Agent skill

Penguin Config

by Prism-Shadow in Prism-Shadow/penguin-harness

Manage model API keys, default models and per-agent vault secrets with the penguin CLI.

Apache-2.0Auto-check passedDevOps & Cloud

Install Penguin Config

skills CLI
$ npx skills add Prism-Shadow/penguin-harness --skill penguin-config -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Prism-Shadow/penguin-harness penguin-config --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Prism-Shadow/penguin-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/agent-development/skills/penguin-config .claude/skills/penguin-config && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
penguin-config
GitHub stars
2.5k
Token cost
~2.3k tokens
SKILL.md length
1,079 words
Files
1
Skills in repo
31
Repo updated
First seen
Licence
Apache-2.0

At a glance

Manage model API keys, default models and per-agent vault secrets with the penguin CLI.

  • Tasks that involve Secrets management
  • SKILL.md covers Before you start, Models, Group connection and Vault (per-agent secrets), plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Penguin Config is an agent skill from Prism-Shadow/penguin-harness. Manage model API keys, default models and per-agent vault secrets with the penguin CLI.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Secrets management. The repository describes itself as: 🐧 Unified and Stable RSI Platform. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Secrets management

Example prompts

  • “/penguin-config”

What it can do on your machine

Read from SKILL.md and the folder at commit d56d9ce. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Penguin Config loads about 2.3k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 1,079 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Prism-Shadow/penguin-harness at commit d56d9ce, republished under its Apache-2.0 licence (© Prism-Shadow). 1,079 words, ~2,328 tokens.

Download SKILL.mdSave it as .claude/skills/penguin-config/SKILL.md (or your agent's skills folder).
name
penguin-config
description
Manage model API keys, default models and per-agent vault secrets with the penguin CLI.

Penguin Config (CLI)

The penguin CLI manages model credentials, default models and per-agent vault secrets. Its primary job is model configuration: penguin config model add registers a model, or — without --model-id — gives a whole group its key, base URL or protocol, and penguin config model list shows the groups' connections and the models currently available. Configuration goes through the CLI only — never read or hand-edit the underlying hidden files.

Before you start

If the user's message only invokes this skill (e.g. "use penguin-cli skill") without a concrete request, ask the user what they want to configure. Do not run any command until the goal is clear.

Models

Add or update a model (upsert by the (provider, model_id) pair; re-run with more options to amend an entry):

bash
penguin config model add --provider <group> --model-id <upstream_id> [--api-key <key> | --clear-api-key] \
  [--base-url <url> | --clear-base-url] [--client-type <type> | --clear-client-type] \
  [--context-window <n>] [--max-tokens <n>] [--vision | --no-vision] [--fast-mode | --no-fast-mode] \
  [--price-cache-read <n>] [--price-cache-write <n>] [--price-output <n>] \
  [--project-id <id>] [--root <dir>] [--set-default]
  • A model is identified by the (provider, model_id) pair, so --provider and --model-id are both required to name one — the group is never inferred from the model id, because gateways resell vendor models under their upstream ids and a wrong guess would send the key to another vendor's endpoint. --model-id takes the provider's upstream model id (what the API expects) and is persisted as the entry's request id, so it reaches the API unchanged; --provider names the group (deepseek, openai, anthropic, google, openrouter, siliconflow, … — custom for any other endpoint).
  • Only custom, vllm, openrouter, tokendance, siliconflow and groups of your own take models added by hand. Every other built-in group carries its catalog models only: model add there accepts one of that group's catalog model ids (to set its key, limits or prices, or to bring it back) and refuses any other id with "cannot be added". Add such a model under custom with --base-url, or under a group of your own.
  • --api-key, --base-url and --client-type set the model's own values, and --clear-api-key, --clear-base-url and --clear-client-type remove them. Omitted, the model follows its group (see Group connection), and with no group value it gets the client's default; the built-in catalog is never consulted. A model added to openrouter, tokendance or siliconflow needs only --provider and --model-id once the group has a key: a new Project already stores those gateways' endpoint and protocol on the group. For an OpenAI chat-completion compatible endpoint under custom or a group of your own, use --client-type openai --base-url <endpoint>, or set both once on the group; in a vendor group, omit --client-type to auto-route by model id.
  • Prices are USD per million tokens (cache read / cache write / output).
  • --vision / --no-vision mark whether the model accepts images; omitting both keeps the current value (default is vision-capable).
  • --max-tokens <n> pins a per-model output cap (positive integer), overriding the Agent's model.max_tokens; omit to inherit. Lower it for small-context models — the per-Agent default (32000) cannot fit into e.g. a 32k context window together with any prompt.
  • All penguin config model ... and penguin config vault ... commands accept --root <dir> to target another data root (default PENGUIN_HOME, then ~/.penguin/data). Two configuration targets — treat the difference as a hard rule:
    • Penguin's own model (self-configuration: the model Penguin itself runs on): pass --root <data root> as well — the parent directory of the App Data Dir named in your Environment section. An agent running inside PenguinHarness never sees PENGUIN_HOME: the harness strips every PENGUIN_* variable from a command's environment, so a command without --root falls back to ~/.penguin/data and configures a root the running server does not read.
    • An AI app you are building: --root must point at the app's own data directory inside the project (e.g. --root ./penguin_data, the same path the app gives createAgent({ root })) unless the user explicitly chose another location — never write an app's models or keys into the global ~/.penguin/data, which belongs to the person running Penguin, not to the app.
    • While developing an app, review regularly: penguin config model list --root <app root> should show the app's entries, and the global list (no --root) should stay clean.

