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 company-researchgit 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/company-research)<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/company-research"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/company-research/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/company-research"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/company-research.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 | $0.00080 | $0.03266 |
| Opus 5.5 | $0.00032 | $0.01306 |
| Sonnet 5.5 | $0.00016 | $0.00653 |
| Haiku 4.5 | $0.00008 | $0.00327 |
Grade A, and why
company-research 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 5d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Research
A research organization produces claims — a number on a metric, a method that beats a baseline, a paper — and a claim is worth exactly what survives an attempt to break it. This skill is what the researchers and the reviewers of such an organization add to company-employee: an experiment loop shaped after autoresearch, a resource envelope the board approves before the loop starts, and a review that is adversarial by design because the reviewer is never the author. The CEO's checklist in company-ceo still applies; this skill says what the tickets of a research stream look like inside it.
Before you start
If the message only names this skill (e.g. "use company-research skill") without a concrete request, ask what is wanted — an experiment loop to set up, a run to continue, a claim to review, a review to answer. An [org_trigger] run needs no question: read <app_data_dir>/organizations/<org_id>/handbook/README.md and act on what the block says.
When this applies
The mission asks for research: experiments to run, results to claim, papers to write. Two roles use this skill:
- Authors — the researchers and engineers who own experiment and paper tickets, run the loop and write the claims;
- Reviewers — employees the CEO hires for reviewing alone. A reviewer authors nothing it reviews, and an author never reviews its own claim. The CEO of a research organization hires at least one dedicated reviewer in its first plan, and a reviewer's brief names the streams it reviews.
Both are ordinary employees: the desk schedules, ticket sessions do the work, the handbook comes first. A researcher's or reviewer's brief (agent_state/AGENTS.md) names company-research beside company-employee.
The resource envelope comes first
An experiment loop is the textbook case of what company-employee calls heavy or long compute, so it never starts on your own decision. Before the first experiment of a ticket, ask the board — the organization's creator, @user:<id> from created_by in org_config.toml — in the all-hands channel for the envelope, in one message:
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.
- 5d ago Changed · +1 lines ebfccb2596e8
- 28d ago First seen · 116 lines · 80 tokens per session scan A b8aa7294ba3c
company-research is a skill published in the GitHub repository Prism-Shadow/penguin-harness (2,549 stars, last pushed today), licensed Apache-2.0. It adds 80 tokens to every session and 3,266 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 2026-09-18.
Other skills, from other repositories
Jupyter Live Kernel
Guides notebook-first analysis with reproducible kernels, inspectable data loading, and explicit promotion paths back into durable code.
arXiv
Search, filter, and summarize arXiv papers with explicit titles, authors, dates, and paper links before drawing conclusions.
source-verify
A source-checking workflow for confirming that medical information comes from an authoritative, complete, and currently valid document.
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
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.