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 skills add Prism-Shadow/penguin-harness --skill penguin-orchestrationgit 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/penguin-orchestration)<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/penguin-orchestration"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/penguin-orchestration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/penguin-orchestration"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/penguin-orchestration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 127 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 5.1 | $0.00044 | $0.04064 |
| Opus 5 | $0.00022 | $0.02032 |
| Sonnet 5 | $0.00009 | $0.00813 |
| Haiku 4.5 | $0.00004 | $0.00406 |
Grade A, and why
penguin-orchestration 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 5d ago.
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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Penguin Orchestration
The penguin CLI is a thin client of the PenguinHarness server. Inside a harness agent session it reaches the same server that is running you, so you can orchestrate the platform yourself: list and create agents, start conversations with them, steer those conversations while they run, and query costs and scheduled tasks.
Before you start
If the user's message only invokes this skill (e.g. "use penguin-orchestration skill") without a concrete request, ask the user what they want to orchestrate. Read-only commands (project ls, agent ls, ls, logs, cost, schedule ls) are always safe; do not create agents, start sessions or send messages until the goal is clear.
How the connection works
- Inside a harness agent session (you, now): every command subprocess has
PENGUIN_API_URL,PENGUIN_API_TOKEN,PENGUIN_PROJECT_ID,PENGUIN_AGENT_IDandPENGUIN_SESSION_IDinjected, sopenguincommands automatically reach your own server with your project and agent as the defaults — no login step. - Outside an agent (a human shell): the CLI attaches to the running local server via its lock file, or auto-starts one; the local
<data-root>/api-tokenfile (0600) authenticates it. - You are operating the same server that runs you: sessions and agents you create appear live in the web UI, where the user sees and owns everything you spawn.
- The injected token is admin-equivalent. Act accordingly: stick to what the task requires, and prefer read-only commands until a mutation is clearly needed.
Orient first
Before mutating anything, see what exists:
penguin project ls # projects on this server
penguin agent ls # agents in the current project
penguin ls --json # the project's sessions, with running state
--json on any listing gives machine-parseable output.
Command surface
penguin run -m <msg> [--project-id <id>] [--agent-id <id>] [--workspace <path>]
[--model-id <id> --provider <p>] [--approve <mode>] [--thinking <level>]
[--session <session_id>] [--background] [--timeout <duration>]
[--goal [budget]] [--json]
penguin ls [--project-id <id>] [--agent-id <id>] [--days <n>] [-a|--all] [--json]
penguin input [session_id] [-m <text>] [--timeout <duration>]
[--project-id <id>] [--agent-id <id>] [--json] [--server <url>]
penguin logs [session_id] [--project-id <id>] [--agent-id <id>] [--tail <n>]
[-f|--follow] [--timeout <duration>] [--json]
penguin agent ls [--project-id <id>] [--json]
penguin agent create --agent-id <id> [--name <s>] [--description <s>] [--skills <a,b>]
[--project-id <id>] [--json]
penguin project ls [--json]
penguin cost [--days <n>] [--from <d> --to <d>] [--by date|agent|model|session]
[--project-id <id>] [--agent-id <id>] [--json]
penguin schedule ls [--project-id <id>] [--agent-id <id>] [--json]
penguin schedule add <name> --prompt <s> --start-at <ISO|now> [--period <duration>]
[--end-at <ISO>] [--session-id <id> | --workspace <path>
[--model-id <id> --provider <p>]] [--disabled]
[--project-id <id>] [--agent-id <id>]
penguin schedule update <name> [<same field flags>] [--enable|--disable]
[--project-id <id>] [--agent-id <id>]
penguin schedule rm <name> [--project-id <id>] [--agent-id <id>]
What ships with it
1 file 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.
- 5d ago Changed · -4 lines 10a6d0ceaf12
- 11d ago First seen · 166 lines · 44 tokens per session scan A bfbaa8baa773
penguin-orchestration is a skill published in the GitHub repository Prism-Shadow/penguin-harness (2,075 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 4,064 once invoked, about $0.0002 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.
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