⏳ This skill is pending AI review.
Scores will appear once the review pipeline completes.
penguin-harness-frontend
>-
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
Why PenguinHarness
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.
- Every RSI algorithm in one framework. Default, OPRO, APE, ACE and AWM ship as plugins and run on the same built-in Benchmarks, scoreboard and snapshots.
- One sentence builds an agent app. Describe what you need, and an agent writes the app, its page and its run instructions.
- Cheap to run. A deliberately small toolset, tuned for open models: on our data-analysis suite, the best accuracy at 1/70 of Claude Code's cost.
Self-evolution
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
One sentence, one app
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
What agents can do
// HOW IT'S BUILT
KEY FILES