building-neural-networks

A model-agnostic agent-skills platform.

审核状态：已审核Quality 72Security 82

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项目 README

来源文件：README.md

抓取于 2026年8月27日

Tons of Skills

A model-agnostic agent-skills platform. The canonical layer is harness-free by construction; Claude Code is currently the verified-native harness. Other harnesses remain engineering candidates until their native-path integration is verified; source research alone is never presented as public support.

Version semantics: the release badge is this marketplace's display version. npm packages, including the ccpi CLI and publishable plugins, retain their own package versions; they are intentionally not expected to equal the display version. The version-surface checker governs the display surfaces without rewriting package semver.

Install

Inside Claude Code, one command installs the whole marketplace:

/plugin marketplace add jeremylongshore/claude-code-plugins

Or use the CLI:

pnpm add -g @intentsolutionsio/ccpi
ccpi install devops-automation-pack

Browse the marketplace · Explore plugins · Download bundles

Killer Skill of the Week — no-ai-slop by Peter Yang

Strip AI slop from any draft — named-pattern edits that keep the writer's real voice

no-ai-slop does two jobs and refuses to fake a third. In Edit mode it makes the minimum effective edit — cutting throat-clearing, weak verbs, and abstract nouns while deliberately preserving the writer's cadence, bluntness, humor, and honest admissions, so a rough draft still sounds like the same person afterward. In Detect mode it names each AI-slop pattern it finds, quotes the offending line, and gives the fix in a few words — and pointedly does NOT score the draft or guess whether an AI wrote it. That restraint is the whole point: AI detectors guess; named patterns are evidence the reader can check. MIT-licensed, single focused skill, actively maintained by Peter Yang.

"AI detectors guess. Named patterns are evidence the user can check." — Peter Yang

Grade: A | Week of July 22, 2026 (W30) | View on GitHub

Previous picks: tonone, mnemos, databricks-pack, kobiton-automate, skyvern, code-cleanup, web-analytics, token-optimizer, executive-assistant-skills, skill-creator, cursor-pack, crypto-portfolio-tracker. See all at tonsofskills.com.

Scale, labeled

Every number below names the cohort it counts and the command that reproduces it — an unlabeled count is how a corpus ends up with five contradictory answers to "how many skills."

Count	Cohort	Reproduce with
442	catalog plugins (catalog-entry cohort)	`node scripts/generate-readme-toc.mjs` over `marketplace.extended.json`
3,067	marketplace-visible skills (distinct)	`node -e "import('./scripts/corpus-resolver.mjs').then(m=>console.log(m.resolveCorpus('marketplace-visible').length))"`
347	agent definitions in plugins	`git ls-files 'plugins/*' \| grep '/agents/.\.md'`
19	plugin categories	`ls -d plugins/*/`

📦 Live npm Downloads

Across 396 published packages in the claude-code-plugins namespace. Updated daily by GitHub Actions.

Window	All packages	Established (>30d)
Last 24 hours	962	962
Last 7 days	2,920	2,916
Last 30 days	12,868	12,779

_{"Established" excludes packages first published within the last 30 days, so a bulk-publish event doesn't dominate the headline.}

Top 10 by last 30 days:

#	Package	Last 30d
1	`@intentsolutionsio/openrouter-pack`	556
2	`@intentsolutionsio/groq-pack`	496
3	`@intentsolutionsio/databricks-pack`	274
4	`@intentsolutionsio/clickhouse-pack`	273
5	`@intentsolutionsio/wallet-security-auditor`	263
6	`@intentsolutionsio/notion-pack`	258
7	`@intentsolutionsio/elevenlabs-pack`	244
8	`@intentsolutionsio/freshie-inventory-manager`	214
9	`@intentsolutionsio/supabase-pack`	210
10	`@intentsolutionsio/agency-os`	204

_{Last refreshed 2026-08-19T03:03:05.709Z.}

Ways in

Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:

By category — the table below, regenerated from the catalog on every sync.
By plugin — tonsofskills.com/explore, the full browsable catalog.
By skill — tonsofskills.com/skills, searchable across the whole corpus.
By job to be done — tonsofskills.com/cowork, curated bundles as one-click downloads.
By certification tier — the Certification section below, rendered from the live report.

Browse by category

The 19 categories below link into the live marketplace. Plugin counts are the catalog-entry cohort — regenerated from marketplace.extended.json by this generator; the catalog itself lives on tonsofskills.com, never in this file (§ 6A of the platform blueprint).

