description
Official Whisper Context skill for OpenClaw. Cuts context tokens via delta compression + caching, and adds long-term memory across sessions.
author
Whisper
metadata
openclaw
requires
bins
["node"]
env
["WHISPER_CONTEXT_API_KEY", "WHISPER_CONTEXT_PROJECT"]
optional_env
["WHISPER_CONTEXT_API_URL"]
security
notes

Whisper Context (OpenClaw Skill)

Reduce OpenClaw API spend by shrinking the context you send to the model (delta compression + caching), while keeping long-term memory across sessions.

This skill provides a minimal Node-based helper (whisper-context.mjs) that OpenClaw agents can run to:

  • Retrieve packed context for a user/session (query_context) with compress: true and compression_strategy: "delta"
  • Persist the latest turn into long-term memory (ingest_session)
  • Write/search memories (memory_write, memory_search)
  • Run Oracle search/research (oracle_search)
  • Fetch cost analytics (get_cost_summary)
  • Inspect/warm cache (cache_stats, cache_warm)

Install (ClawHub)

npx clawhub@latest install whisper-context

ClawHub installs the skill folder into your OpenClaw skills workspace (typically ~/.openclaw/workspace/skills/).

Setup

Set environment variables (where OpenClaw reads env for your agent):

WHISPER_CONTEXT_API_URL=https://context.usewhisper.dev
WHISPER_CONTEXT_API_KEY=YOUR_KEY
WHISPER_CONTEXT_PROJECT=openclaw-cost-optimization

Notes:

  • WHISPER_CONTEXT_API_URL is optional (defaults to https://context.usewhisper.dev).
  • WHISPER_CONTEXT_PROJECT can be a project slug/name.
  • If the project does not exist yet, the helper will auto-create it in your org on first use.
  • For best memory behavior, use stable user_id and session_id values (don’t hardcode them globally; derive them per user/session in your agent).

Usage

All commands print JSON to stdout.

Global flags

  • --project <slugOrName>: override WHISPER_CONTEXT_PROJECT
  • --api_url <url>: override WHISPER_CONTEXT_API_URL
  • --timeout_ms <n>: request timeout (default: 30000)

Tips for real agents (to actually slash spend)

  • Always call query_context first and inject the returned context instead of re-sending your entire chat history.
  • Keep compress: true, compression_strategy: "delta", and use_cache: true (the defaults in this helper) to maximize token savings.
  • Use stable user_id and session_id so memory works across sessions and cache keys stay effective.

Query packed context

node whisper-context.mjs query_context \
  --query "What did we decide about the retriever cache?" \
  --user_id "user-123" \
  --session_id "session-123"

Ingest a completed turn

node whisper-context.mjs ingest_session \
  --user_id "user-123" \
  --session_id "session-123" \
  --user "..." \
  --assistant "..."

If your message text is large or hard to shell-escape, pass JSON via stdin:

echo '{ "user": "....", "assistant": "...." }' | node whisper-context.mjs ingest_session --session_id "session-123" --turn_json -

Security / Privacy Notes

  • ingest_session sends both user and assistant text to the Context API (so it can build memory and improve retrieval).
  • The helper only reads local files if you explicitly pass @path (or stdin via -).
  • Treat your WHISPER_CONTEXT_API_KEY like a secret; don’t commit it to git.

Write a memory

node whisper-context.mjs memory_write \
  --memory_type "preference" \
  --content "User prefers concise answers." \
  --user_id "user-123"

Search memories

node whisper-context.mjs memory_search \
  --query "preferences" \
  --user_id "user-123"

Oracle search / research

node whisper-context.mjs oracle_search --query "How does delta compression work?" --mode search
node whisper-context.mjs oracle_search --query "Design a plan..." --mode research --max_steps 3

Cost summary

node whisper-context.mjs get_cost_summary \
  --start_date "2026-01-01T00:00:00.000Z" \
  --end_date "2026-02-01T00:00:00.000Z"

Cache stats (prove your savings)

node whisper-context.mjs cache_stats

Cache warm (optional)

node whisper-context.mjs cache_warm --queries "retriever cache,l1 query cache,delta compression" --ttl_seconds 3600

Agent Integration Pattern

  1. Before calling the model: run query_context and prepend the returned context (if present) to your prompt.
  2. After replying: run ingest_session with the user + assistant messages to persist memory.

Troubleshooting

  • Missing WHISPER_CONTEXT_API_KEY: export the env var where OpenClaw runs commands.
  • HTTP 401/403: verify your API key and that it has access to the project/org.
  • HTTP 404 Project not found: verify WHISPER_CONTEXT_PROJECT (slug/name) exists.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

79.3%
按下载量换算6,657

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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