安全扫描（ClawHub）

• VirusTotal ：可疑
• OpenClaw ：良性

OpenClaw 评估

The skill's requirements and behavior are coherent with its stated purpose (compressing and caching context and storing/retrieving memories) and nothing in the package requests unrelated privileges or credentials.

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「usewhisper」。简介：Official Whisper Context skill for OpenClaw. Cuts context tokens via delta comp…。
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/alinxus/usewhisper/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
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`) 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)

```bash
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):

```bash
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

```bash
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

```bash
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:

```bash
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

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

### Search memories

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

### Oracle search / research

```bash
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

```bash
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)

```bash
node whisper-context.mjs cache_stats
```

### Cache warm (optional)

```bash
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.