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.
Borrowing it
Nothing to install: this file belongs to Prism-Shadow/penguin-harness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Prism-Shadow/penguin-harness/main/.agents/skills/penguin-harness-writing/SKILL.mdgit 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-harness-writing)<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/penguin-harness-writing"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/penguin-harness-writing/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-harness-writing"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/penguin-harness-writing.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 | $0.00119 | $0.11056 |
| Opus 5.5 | $0.00048 | $0.04422 |
| Sonnet 5.5 | $0.00024 | $0.02211 |
| Haiku 4.5 | $0.00012 | $0.01106 |
Grade A, and why
penguin-harness-writing scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
dark, the references list and a jump to it). `curl` there may need `--noproxy '*'`. How it starts
The opening of the file, as written. The whole thing — 535 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing for penguin.ooo
Posts, docs pages and landing copy are written by a model on the TokenDance gateway and shipped
by agents. A post is written the way a person writes one: an outline first, then the whole post in
one call, so its sections join; the review, the fixes, the cuts and the translation work on the
whole post too, and a round of its own edits only the sentences at the headings. A long post or a docs page can fall back to section mode. This page is the contract; scripts/prose.mjs is
the only way text reaches a draft, and prompts/ plus reference/ hold every word sent to the
model. penguin-harness-dev/reference/authoring.md covers what is true of every post (the
bilingual pair, frontmatter, where screenshots live).
1. Who writes what
The API model writes every word a reader sees: the outline and its plan, every section, the
title and excerpt, every string inside a figure, alt text and captions, the second language, every
docs sentence and landing string, and every later fix or cut. Each is its own API call through
prose.mjs.
Agents never type a sentence into the text. They research, compile FACTS, write prompts and
findings, put the model's text together, draw figures from the model's figure blocks, take
screenshots, run the checks, review blind and ship. Putting the text into the manuscript is also
the agent's job, and it is mechanical: write saves the draft, assemble joins section files in
section mode, and strip publishes them. A wrong sentence goes back through a fix call with a numbered finding,
never through an editor. The only hand edits are mechanical: frontmatter date / category,
screenshot paths and their -en / -zh suffixes, whitespace, the boilerplate version note copied
verbatim from an existing post, and copying landing strings into the dictionaries verbatim.
The key never enters the repo. It sits in a chmod-600 file outside the tree; the script reads
TOKENDANCE_API_KEY or the file named by TOKENDANCE_KEY_FILE, never an argument, and prints
neither. Raw responses (with the model's reasoning) are saved in a scratch directory and never
committed.
What ships with it
32 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.
- prompts/contract-docs.md 1.3 KB
- prompts/contract-landing.md 618 B
- prompts/contract-post.md 2.3 KB
- prompts/contract-section.md 2.1 KB
- prompts/emphasize.system.md 1.6 KB
- prompts/emphasize.user.md 79 B
- prompts/fix.system.md 1.1 KB
- prompts/fix.user.md 97 B
- prompts/fragments.md 14 KB
- prompts/outline.system.md 1.6 KB
- prompts/outline.user.md 1.8 KB
- prompts/polish.system.md 721 B
- prompts/polish.user.md 41 B
- prompts/review.system.md 2.5 KB
- prompts/review.user.md 246 B
- prompts/section.system.md 1.5 KB
- prompts/section.user.md 560 B
- prompts/transition.system.md 1.6 KB
- prompts/transition.user.md 104 B
- prompts/translate.system.md 1.2 KB
- prompts/translate.user.md 132 B
- prompts/trim.system.md 1.9 KB
- prompts/trim.user.md 60 B
- prompts/write.system.md 1.1 KB
- prompts/write.user.md 314 B
- reference/en-style.md 6.3 KB
- reference/profile-blog-perspectives.md 4.2 KB
- reference/profile-blog-practice.md 22 KB
- reference/profile-docs.md 1.8 KB
- reference/profile-landing.md 1.2 KB
- reference/zh-style.md 7.6 KB
- scripts/prose.mjs 324 KB runs code
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.
- 3d ago First seen · 535 lines · 119 tokens per session scan A 16125ec05a14
penguin-harness-writing is a skill published in the GitHub repository Prism-Shadow/penguin-harness (2,975 stars, last pushed today), licensed Apache-2.0. It adds 119 tokens to every session and 11,056 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-24.
Other skills, from other repositories
agentscope-java
Expert Java developer skill for AgentScope Java framework - a reactive, message-driven multi-agent system built on Project Reactor. Use when working with reactive programming, LLM integration, agent orchestration, multi-agent systems, or when the user mentions AgentScope, ReActAgent, Mono/Flux, Project Reactor, or…
apply-patch
Apply multi-file or tricky edits atomically with git apply instead of many fragile editfile calls. Use when changing several files at once or when editfile fails to match.
git-checkpoint
Use git as a safety net - create a checkpoint commit before risky or large changes and roll back cleanly if a change makes things worse. Use before multi-file refactors.
pptagent
Create and revise editable PowerPoint presentations from a brief using HTML slide sources, rendering, and visual review. Use when the requested deliverable is a PPTX deck. The host authors the slides; text-only hosts can use an external visual reviewer.
computer-history
Answer questions about what the user recently did on their computer — who contacted them, what they were working on, where they left off — by reading the local Computer History summaries and raw event streams.
browser-workflow-skill-builder
Create robust browser automation skills for sites like LinkedIn, X/Twitter, YouTube, Stripe, or other logged-in web apps by saving page context, analyzing HTML/CSS, writing skill-local JS extract/verify scripts, and using CSS selector actions with screenshot verification.