Other model commands:

bash
penguin config model default --model-id <upstream_id> --provider <group> [--root <dir>]   # set the project default model
penguin config model vision --model-id <upstream_id> --provider <group> [--root <dir>]    # set the project vision model (reads images for text-only sessions)
penguin config model list [--root <dir>]                      # the groups' connections, then the models with the connection each uses; api_key masked, (provider) = from the group, (env) = from an environment variable
penguin config model remove --model-id <upstream_id> --provider <group> [--root <dir>]   # remove one model
Show full SKILL.md (432 more words)Show less

Group connection

A group holds one connection — API key, base URL, protocol — in its [providers.<group>] table, and every model in it without a value of its own follows it. Each field resolves on its own from the config file alone: the model's value, else the group's, else nothing (the client's default endpoint, MMSP routing by model id, the environment key where allowed). The built-in catalog is only a reference: a new Project's file already stores each gateway's endpoint and protocol on its group. Set a key once on the group rather than on every model. model add and model remove without --model-id act on the group:

bash
penguin config model add --provider <group> [--api-key <key> | --clear-api-key] [--base-url <url> | --clear-base-url] \
  [--client-type <type> | --clear-client-type] [--project-id <id>] [--root <dir>]
penguin config model remove --provider <group> [--project-id <id>] [--root <dir>]   # removes the group's connection only; its models stay
  • A field you do not name is left as it is. An empty value is refused: remove a field with its --clear-* flag.
  • The flags that describe one model (--context-window, --max-tokens, --vision, --fast-mode, --price-*, --set-default) are refused without --model-id; nothing is written.
  • Any group takes a protocol, penguin-go and opencode-go included: a model's own protocol always wins over its group's.
  • The group key never replaces a key set on one model with model add --api-key; the command says how many models keep their own. It reaches only the models that go to the group's endpoint: a model whose own --base-url is on another host (scheme, host and port) needs its own --api-key.
  • For a group of your own on one endpoint, set its connection first, then add each model with only --provider and --model-id: the models store nothing and follow the group. Removing the group's last model removes its connection too.

Vault (per-agent secrets)

The vault holds an agent's environment-variable secrets (third-party API keys etc.); values are injected into that agent's shell subprocesses:

bash
penguin config vault set --key <NAME> --value <value> [--project-id <id>] [--agent-id <id>] [--root <dir>]
penguin config vault list [--project-id <id>] [--agent-id <id>] [--root <dir>]      # values are shown masked
penguin config vault remove --key <NAME> [--project-id <id>] [--agent-id <id>] [--root <dir>]
  • --project-id defaults to default_project, --agent-id to default_agent.
  • Key names follow shell variable rules (letter or underscore first, then letters, digits and underscores); values are limited to 8192 characters.

