cohere-hello-world

A model-agnostic agent-skills platform.

审核状态：已审核Quality 72Security 70

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项目 README

来源文件：README.md

抓取于 2026年8月28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.

其他

中风险

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：1 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/cohere-hello-world" 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/cohere-hello-world" 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/cohere-hello-world" 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/cohere-hello-world" 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/cohere-hello-world" 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: cohere-hello-world
description: 'Create a minimal working Cohere example with Chat, Embed, and Rerank.

  Use when starting a new Cohere integration, testing your setup,

  or learning basic Cohere API v2 patterns.

  Trigger with phrases like "cohere hello world", "cohere example",

  "cohere quick start", "simple cohere code".

  '
allowed-tools: Read, Write, Edit
version: 1.5.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- ai
- nlp
- cohere
compatibility: Designed for Claude Code

Cohere Hello World

Overview

Three minimal working examples: Chat completion, text embedding, and search reranking. Each demonstrates a core Cohere API v2 endpoint.

Prerequisites

Completed cohere-install-auth setup
cohere-ai package installed
CO_API_KEY environment variable set

Instructions

Example 1: Chat Completion

import { CohereClientV2 } from 'cohere-ai';

const cohere = new CohereClientV2();

async function chat() {
  const response = await cohere.chat({
    model: 'command-a-03-2025',
    messages: [
      { role: 'system', content: 'You are a helpful coding assistant.' },
      { role: 'user', content: 'Explain what a closure is in JavaScript in 2 sentences.' },
    ],
  });

  console.log(response.message?.content?.[0]?.text);
}

chat().catch(console.error);

Example 2: Text Embedding

async function embed() {
  const response = await cohere.embed({
    model: 'embed-v4.0',
    texts: ['Cohere builds enterprise AI', 'LLMs power modern search'],
    inputType: 'search_document',
    embeddingTypes: ['float'],
  });

  const vectors = response.embeddings.float;
  console.log(`Generated ${vectors.length} embeddings`);
  console.log(`Dimensions: ${vectors[0].length}`);
}

embed().catch(console.error);

Example 3: Search Reranking

async function rerank() {
  const response = await cohere.rerank({
    model: 'rerank-v3.5',
    query: 'What is machine learning?',
    documents: [
      'Machine learning is a subset of artificial intelligence.',
      'The weather today is sunny and warm.',
      'Deep learning uses neural networks with many layers.',
      'I enjoy cooking Italian food on weekends.',
    ],
    topN: 2,
  });

  for (const result of response.results) {
    console.log(`[${result.relevanceScore.toFixed(3)}] ${result.index}`);
  }
}

rerank().catch(console.error);

Example 4: Streaming Chat

async function streamChat() {
  const stream = await cohere.chatStream({
    model: 'command-a-03-2025',
    messages: [
      { role: 'user', content: 'Write a haiku about APIs.' },
    ],
  });

  for await (const event of stream) {
    if (event.type === 'content-delta') {
      process.stdout.write(event.delta?.message?.content?.text ?? '');
    }
  }
  console.log(); // newline
}

streamChat().catch(console.error);

Python Equivalents

import cohere

co = cohere.ClientV2()

# Chat
response = co.chat(
    model="command-a-03-2025",
    messages=[{"role": "user", "content": "Hello, Cohere!"}],
)
print(response.message.content[0].text)

# Embed
response = co.embed(
    model="embed-v4.0",
    texts=["Hello world", "Goodbye world"],
    input_type="search_document",
    embedding_types=["float"],
)
print(f"Vectors: {len(response.embeddings.float)}")

# Rerank
response = co.rerank(
    model="rerank-v3.5",
    query="best programming language",
    documents=["Python is versatile", "Rust is fast", "SQL manages data"],
    top_n=2,
)
for r in response.results:
    print(f"[{r.relevance_score:.3f}] doc {r.index}")

Output

Chat: Text response from Command A model
Embed: Float vectors (1024 dimensions for v4)
Rerank: Sorted documents with relevance scores (0.0-1.0)
Stream: Token-by-token text output via SSE

Error Handling

Error	Cause	Solution
`model is required`	Missing model param	Always pass `model` in API v2
`embedding_types is required`	Missing for embed	Add `embeddingTypes: ['float']`
`invalid api token`	Bad CO_API_KEY	Check key at dashboard.cohere.com
`rate limit exceeded`	Too many trial requests	Wait 60s or upgrade key

Examples

Use a staging key and synthetic input to make one bounded chat request, inspect only the response status and expected shape, then repeat for embed/rerank using approved fixtures. If authentication, model selection, or rate checks fail, stop the walkthrough and repair the scoped configuration before sending user or production data.

Resources

Next Steps

Proceed to cohere-local-dev-loop for development workflow setup.

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