genpark-ai-discovery

genpark-ai-discovery is a skill for Claude Code, Codex from alphaparkinc/openclaw-skills. It costs 114 tokens per session (1,110 once invoked), scanned A, original, no licence file.

A skill for finding newly launched or currently popular AI tools on GenPark’s AI Tools hub, a website that lists AI applications.

In plain words
What is it for?
Discovering new AI tools, recently launched apps, and weekly trending tools on GenPark.
Why use it?
It helps avoid manually checking the hub for recent, new, or trending tools.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Discovering new AI tools, recently launched apps, and weekly trending tools on GenPark.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/alphaparkinc/openclaw-skills/genpark-ai-discovery
Install

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.

Any agent
npx skills add alphaparkinc/openclaw-skills --skill genpark-ai-discovery
Clone the repo
git clone --depth 1 https://github.com/alphaparkinc/openclaw-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for genpark-ai-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/alphaparkinc/openclaw-skills/genpark-ai-discovery/github.svg)](https://agentmods.dev/skills/alphaparkinc/openclaw-skills/genpark-ai-discovery)
Your own site
<a href="https://agentmods.dev/skills/alphaparkinc/openclaw-skills/genpark-ai-discovery"><img src="https://agentmods.dev/badge/skills/alphaparkinc/openclaw-skills/genpark-ai-discovery/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.

agentmods 80×15 button for genpark-ai-discovery

Your own site · 80×15
<a href="https://agentmods.dev/skills/alphaparkinc/openclaw-skills/genpark-ai-discovery"><img src="https://agentmods.dev/badge/skills/alphaparkinc/openclaw-skills/genpark-ai-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 114 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,110 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00114 $0.01110
Opus 5.5 $0.00046 $0.00444
Sonnet 5.5 $0.00023 $0.00222
Haiku 4.5 $0.00011 $0.00111

Measured 1mo ago against content hash cf1ef5c7fd75, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

genpark-ai-discovery 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 1mo 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.

genpark-ai-discovery/SKILL.md · 104 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 1mo ago First seen · 104 lines · 114 tokens per session scan A cf1ef5c7fd75

Subscribe to this mod's changes

genpark-ai-discovery is a skill published in the GitHub repository alphaparkinc/openclaw-skills (9 stars, last pushed 7mo ago), with no licence file. It adds 114 tokens to every session and 1,110 once invoked, about $0.0005 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 2026-08-31.

Related

Other skills, from other repositories

n8n-agents

Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain. AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output…

czlonkowski/n8n-mcp · 156 tokens

solr-semantic-search

To build Solr phrase-tagging semantic search: concept tagging, taxonomy, graph paths.

griddynamics/rosetta · 24 tokens

opik-test

Turn a failing Opik trace (or a described failure) into a repeatable regression check — a test-suite item with the trace's input and one or two binary assertions — so a fix can be verified by the compare skill. Works over the SDK; uses the MCP write tool when connected. Returns the suite, the item, and the assertion.…

comet-ml/opik-mcp · 141 tokens

td-api-reference

MUST READ before writing TD Python via executepython, setdatcontent, or editdatcontent, and before any heavy build. Parameters, storage, operators, referencing, threading, cook model, heavy-build safety caps.

dylanroscover/Embody · 48 tokens

haiku-rag

Search, read and compute over the user's haiku.rag knowledge base through the haiku-rag MCP tools. Use whenever a request could be answered from the user's ingested documents, when asked to find, look up, check or cite something in their documents or knowledge base, or when the question is about the user's own…

ggozad/haiku.rag · 76 tokens

manage-annotations

MUST READ before creating, modifying, or querying annotations. Contains parameter names, coordinate model, and API quirks.

dylanroscover/Embody · 28 tokens