Language

bash
penguin config lang <en|zh>   # persist the CLI language via PENGUIN_LANG in your shell rc

Running agents

penguin run -m "<task>" [--provider <group> --model-id <id>] [--agent-id <id>] [--workspace <path>] [--approve <mode>] runs one task; penguin chat [--resume [session_id]] starts or resumes an interactive chat with the same options. The model reference stays a pair here too: pass --provider and --model-id together, or neither to run on the project's default model — one without the other is rejected.

Storage

Paths use <app_data_dir>, the App Data Dir value from your Environment section.

  • <app_data_dir>/.project_config.toml — the project's single hidden config file: model list, settings, each group's connection ([providers.<group>]) and the values a model overrides (api_key etc. on its entry). Configuration is CLI-only — never read, print or hand-edit this file.
  • <app_data_dir>/agents/<agent_id>/agent_state/.vault.toml — that agent's vault entries, hidden file; same rule, manage it with penguin config vault.

© Prism-Shadow, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/agent-development/skills/penguin-config of Prism-Shadow/penguin-harness.

Open the folder on GitHubat commit d56d9ce

Compare with similar skills

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Penguin Config compared with similar skills
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LangBot Deployment Guidelangbot-app/LangBot18k—~1.2kAutomated safety check: NotesApache-2.0
Env Var Conventionssgl-project/sglang37k2 repos~2.9kAutomated safety check: PassApache-2.0
Mac Fleet Maintenancesteipete/agent-scripts7.3k—~4.8kAutomated safety check: PassMIT
Add Config Env Varbaserow/baserow6.1k—~1.1kAutomated safety check: PassCustom licence

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Categories

Questions about Penguin Config

What does Penguin Config do?

Manage model API keys, default models and per-agent vault secrets with the penguin CLI. Penguin Config is an agent skill from Prism-Shadow/penguin-harness. Manage model API keys, default models and per-agent vault secrets with the penguin CLI.

When should I use Penguin Config?

Penguin Config fits situations like: tasks that involve Secrets management.

How do I install Penguin Config in Claude Code?

Run `npx skills add Prism-Shadow/penguin-harness --skill penguin-config -a claude-code`. Or copy the skill folder (plugins/agent-development/skills/penguin-config in Prism-Shadow/penguin-harness) into .claude/skills/penguin-config in your project. Claude Code loads it when a task matches its description.

How do I install Penguin Config in Codex?

Run `npx skills add Prism-Shadow/penguin-harness --skill penguin-config -a codex`. Or copy the skill folder (plugins/agent-development/skills/penguin-config in Prism-Shadow/penguin-harness) into .agents/skills/penguin-config in your project. Codex loads it when a task matches its description.

Can I use Penguin Config in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Prism-Shadow/penguin-harness --skill penguin-config -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/penguin-config, .gemini/skills/penguin-config, .github/skills/penguin-config and .opencode/skills/penguin-config in your project.

What does Penguin Config need to run?

SKILL.md names no scripts, command-line tools or credentials: Penguin Config is instructions for the agent only.

Does Penguin Config access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Penguin Config safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Penguin Config use?

Penguin Config is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Penguin Config use?

About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Penguin Config?

Skills that share tags, products or a category with Penguin Config: Iron Proxy Gateway for NanoClaw (nanocoai/nanoclaw, 31k stars), LangBot Deployment Guide (langbot-app/LangBot, 18k stars), Env Var Conventions (sgl-project/sglang, 37k stars) and Mac Fleet Maintenance (steipete/agent-scripts, 7.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Penguin Config?

Prism-Shadow (a GitHub organization) maintains it in Prism-Shadow/penguin-harness, which has 2,464 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on October 7, 2026.

Source: Prism-Shadow/penguin-harness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.