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usewhisper
Official Whisper Context skill for OpenClaw. Cuts context tokens via delta compression + caching, and adds long-term memory across sessions.
安全通过
⚙️脚本
技能说明
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: - Makes outbound HTTPS requests to the Whisper Context API using a user-provided API key. - Does not require additional npm dependencies. - Review the script before use.
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) withcompress: trueandcompression_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_URLis optional (defaults tohttps://context.usewhisper.dev).WHISPER_CONTEXT_PROJECTcan 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_idandsession_idvalues (don’t hardcode them globally; derive them per user/session in your agent).
Usage
All commands print JSON to stdout.
Global flags
--project <slugOrName>: overrideWHISPER_CONTEXT_PROJECT--api_url <url>: overrideWHISPER_CONTEXT_API_URL--timeout_ms <n>: request timeout (default: 30000)
Tips for real agents (to actually slash spend)
- Always call
query_contextfirst and inject the returnedcontextinstead of re-sending your entire chat history. - Keep
compress: true,compression_strategy: "delta", anduse_cache: true(the defaults in this helper) to maximize token savings. - Use stable
user_idandsession_idso 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_sessionsends 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_KEYlike 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
- Before calling the model: run
query_contextand prepend the returnedcontext(if present) to your prompt. - After replying: run
ingest_sessionwith 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: verifyWHISPER_CONTEXT_PROJECT(slug/name) exists.
如何使用「usewhisper」?
- 打开小龙虾AI(Web 或 iOS App)
- 点击上方「立即使用」按钮,或在对话框中输入任务描述
- 小龙虾AI 会自动匹配并调用「usewhisper」技能完成任务
- 结果即时呈现,支持继续对话优化