复制安装命令
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。
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: apify-prod-checklist
description: |
Production readiness checklist for Apify Actor deployments.
Use when deploying an Actor to production, preparing for launch, or
validating Actor configuration, scheduling, monitoring, and rollback
before going live.
Trigger with "apify production", "deploy actor to prod", "apify go-live",
"apify launch checklist", "actor production ready".
allowed-tools: Read, Bash(apify:*), Bash(curl:*), Bash(npm:*)
version: 1.5.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- scraping
- automation
- apify
compatibility: Designed for Claude CodeComplete checklist for deploying Actors to the Apify platform and integrating them into production applications. Covers Actor configuration, scheduling, monitoring, alerting, and rollback. Work top to bottom: clear the pre-deployment gates, then run the six deploy steps, then wire the alert conditions.
apify runapify login configured with production tokenapify-core-workflow-a and apify-deploy-integration.actor/actor.json has correct name, title, descriptionINPUT_SCHEMA.json validates all required inputsDockerfile uses pinned base image version (apify/actor-node:20, not latest)package-lock.json committed (deterministic installs)Actor.main() wraps entry point (handles init/exit/errors)failedRequestHandler logs failures without crashing Actorif (!input?.startUrls) throw ...)maxRequestsPerCrawl set to prevent runaway costsSUMMARY key-value store record saved with run statsRead the Actor's .actor/actor.json, INPUT_SCHEMA.json, and Dockerfile first to confirm the pre-deployment gates above, then run the six deploy steps. Each step's full command and code block lives in references/implementation.md; the skeleton is below.
apify push, then apify builds ls to confirm the build, then apify actors call with a small production-like input to smoke-test on-platform.client.schedules().create({...}) (or Apify Console: Actors > Your Actor > Schedules). Set cronExpression, runInput, and runOptions (memory/timeout).client.webhooks().create({...}) on ACTOR.RUN.SUCCEEDED/FAILED/TIMED_OUT with a payloadTemplate posting runId, status, and datasetId to your server.checkActorHealth(actorId, lookbackHours) helper lists recent runs and reports success rate, failures, timeouts, and total cost.apify builds ls, then repoint the Actor at a prior build via the POST /v2/acts/ACTOR_ID?build=N API, or redeploy from a git tag.runWithCostGuard(actorId, input, maxCostUsd) polls usageTotalUsd every 30s and aborts the run if it exceeds budget.The first deploy step in full:
# Build and push to Apify platform
apify push
# Verify the build succeeded
apify builds ls
Working through this skill produces a production-ready Actor with:
apify builds ls shows a SUCCEEDED build).Health-check output looks like:
Actor: username/product-scraper
Last 24h: 3 runs, 66.7% success
Failed: 1, Timed out: 0
Total cost: $0.4213
| Alert | Condition | Severity |
|---|---|---|
| Run failed | status === 'FAILED' | P1 |
| Run timed out | status === 'TIMED-OUT' | P2 |
| Low yield | Dataset items < expected threshold | P2 |
| High cost | usageTotalUsd > budget | P2 |
| Consecutive failures | 3+ failures in a row | P1 |
| No runs in window | Schedule didn't trigger | P1 |
| Issue | Cause | Solution |
|---|---|---|
| Build fails on platform | Local deps differ | Commit package-lock.json |
| Schedule not firing | Cron syntax error | Validate at crontab.guru |
| Webhook not received | URL not reachable | Use ngrok for testing; check HTTPS |
| Memory exceeded | Workload too large | Increase memory or reduce concurrency |
| Unexpected cost spike | No maxRequestsPerCrawl | Always set an upper bound |
Four worked examples — a first production deploy, health-check output, a cost guard aborting a runaway run, and a build rollback — are in references/examples.md. A first production deploy in brief:
# Smoke-test on-platform with a tiny input before scheduling
apify actors call username/product-scraper \
--input='{"startUrls":[{"url":"https://target.com"}],"maxItems":10}'
Then create the daily schedule and completion webhook (implementation.md Steps 2–3).
Once production is stable, plan version upgrades with the apify-upgrade-migration skill: it covers bumping the Actor base image, migrating INPUT_SCHEMA.json fields without breaking existing schedules, and re-running this checklist against the new build before repointing traffic.
评论 (0)
暂无评论,成为第一个评论者吧!