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 data-analysisgit 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/data-analysis)<a href="https://agentmods.dev/skills/prism-shadow/penguin-harness/data-analysis"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/data-analysis/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/data-analysis"><img src="https://agentmods.dev/badge/skills/prism-shadow/penguin-harness/data-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00027 | $0.00721 |
| Opus 5.5 | $0.00011 | $0.00288 |
| Sonnet 5.5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
data-analysis 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 9d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
Deliver the requested result and artifacts. Do not turn the task into a proof exercise or add evidence, reports, explanations, or intermediate files that were not requested.
Before you start
Require a concrete data-analysis task, its available inputs, and the requested deliverable, location, and format. Ask only when missing information prevents a defensible result and would materially change the deliverable; otherwise proceed.
Contract
Read the task, supplied inputs, and relevant data documentation. Identify every required output path and format, plus only the definitions that can change the result: scope, observation grain, keys, units, operators, ordering, coverage, and explicit formatting rules. Treat examples as illustrative unless the task makes them normative.
If information is incomplete or ambiguous, first resolve it from the supplied materials. Ask only when the missing choice prevents a defensible result and would materially change the deliverable. Otherwise choose the best-supported interpretation and proceed.
Bounded inspection
For large or unfamiliar inputs, begin with a bounded inventory, schema check, targeted sample, or narrow query. Expand inspection only when it can change a selection, transformation, calculation, or output. Do not exhaustively read or render data merely to increase confidence.
Data semantics
Compute at the correct row or entity grain. Evaluate conjunctive conditions on the same record or entity; do not replace row-level matching with unions of separate field values. Preserve nulls, exclusions, and explicit prohibitions. Enumerated outputs must cover the complete requested universe.
Ground answer-changing choices in the task and supplied data. Preserve documented source semantics, units, mappings, and native workflow behavior when they define the requested result. Do not reproduce an apparent source or tool defect merely for consistency. When plausible methods disagree, compare only the smallest answer-changing difference, choose the best-supported method, and use it consistently.
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.
- 9d ago Changed · +1 lines 519713281a7c
- 1mo ago Changed · -4 lines 3166fa103bb2
- 1mo ago First seen · 87 lines · 27 tokens per session scan A 63ab40c6dd02
data-analysis is a skill published in the GitHub repository Prism-Shadow/penguin-harness (2,862 stars, last pushed today), licensed Apache-2.0. It adds 27 tokens to every session and 721 once invoked, about $0.0001 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 2026-08-30.
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