	Category	Plugins
🤖	AI & Machine Learning	36
🎭	AI Agents & Agency	10
🔌	API Development	26
💼	Business Tools	6
👥	Community	21
₿	Crypto & Web3	27
💾	Database	26
🎨	Design	2
🔧	DevOps & Infrastructure	36
📚	Examples & Templates	5
🧩	MCP Servers	16
📦	Packages	5
⚡	Performance	25
✅	Productivity	30
🎁	SaaS Skill Packs	106
🔐	Security	27
✨	Skill Enhancers	9
🧪	Testing	28
📁	Analytics	1

What the classes mean

Four artifact classes live in this repository, distinguished on sight and never blurred — provenance is a truth requirement here, not a UX nicety:

Class	What it is	How the reader can tell
Canonical skill	First-party, harness-free, the source of truth	No `.source.json` in its plugin directory
Generated adapter	A thin, machine-produced harness projection	Lives under a generated path with a "generated — do not edit" header
First-party package	An Intent Solutions distribution (npm, cowork zip)	`@intentsolutionsio` scope, IS-authored license
Upstream mirror	Somebody else's work, hosted mirror-by-default	`.source.json` present — upstream author, license, and pinned commit recorded

Certification

Not yet certified. The certification program (tiers T0–T4 with retained, hash-matched evidence) is a later epic of the platform blueprint; until its report exists, no artifact on this surface claims a tier. This line is rendered from the absence of certification-report.json — honestly, not cosmetically.

Contribute

Start with the contribution guide, then the intake and review standards every submission passes through:

Contribution requirements — including the AI-assistance disclosure expectation.
Skill submission intake standard — the tiered document matrix new plugins ship with.
External-PR review standard — how maintainers triage and what gets a submission merged.

Governance

STANDARDS.md — the public spec posture and the canonical-documents index (one owner per fact class).
The platform master blueprint — CI gates, release, docs governance, and this README's own landing contract.
GOVERNANCE.md · SECURITY.md · LICENSE

Provenance

External plugins are hosted mirror-by-default: the contributor's repository stays the source of truth, every mirrored source is pinned in a content lockfile, and upstream credit — author, license, resolved commit — is recorded in the mirror itself. Improvements flow by upstreaming to the author's repository, never by silently editing the mirror. The full decision record is the external-sync model.

License

MIT for the repository scaffolding and first-party tooling; each plugin carries its own license in its manifest, and mirrored plugins keep their upstream license verbatim.

Agent / MCP / Skill 创作

低风险

来源需自行核对维护者身份。
包含脚本或命令调用，安装前请复核。
可能需要外部 token、网络权限或第三方服务。
未检测到高风险命令。
扫描发现：0 条。

Codex — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
将 "skills/.curated/building-neural-networks" 文件夹复制到 Codex 的 skills 目录中。
重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Codex 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
将 "skills/.curated/building-neural-networks" 文件夹复制到 Claude Code 的 skills 目录中。
重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Claude Code 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
将 "skills/.curated/building-neural-networks" 文件夹复制到 Cursor 的 skills 目录中。
重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Cursor 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
将 "skills/.curated/building-neural-networks" 文件夹复制到 GitHub Copilot 的 skills 目录中。
重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 GitHub Copilot 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

安装前请先查看来源仓库和风险报告。
克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
将 "skills/.curated/building-neural-networks" 文件夹复制到 Windsurf 的 skills 目录中。
重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

安装前请先查看来源仓库和风险报告。
从源仓库下载 SKILL.md 及相关文件。
在 Windsurf 的 skills 目录中创建新文件夹。
将所有 skill 文件复制到新文件夹中。
重启 Windsurf 让新的 skill 生效。

查看 SKILL.md 原文

name: building-neural-networks
description: 'Execute this skill allows AI assistant to construct and configure neural
  network architectures using the neural-network-builder plugin. it should be used
  when the user requests the creation of a new neural network, modification of an
  existing one, or assistance... Use when appropriate context detected. Trigger with
  relevant phrases based on skill purpose.

  '
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.20.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
tags:
- ai
- neural-networks
compatibility: Designed for Claude Code

Neural Network Builder

Design and construct neural network architectures with configurable layers, activation functions, and training parameters for tasks like classification, generation, and sequence modeling.

Overview

This skill empowers Claude to design and implement neural networks tailored to specific tasks. It leverages the neural-network-builder plugin to automate the process of defining network architectures, configuring layers, and setting training parameters. This ensures efficient and accurate creation of neural network models.

How It Works

Analyzing Requirements: Claude analyzes the user's request to understand the desired neural network architecture, task, and performance goals.
Generating Configuration: Based on the analysis, Claude generates the appropriate configuration for the neural-network-builder plugin, specifying the layers, activation functions, and other relevant parameters.
Executing Build: Claude executes the build-nn command, triggering the neural-network-builder plugin to construct the neural network based on the generated configuration.

When to Use This Skill

This skill activates when you need to:

Create a new neural network architecture for a specific machine learning task.
Modify an existing neural network's layers, parameters, or training process.
Design a neural network using specific layer types, such as convolutional, recurrent, or transformer layers.

Examples

Example 1: Image Classification

User request: "Build a convolutional neural network for image classification with three convolutional layers and two fully connected layers."

The skill will:

Analyze the request and determine the required CNN architecture.
Generate the configuration for the build-nn command, specifying the layer types, filter sizes, and activation functions.

Example 2: Text Generation

User request: "Define an RNN architecture for text generation with LSTM cells and an embedding layer."