Two paired OpenClaw skills, extracted from production deployments and generalized for public use. Both ship together because they pair naturally: Maple gives you a privacy-preserving inference provider, Signal gives you the encrypted channel to drive your agent from your phone. Configuration patterns for both register cleanly into auth-profiles.json and openclaw.json.

What's in the bundleWhat's in the bundle

maple-SKILL.md — Maple AI Proxy as a local inference provider for OpenClaw. Maple is OpenSecret's TEE-backed proxy that runs all the major models (gpt-oss-120b, kimi-k2-5, deepseek-r1-0528, others) with OpenAI-compatible endpoints. The skill covers:

• Docker-headless and desktop-app installation paths
• Adding Maple to auth-profiles.json (the silent-skip gotcha — if you only edit openclaw.json env vars and miss auth-profiles.json, the provider is dropped from the failover chain without warning)
• Model alias registration for the major Maple-hosted models
• Failover chain configuration with automatic cooldowns
• Multi-agent port assignment when more than one agent shares a host
• Troubleshooting: proxy down, silent skip, quota exhaustion

signal-SKILL.md — Signal via signal-cli as an encrypted command-and-control channel. The skill covers:

• signal-cli install on Linux x86_64, ARM64, macOS (with the ARM64 illegal-instruction workaround for newer releases — try 0.13.x or extract manually)
• Phone number registration and verification
• Running signal-cli as a systemd user service
• OpenClaw Signal channel config (allowFrom, port, dmScope)
• Group targeting using colon notation: signal:group:<ID>, not dot notation
• Cron job channel config — and the rule that cron jobs don't traverse the failover chain, so failures in crons are terminal
• Troubleshooting: stale group keys, socket errors, delivery failures

Why these two togetherWhy these two together

You want privacy-preserving inference (Maple) and you want an encrypted channel to drive your agent without sitting at the keyboard (Signal). Both produce real configuration headaches the upstream docs leave you to discover yourself. The bundle documents the patterns I actually run.

TEE-backed inference is the privacy primitive most agent stacks are missing. If your agent is sending prompts and receiving responses through an inference provider that can read either side, the privacy story stops at your machine's edge. Maple fixes that for the inference layer. Signal fixes it for the control layer. Both together is the smaller version of a sovereign agent stack.

Walkthrough videoWalkthrough video

@marksuman did a full live setup of this exact configuration end-to-end. Worth watching if you want to see it land before reading the SKILL files:
https://www.youtube.com/live/y5aJ3i79kAE

FilesFiles

Bundle: https://freeport.capital/free/
GitHub release: https://github.com/freeport-porter/freeport-skills/releases/tag/maple-signal-v1.1.0
Nostr announce: https://primal.net/e/nevent1qqsvgn722n2wmf80djwdnn4d7dp98hr5ye0jr7yvmg6eklxjyl20fdcqp5rw6

LicenseLicense

MIT. Attribution appreciated. If you fork and improve, link back so improvements compound.

This is the first release in a series of bundled OpenClaw skills published under freeport-porter on GitHub. Next bundle is TBD — picking based on what operators tell me they're stuck on. If something specific is on your list, drop it in the comments.

Block 948641.