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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。
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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-ci-integration
description: |
Configure CI/CD pipelines for Apify Actor builds and deployments.
Use when automating Actor deployment via GitHub Actions, running
integration tests against the live Apify API, or building CI/CD for
scrapers that push to the Apify platform.
Trigger with "apify CI", "apify GitHub Actions", "apify automated deploy",
"CI apify", "apify pipeline", "auto deploy actor".
allowed-tools: Write, Bash(gh:*), Bash(npm:*)
version: 1.5.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- scraping
- automation
- apify
compatibility: Designed for Claude CodeAutomate Apify Actor builds, tests, and deployments using GitHub Actions — test-on-PR, deploy-on-merge, live-API integration testing, and Docker build verification. Three workflow files do the work (test, deploy, verify-build); full copy-paste-ready definitions live in references/workflows.md.
CI authenticates to Apify with a personal API token, never a hard-coded credential:
gh secret set APIFY_TOKEN),
and expose it to a job only via env: APIFY_TOKEN: ${{ secrets.APIFY_TOKEN }}.apify login --token $APIFY_TOKEN; the REST API and
apify-client read it from the APIFY_TOKEN environment variable.APIFY_TOKEN_TEST / APIFY_TOKEN_PROD secrets so integration
runs never touch production data. Get tokens from Apify Console → Settings →
Integrations.# Store Apify token for CI
gh secret set APIFY_TOKEN --body "apify_api_YOUR_CI_TOKEN"
# Optional: separate tokens for test vs production
gh secret set APIFY_TOKEN_TEST --body "apify_api_test_token"
gh secret set APIFY_TOKEN_PROD --body "apify_api_prod_token"
Add .github/workflows/apify-test.yml with two jobs — unit-tests on every PR
and integration-tests gated to push (merge) events. The integration job
proves connectivity before running tests:
- name: Verify Apify connection
run: |
curl -sf -H "Authorization: Bearer $APIFY_TOKEN" \
https://api.apify.com/v2/users/me | jq '.data.username'
Full two-job workflow: references/workflows.md (Test Workflow).
Add .github/workflows/apify-deploy.yml — triggered on merges that touch
src/**, package.json, or .actor/** (plus workflow_dispatch). It builds,
tests, installs the Apify CLI, apify pushes, then runs a minimal smoke call to
confirm the new build actually starts. Full definition:
references/workflows.md (Deploy Workflow).
Gate live-API tests on the token so they skip cleanly when it is absent:
const SKIP_INTEGRATION = !process.env.APIFY_TOKEN;
describe.skipIf(SKIP_INTEGRATION)('Apify Integration', () => {
it('should authenticate successfully', async () => {
const user = await client.user().get();
expect(user.username).toBeTruthy();
});
});
The complete suite (auth, live Actor run, create/delete a named dataset with cleanup) is in references/integration-tests.md.
Add a verify-build.yml that docker builds the Actor image and boots it once
to confirm the entry point loads. Optionally add branch-protection rules that
require the CI contexts before merge. Both blocks:
references/workflows.md (Actor Build Verification).
Applying this skill produces committed CI/CD infrastructure in the repository:
.github/workflows/apify-test.yml — unit tests on every PR, integration tests
on merge to main..github/workflows/apify-deploy.yml — apify push + post-deploy smoke test on
merge, plus manual workflow_dispatch..github/workflows/verify-build.yml — Docker build + entry-point check on PRs.tests/integration/apify.test.ts — token-gated live-API integration suite.APIFY_TOKEN, optional _TEST / _PROD variants)
and, optionally, branch-protection requiring the CI checks to pass.Once merged, every PR runs unit tests, every merge deploys and smoke-tests the
Actor, and a failed deploy surfaces a ::error:: annotation in the run log.
| Issue | Cause | Solution |
|---|---|---|
APIFY_TOKEN not set | Secret not configured | gh secret set APIFY_TOKEN |
| Integration test timeout | Slow Actor run | Increase timeout, use smaller input |
| Docker build fails in CI | Local-only deps | Commit package-lock.json |
apify push fails | Not logged in | Add apify login --token step |
| Flaky integration tests | External service issues | Add retries, use test.retry(2) |
Minimal test-on-PR gate — the smallest useful setup is Step 1 (store the
secret) plus the unit-tests job from the test workflow. Every PR then runs
npm ci && npm run build && npm test before it can merge.
Full deploy pipeline — add the deploy workflow so a merge to main that
touches src/** builds, tests, runs apify push, then smoke-tests the new
build:
- name: Push Actor to Apify
run: apify push
- name: Verify deployment
run: |
ACTOR_ID=$(jq -r '.name' .actor/actor.json)
apify actors call $ACTOR_ID \
--input='{"startUrls":[{"url":"https://example.com"}],"maxItems":1}' \
--timeout=120
apify-client app (not Actor dev) — for an app that calls Actors rather than
publishing one, mock apify-client in unit tests and gate a real-token
integration job to main. Full workflow:
references/workflows.md (CI Configuration for apify-client Apps).
See references/workflows.md for every full workflow and references/integration-tests.md for the complete integration suite.
For runtime deployment patterns beyond CI wiring — release channels, versioned
Actor builds, and rollback — see the apify-deploy-integration skill in this
pack.
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