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 agentmods add skills/prism-shadow/penguin-harness/agent-initializationnpx skills add Prism-Shadow/penguin-harness --skill agent-initializationgit 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/agent-initialization)<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/agent-initialization"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/agent-initialization.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.01761 |
| Opus 5 | $0.00015 | $0.00881 |
| Sonnet 5 | $0.00006 | $0.00352 |
| Haiku 4.5 | $0.00003 | $0.00176 |
Grade A, and why
agent-initialization 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 4d 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.
Write skills yourself, or fetch existing ones from the internet with shell commands (`curl`, `git clone`) and place them under `skills/`. Anything fetched from the internet must be read in full and reviewed before instal How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Initialization
This skill initializes an agent's settings from a user requirement — plain files in the target agent's directory.
Before you start
If the user's message only invokes this skill (e.g. "use agent-initialization skill") without a concrete requirement, ask the user what agent they want and what it should do. But when the requirement is already concrete — even a single sentence like "an expert that answers questions about X" — do not ask follow-up questions: derive the role and rules from that sentence, apply the defaults below, and list your assumptions in the final reply.
Resolve the inherited runtime
Treat the current Agent as the Builder. Resolve the runtime before creating a new Agent:
providerandmodel_idare one complete pair. If the user explicitly supplies both, use that pair. If the user supplies neither, inherit the current Builder Session'sProviderandModel IDfrom the Environment. Reject a half pair.thinking_levelis independent. If the user explicitly supplies it, use that value. Otherwise readmodel.thinking_levelfrom the Builder's ownagent_state/system_config.yaml; when the field is absent, use the normal Agent-config defaultmedium.
Write the resolved thinking_level into a brand-new target Agent's model.thinking_level, preserving all other copied model fields. Penguin does not persist provider or model_id in Agent State, so never add either field to system_config.yaml. When the same request continues into Benchmark design, carry the resolved model pair forward explicitly so evaluation uses the Builder runtime instead of a Project default. When configuring an existing Agent, change model.thinking_level only when the user explicitly requests that runtime change.
Locate the target agent
All agents of this project live side by side under agents/ in the App Data Dir:
APP_DATA_DIR="<app_data_dir>" # the App Data Dir value from your Environment section
ls "$APP_DATA_DIR/agents" # existing agents (each is a folder here)
TARGET="$APP_DATA_DIR/agents/<agent_id>" # the agent to configure
What ships with it
1 file 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.
- 4d ago First seen · 108 lines · 30 tokens per session scan A 763d92ed2f4b
agent-initialization is a skill published in the GitHub repository Prism-Shadow/penguin-harness (1,887 stars, last pushed 2d ago), licensed Apache-2.0. It adds 30 tokens to every session and 1,761 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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