复制安装命令
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A model-agnostic agent-skills platform.
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
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
ccpiCLI 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.
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.
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/*/ |
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:
Last refreshed 2026-08-19T03:03:05.709Z.
Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:
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 |
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 |
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.
Start with the contribution guide, then the intake and review standards every submission passes through:
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.
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.
name: clickhouse-sdk-patterns
description: |
Production-ready patterns for @clickhouse/client — streaming inserts, typed
queries, error handling, and connection management.
Use when building robust ClickHouse integrations, implementing streaming
inserts or low-memory streaming reads, or establishing team coding standards.
Trigger with "clickhouse SDK patterns", "clickhouse client patterns",
"clickhouse best practices", "clickhouse streaming insert".
allowed-tools: Read
version: 1.7.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- database
- analytics
- clickhouse
- olap
compatibility: Designed for Claude CodeProduction patterns for @clickhouse/client — typed queries, streaming inserts,
error handling, and connection lifecycle management. Start from the typed query
helper below, then drill into references/implementation.md for the streaming,
batching, and lifecycle patterns.
@clickhouse/client installed and authenticated (see clickhouse-install-auth)CLICKHOUSE_HOST / CLICKHOUSE_USER / CLICKHOUSE_PASSWORD env setdrain, Readable)Apply the pattern that fits your workload. Steps 2–7 live in references/implementation.md with full, copy-pasteable code; the core typed-query skeleton stays here.
query<T> wrapper that returns parsed rows (skeleton below).Readable instead of buffering in memory; honor drain.{ inserted, errors }.AsyncGenerator so you never load the full set into RAM.ClickHouseError (code + message)
from network/client errors and normalize into a structured result.SIGTERM via
client.close(); expose a ping()-based health check.max_threads, max_memory_usage,
max_execution_time, and max_result_rows for heavy queries.import { createClient } from '@clickhouse/client';
const client = createClient({
url: process.env.CLICKHOUSE_HOST!,
username: process.env.CLICKHOUSE_USER ?? 'default',
password: process.env.CLICKHOUSE_PASSWORD ?? '',
});
// Generic typed query — returns parsed JSON rows
async function query<T>(sql: string, params?: Record<string, unknown>): Promise<T[]> {
const rs = await client.query({
query: sql,
query_params: params,
format: 'JSONEachRow',
});
return rs.json<T>();
}
Note on parameterized queries: ClickHouse uses {name:Type} syntax for
parameters, not $1 or ?. Always use typed parameters to prevent SQL injection.
Applying these patterns produces:
client instance plus a generic query<T> helper that
returns typed, parsed rows.{ inserted: number; errors: Error[] } you can act
on programmatically.CH-<code>: <message> for server-side failures) rather
than raw thrown exceptions.Map common ClickHouse server error codes to a corrective action:
| Error Code | Meaning | Action |
|---|---|---|
SYNTAX_ERROR (62) | Bad SQL | Fix query syntax |
UNKNOWN_TABLE (60) | Table doesn't exist | Check table name, database |
TOO_MANY_SIMULTANEOUS_QUERIES (202) | Connection overload | Reduce concurrency or pool |
MEMORY_LIMIT_EXCEEDED (241) | Query uses too much RAM | Add filters, use streaming |
TIMEOUT_EXCEEDED (159) | Query too slow | Optimize ORDER BY, add indexes |
Full safeQuery wrapper (server-vs-client error discrimination) is in
references/implementation.md under Pattern 5.
Worked, runnable usage of each helper is in references/examples.md. Quick look — a typed aggregation query with named parameters:
interface EventCount {
event_type: string;
cnt: string; // ClickHouse JSON returns numbers as strings
}
const rows = await query<EventCount>(
'SELECT event_type, count() AS cnt FROM events WHERE user_id = {user_id:UInt64} GROUP BY event_type',
{ user_id: 42 }
);
See references/examples.md for streaming reads and structured error-result usage.
Apply these patterns in clickhouse-core-workflow-a for real data modeling, then
tune query cost and concurrency with clickhouse-cost-tuning and
clickhouse-performance-tuning.
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