PenguinHarness is a local-first platform in which multiple AI agents create, evaluate, optimize, and deploy agent applications. It is for people building AI software who want agents to generate applications and improve their own behavior through skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/prism-shadow/penguin-harness/remote-claude-codenpx skills add Prism-Shadow/penguin-harness --skill remote-claude-codegit clone --depth 1 https://github.com/Prism-Shadow/penguin-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/prism-shadow/penguin-harness/remote-claude-code)<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/remote-claude-code"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/remote-claude-code.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00107 | $0.05635 |
| Opus 5 | $0.00053 | $0.02818 |
| Sonnet 5 | $0.00021 | $0.01127 |
| Haiku 4.5 | $0.00011 | $0.00564 |
Grade A, and why
remote-claude-code scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Remote Claude Code
Drive Claude Code on a remote Linux host over SSH. Three modes, in increasing interactivity:
- Persistent SSH session — one long-lived expect-driven connection you keep feeding commands across turns.
- Headless (
claude -p) — one-shot or scripted calls, with the stdin fix that naive invocations need. - Interactive TUI — the real Claude Code UI inside a remote
tmuxsession, driven withtmux send-keys/tmux capture-pane. tmux is the way to do interactive use.
Reach for this skill when the user wants to run or drive Claude Code on a server, keep an SSH connection open across turns, or hold a continuous conversation with Claude Code on a remote box. In a relayed conversation you are a pure message pipe — the user's words go to Claude Code verbatim (section 4).
Before you start
If the user's message only invokes this skill without a concrete task, ask what they want — at minimum the remote host, the SSH user, and what Claude Code should do there.
Credential rules — non-negotiable:
- This document contains no real credentials:
<ssh-user>,<remote-host>,<target-user>,<sess>are placeholders you substitute at runtime from what the user provides or from this agent's key vault (suggested keys:REMOTE_SSH_HOST,REMOTE_SSH_USER,REMOTE_SSH_PASSWORD). Vault values reach your shell environment on the next task — check with[ -n "$REMOTE_SSH_PASSWORD" ] && echo ok || echo missing, and if missing ask the user to add the keys to the key vault (gear icon on the agent card → settings → key vault tab) or to provide the values in chat. - Never hardcode a password into scripts left on disk, deliverables, or your final answer. Have expect read it from the environment (
$env(...)); if the user pasted it in chat, export it only into the running process's environment, scrub it from logs and replies, and delete any temp file that embeds it as soon as the session is done. - Prefer SSH keys when they are already set up — then no password tooling is needed at all.
- Back up remote config before modifying it, and never copy remote credential stores (
~/.claude/.credentials.json, OAuth fields of~/.claude.json, private keys) anywhere.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · -5 lines fcebcbe01f6a
- 5d ago First seen · 218 lines · 107 tokens per session scan A ecb47ae6c6ad
remote-claude-code is a skill published in the GitHub repository Prism-Shadow/penguin-harness (1,982 stars, last pushed today), licensed Apache-2.0. It adds 107 tokens to every session and 5,635 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
release
Prepare and publish stable Agent Lightning releases through the repository's version bump, pull-request checks, merge, tag, PyPI trusted-publishing, and versioned-documentation workflows. Use when asked to plan, cut, verify, or explain a release; treat nightly TestPyPI builds as a separate path.
agent-lightning
Provides the action space, tradeoffs, and evaluation context for improving an editable AI agent against a benchmark while preserving its deployment contract. Use when optimizing agent accuracy, cost, latency, or reliability.
harness-creator
Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is…
agentfield-use
Whenever you have a discrete task to perform — one the user delegated, or one that arose inside your own work — check FIRST whether an installed AgentField agent covers it, and offload to it by default when one does. Coverage, not task size, is the test: even a small job goes to a covering agent. The check is cheap …
agentfield
Design and ship a multi-agent system on AgentField. Use when the user asks to build, scaffold, design, or run an agent, reasoner network, multi-agent backend, or 'an agent that does X' — whenever the work would otherwise be a single LLM call or a flat LangChain/CrewAI/AutoGen chain. The skill produces composite…
agentfield-personal
Build and install a personal AI agent on this machine's AgentField: real source in /agentfield-agents, packaged with agentfield-package.yaml, installed with af install, started with af run, registered on the local control plane, and visible in AgentField Desktop with a keys form and an auto-start toggle. Use when the…