usewhisper

Official Whisper Context skill for OpenClaw. Cuts context tokens via delta compression + caching, and adds long-term memory across sessions.

Security Scan

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.

Purpose & Capability

Name/description, required binary (node), and required env vars (WHISPER_CONTEXT_API_KEY, WHISPER_CONTEXT_PROJECT) match the declared purpose of calling a remote Whisper Context API. The helper's commands (query_context, ingest_session, memory_* etc.) are directly relevant to context compression, caching, and memory management.

Instruction Scope

SKILL.md and the included script limit local data access to explicit inputs (flags, @file paths, or stdin). The instructions clearly state that ingest_session will send user and assistant text to the remote Context API — a necessary behavior for long-term memory but a privacy consideration. There are no vague 'gather whatever context you need' directives; behavior is explicit.

Install Mechanism

No install spec is present (instruction-only install via ClawHub is documented), and the package includes a single Node script rather than an archive download or installer. This minimizes filesystem persistence risk compared to arbitrary downloads.

Credentials

The skill requests only an API key and project identifier for the remote service (plus an optional API URL). It does not request unrelated credentials or config paths. The required secret (WHISPER_CONTEXT_API_KEY) is proportionate to its function; SKILL.md explicitly warns to treat this as a secret.

Persistence & Privilege

The skill does not request always:true and does not modify other skills or system-wide settings. It can be invoked autonomously (platform default) but has no unusual persistent privileges.

Assessment

This skill appears coherent with its stated purpose, but review and consider the following before installing: 1) Privacy: ingest_session sends user and assistant text to the remote API—ensure you trust the service and its data-retention policies. 2) Secrets: keep WHISPER_CONTEXT_API_KEY secret and rotate it if needed. 3) Default endpoint: the script defaults to https://context.usewhisper.dev—if you have a different endpoint (self-hosted or internal), set WHISPER_CONTEXT_API_URL. 4) Auto-create behavior: the helper may create projects in your org on first use—be aware it will perform write operations remotely. 5) Review the included whisper-context.mjs script yourself (or have an engineer do so) if you have high security requirements; the code is small and uses only fetch and explicit file/stdin reads.

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.