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 ace-rsi-toolkitgit 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/ace-rsi-toolkit)<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/ace-rsi-toolkit"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/ace-rsi-toolkit/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/ace-rsi-toolkit"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/ace-rsi-toolkit.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00069 | $0.04016 |
| Opus 5.5 | $0.00028 | $0.01606 |
| Sonnet 5.5 | $0.00014 | $0.00803 |
| Haiku 4.5 | $0.00007 | $0.00402 |
Grade A, and why
ace-rsi-toolkit 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 2d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACE
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models, Zhang et al., 2025 (arXiv:2510.04618v1, §3–4 and Appendix B). GitHub: ace-agent/ace. Verified implementation revision 82709de050e1db6e6ef2f07bcb0393560b94992a, chiefly ace/ace.py (_train_single_sample, _offline_train), ace/core/reflector.py, ace/core/curator.py, ace/prompts/ and playbook_utils.py. This Skill runs the sequential offline algorithm: the Target (the paper's Generator) solves one training case at a time with the current playbook, a Reflector diagnoses that execution, and a Curator adds the lessons the playbook lacks as itemized rules; the final playbook is then measured on the frozen Benchmark. Run the method the way the paper does; every departure is a declared adaptation reported at the end.
Before you start
This Skill needs run_subagent, so it runs in a top-level Session. If the message only names it, ask which agent to train and on which published Benchmark. Otherwise resolve the inputs below and ask only for a missing Target or Benchmark; everything else has a default.
test_agent_id— the Target (the Evaluation Center's Test Agent): an existing agent of this Project, orcreate: <id>when the user wants a fresh one, created with no plugins bypenguin agent create --agent-id <id> --project-id "$PROJECT_ID" --json(an id matches^[a-z][a-z0-9_-]{1,63}$). If that id exists already, stop and ask; never reinitialize an agent. The Target is never the agent you run as: training rewrites its State while you work.benchmark_id— a Benchmark of this Project whosebenchmark_config.tomlstatus is published (not a literaldraftorfailed). Its cases (every directory of it holding astatement/README.md) are both the training samples and the measured case set. Without one, list the published Benchmarks and ask; this Skill never builds one.test_benchmark_id(optional) — a second published Benchmark held out from training: H1 (the initialized State) and the final playbook are measured on it too, and nothing of it reaches the Reflector or the Curator.runs— runs per case in every measurement; default 1.- The runtime — the
provider/model_idpair the user names, else your Environment's, fixed for the whole experiment (references/evaluation.md, controller step 2). The Reflector and the Curator run on your own model; record it. - The settings below. Use the smoke profile unless the user asks for other values. A target score or a round limit is not an ACE setting: say so and leave it out.
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.
- 2d ago First seen · 171 lines · 69 tokens per session scan A 781cad29607c
ace-rsi-toolkit is a skill published in the GitHub repository Prism-Shadow/penguin-harness (2,527 stars, last pushed today), licensed Apache-2.0. It adds 69 tokens to every session and 4,016 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-10-13.
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