Maintainer of this skill?
Automated security scan — not a certification.
by Prism-Shadow
🐧 Unified and Stable RSI Platform
# Add to your Claude Code skills
git clone https://github.com/Prism-Shadow/penguin-harnessGuides for using ai agents skills like penguin-harness.
Last scanned: 7/22/2026
{
"issues": [
{
"file": "README.md",
"line": 101,
"type": "remote-install",
"message": "Install command (remote install script piped to a shell — review the source before running): \"curl -fsSL https://penguin.ooo/install.sh | sh\"",
"severity": "low"
},
{
"file": "packages/skills/skills/firecrawl/SKILL.md",
"line": 24,
"type": "secret-exfiltration",
"message": "Instruction appears to send credentials/secrets to an external endpoint",
"severity": "medium"
},
{
"file": "packages/skills/skills/firecrawl/SKILL.md",
"line": 37,
"type": "secret-exfiltration",
"message": "Instruction appears to send credentials/secrets to an external endpoint",
"severity": "medium"
}
],
"status": "PASSED",
"scannedAt": "2026-07-22T06:27:41.276Z",
"npmAuditRan": false,
"pipAuditRan": true,
"promptInjectionRan": true
}See how penguin-harness compares with popular alternatives.
penguin-harness is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Prism-Shadow. 🐧 Unified and Stable RSI Platform. It has 2,845 GitHub stars.
Yes. penguin-harness passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Prism-Shadow/penguin-harness" and add it to your Claude Code skills directory (see the Installation section above).
penguin-harness is primarily written in TypeScript. It is open-source under Prism-Shadow on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh penguin-harness against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
PenguinHarness is an open-source framework for agent self-evolution (recursive self-improvement, RSI). The model stays fixed; the harness evolves: prompts, Skills, tools and hooks. It runs on your computer or server as a desktop app, a Web App, a CLI and an SDK.
Pick an algorithm, pick a Benchmark, and an agent improves another agent round by round. Every candidate is a new version, snapshotted before it changes, and every score lands in the Evaluation Center.
| Toolkit | Algorithm |
|---|---|
default-rsi-toolkit |
Trial, reflect, improve: one hypothesis and one change per round |
opro-rsi-toolkit |
OPRO: new instructions from the history of scored ones |
ape-rsi-toolkit |
APE: candidate instructions induced from examples |
ace-rsi-toolkit |
ACE: lessons from Traces curated into a playbook |
awm-rsi-toolkit |
AWM: workflows induced from successful runs |
Every new Project includes seven Benchmarks, run as Harbor tasks in Docker: PenguinHarness Benchmark Sec A to Sec E (from rag-bench-essential, DeepSWE, AutomationBench, Terminal-Bench-Science and Terminal-Bench) and GDPevo Train and Test. More are in penguin-harness-benchmark.
Demo: a decision agent is evaluated, revises its working method once, and is retested on the same model. Its score goes from 84 to 100, and the cost of every run is on screen.
https://github.com/user-attachments/assets/7925b4b5-db78-4e00-b209-89deca394e34
Build a legal review agent with a simple workspace where users upload contracts, track the review, and download a report with risky clauses, explanations, and suggested revisions.
The result: a legal review app. Users upload contracts, follow each file's review, and download a report of risky clauses with explanations and suggested revisions. Five sample reviews cost $0.0423 in model calls.
https://github.com/user-attachments/assets/28c0ef95-1dfa-47be-bad0-7326ccdf4e7c
Replies can also carry UI: choices, forms, steps, Mermaid diagrams and live widgets (A2UI).
Each harness on the model it is usually paired with, on the same tasks:
Agents can also write and optimize their own Skills (docs).
| Category | Plugins |
|---|---|
| Agent Self-Evolution | default-rsi-toolkit, opro-rsi-toolkit, ape-rsi-toolkit, ace-rsi-toolkit, awm-rsi-toolkit |
| Office Productivity | a2ui, data-analysis, use-firecrawl, browser-automation, use-bento-slides, humanizer, goal, continual-learning, agent-company, penguin-support |
| Software Development | software-development, use-claude-code |
| AI App Development | agent-development, agent-evaluation, model-development, skill-porting |
These are the model families we focus on supporting. The app's Models page lists each variant and its providers.
| Model | Providers |
|---|---|
| GPT 6.1 | OpenAI, OpenRouter |
| Claude 5.5 | Anthropic, OpenRou |