penguin-harness/packages/docs/content/skills.en.md

title, description

title	description
Skills	Skills package reusable instructions as directories with a SKILL.md — metadata up front, body on demand, editable by the Agent itself.

Anatomy of a Skill

A Skill is a directory containing a SKILL.md, optionally with a custom icon.svg. The directory name is the authoritative skill name and must match ^[A-Za-z0-9_-]+$; a name in the frontmatter is overridden by it.

Frontmatter fields:

Field	Meaning
name	Skill name, matching the directory name
description	English one-liner injected into the system prompt
short_description / short_description_zh	UI labels for compact spots such as cards; not injected into the prompt
version	Natural-number version, default 1
updated	Update date

---
name: my-skill
description: One-line English description injected into the system prompt.
short_description: Short UI label.
short_description_zh: 简短的中文标签。
version: 1
updated: 2026-07-17
---

# My Skill

Concrete steps, boundaries and acceptance criteria...

Parsing is tolerant: only key: value scalar lines inside the first --- block are recognized; a version that is not a natural number falls back to 1, and a missing updated defaults to empty.

Progressive loading

Skills follow an "index first, body on demand" design: the system prompt injects only each installed Skill's metadata (name + description) through the {{SKILL_METADATA}} placeholder, and instructs the model to read the matching SKILL.md in full via the shell before following it. There is no dedicated skill tool — reading the body is just one read_file or shell call (see Tools & Approval).

Chat can also pin skills explicitly: the message then starts with a [use_skills] block listing the skill names (the earlier <use_skills> form is still recognized when re-rendering old Traces).

If a message only names a skill without a concrete task, the model is instructed to ask what is needed before starting.

Installation and storage

Installed Skills live under agent_state/skills/<name>/ inside the Agent State. The files are the source of truth: every read goes straight to disk with no cache, which makes Skills naturally editable.

• The built-in Agent default_agent gets the whole library installed at initialization;
• other Agents install on demand — through the Web UI's Skill library page, or via the SDK;
• installing writes the library SKILL.md verbatim (frontmatter included) and copies any icon.svg alongside it.

The library ships as the npm package @prismshadow/penguin-skills, carrying the raw skills/ directory in the tarball; at runtime the package's skills/<name>/SKILL.md files are likewise the source of truth for library content.

Built-in library

The built-in Skills, by group (the group manifest is SKILL_GROUPS in packages/skills/src/index.ts; the library directory is the source of truth as Skills are added):

Group	Skill	Purpose
Office Productivity	data-analysis	Complete data-analysis tasks with bounded evidence inspection, explicit answer-changing decisions, native artifact handling and final output verification
	firecrawl	Web search and page scraping into clean markdown via the Firecrawl API
	bento-slides	Author and edit Bento presentations: single-file .bento.html decks whose document is JSON, mapping material to charts, morph transitions and state slides
Software Development	web-design	Penguin visual language for generated web pages and app UIs: design tokens, components, light/dark themes and chat layouts
	software-engineering	Complete software-engineering tasks: investigate and review code, implement fixes, features and refactors with minimal scope, validate changes, and report verified outcomes
AI App Development	penguin-sdk	Build AI and RAG apps on the SDK: the createSession/run streaming loop plus a complete retrieval recipe with chunk-revealing citations
	penguin-cli	Manage model API keys, default models and per-agent Vault secrets with the penguin CLI
	agenthub-models	Call model APIs through @prismshadow/agenthub: streaming text, image generation, speech synthesis and embeddings
	vllm	Deploy and serve LLMs with vLLM behind an OpenAI-compatible endpoint, with tool calling enabled for agent workloads
	ollama	Deploy and serve local models with Ollama: pull and run them, then expose the OpenAI-compatible endpoint to apps and agents
	llamafactory	Fine-tune LLMs with LlamaFactory: register datasets, train via YAML configs, merge LoRA adapters and serve the result
Agent Tuning	agent-creation	Turn a user requirement into a concrete agent: write the target agent's AGENTS.md and install the skills it needs
	benchmark-design	Design and calibrate a multi-Case capability Benchmark with repeated independent evaluations and a traceable baseline
	agent-evaluation	Run and score exactly one Benchmark Case run, with CLI execution, Trace provenance checks and private Rubric isolation
	agent-optimization	Improve an Agent State from direct feedback or versioned multi-Case Benchmark scores and score-linked Traces

Writing and optimizing Skills

• Manual install: create a directory under agent_state/skills/<name>/ and write a SKILL.md; the system scans skills/ when assembling the system prompt and injects the metadata. A directory without a SKILL.md does not count as a Skill.
• Uninstalling deletes the whole skills/<name>/ directory and is idempotent.
• An Agent can rewrite its own SKILL.md as part of a task — combined with Benchmark evaluation and optimization this closes the improvement loop, see Self-Improvement.