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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: adobe-advanced-troubleshooting
description: 'Apply advanced debugging techniques for Adobe API issues: IMS token
introspection, Firefly job failure analysis, PDF Services error
codes, and network-layer diagnostics for Adobe endpoints.
Trigger with phrases like "adobe hard bug", "adobe mystery error",
"adobe impossible to debug", "difficult adobe issue", "adobe deep debug".
'
allowed-tools: Read, Grep, Bash(kubectl:*), Bash(curl:*), Bash(tcpdump:*)
version: 1.7.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- design
- adobe
compatibility: Designed for Claude CodeDeep debugging techniques for complex Adobe API issues that resist standard troubleshooting: IMS token problems, Firefly async job failures, PDF Services edge cases, and network-layer diagnostics.
curl with verbose mode for HTTP debuggingtcpdump, openssl s_client)When auth issues occur, decode the access token to check claims:
# Adobe IMS tokens are JWTs — decode the payload (middle segment)
TOKEN=$(curl -s -X POST 'https://ims-na1.adobelogin.com/ims/token/v3' \
-d "client_id=${ADOBE_CLIENT_ID}&client_secret=${ADOBE_CLIENT_SECRET}&grant_type=client_credentials&scope=${ADOBE_SCOPES}" | jq -r '.access_token')
# Decode JWT payload (base64url-decode the middle segment)
echo "$TOKEN" | cut -d. -f2 | tr '_-' '/+' | base64 -d 2>/dev/null | jq .
# Look for:
# - "exp": expiration timestamp (is it expired?)
# - "iss": should be "ims-na1.adobelogin.com"
# - "as": scopes granted (do they match what you requested?)
# - "client_id": verify it matches your ADOBE_CLIENT_ID
# Full HTTP trace against Firefly API
curl -v -X POST 'https://firefly-api.adobe.io/v3/images/generate' \
-H "Authorization: Bearer ${TOKEN}" \
-H "x-api-key: ${ADOBE_CLIENT_ID}" \
-H "Content-Type: application/json" \
-d '{"prompt":"test","n":1,"size":{"width":512,"height":512}}' 2>&1 | tee firefly-debug.log
# Check for:
# - TLS handshake issues (look for SSL/TLS lines)
# - Request headers actually sent
# - Response headers (Retry-After, x-request-id, x-adobe-*)
# - Response body with error details
// When async Firefly jobs fail, the status endpoint returns error details
async function diagnoseFireflyJob(jobId: string) {
const token = await getAccessToken();
const response = await fetch(
`https://firefly-api.adobe.io/v3/images/jobs/${encodeURIComponent(jobId)}`,
{
headers: {
'Authorization': `Bearer ${token}`,
'x-api-key': process.env.ADOBE_CLIENT_ID!,
},
}
);
const status = await response.json();
console.log('=== Firefly Job Diagnosis ===');
console.log('Job ID:', jobId);
console.log('Status:', status.status);
if (status.status === 'failed') {
console.log('Error code:', status.error?.code);
console.log('Error message:', status.error?.message);
console.log('Error details:', JSON.stringify(status.error?.details, null, 2));
// Common failure reasons:
// - "content_policy": prompt violated guidelines
// - "input_validation": invalid parameters
// - "internal_error": Adobe server issue (retry)
// - "timeout": job took too long (simplify prompt)
}
// Log all response headers for Adobe support
console.log('Response headers:', Object.fromEntries(response.headers.entries()));
}
// src/adobe/pdf-error-map.ts
// Comprehensive PDF Services error codes and recovery actions
const PDF_ERROR_MAP: Record<string, { cause: string; action: string; retryable: boolean }> = {
'DISQUALIFIED': { cause: 'File is encrypted/password-protected', action: 'Decrypt PDF first', retryable: false },
'BAD_PDF': { cause: 'Corrupted or invalid PDF', action: 'Validate with pdfinfo/pdftk', retryable: false },
'BAD_PDF_CONTENT': { cause: 'PDF content is malformed', action: 'Re-export from source', retryable: false },
'UNSUPPORTED_MEDIA_TYPE': { cause: 'Wrong file format for operation', action: 'Check MimeType matches file', retryable: false },
'FILE_SIZE_EXCEEDED': { cause: 'File exceeds size limit', action: 'Compress or split PDF', retryable: false },
'PAGE_LIMIT_EXCEEDED': { cause: 'Too many pages for operation', action: 'Split into smaller PDFs', retryable: false },
'QUOTA_EXCEEDED': { cause: 'Monthly transaction limit hit', action: 'Upgrade plan or wait for reset', retryable: false },
'INTERNAL_ERROR': { cause: 'Adobe server error', action: 'Retry with backoff', retryable: true },
'TIMEOUT': { cause: 'Processing timeout', action: 'Try smaller file or fewer pages', retryable: true },
};
export function diagnosePdfError(errorCode: string): string {
const info = PDF_ERROR_MAP[errorCode];
if (!info) return `Unknown PDF Services error: ${errorCode}`;
return `${errorCode}: ${info.cause}\nAction: ${info.action}\nRetryable: ${info.retryable}`;
}
#!/bin/bash
# adobe-layer-test.sh — Test each network layer independently
echo "=== Layer 1: DNS Resolution ==="
nslookup ims-na1.adobelogin.com
nslookup firefly-api.adobe.io
nslookup image.adobe.io
echo ""
echo "=== Layer 2: TCP Connectivity ==="
for host in ims-na1.adobelogin.com firefly-api.adobe.io image.adobe.io; do
timeout 5 bash -c "echo > /dev/tcp/$host/443" 2>/dev/null && echo "$host:443 OPEN" || echo "$host:443 BLOCKED"
done
echo ""
echo "=== Layer 3: TLS Handshake ==="
for host in ims-na1.adobelogin.com firefly-api.adobe.io; do
echo | openssl s_client -connect "$host:443" -servername "$host" 2>/dev/null | grep -E "subject|issuer|Verify return"
done
echo ""
echo "=== Layer 4: IMS Authentication ==="
HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
'https://ims-na1.adobelogin.com/ims/token/v3' \
-d "client_id=${ADOBE_CLIENT_ID}&client_secret=${ADOBE_CLIENT_SECRET}&grant_type=client_credentials&scope=${ADOBE_SCOPES}")
echo "IMS Token: HTTP $HTTP_CODE"
echo ""
echo "=== Layer 5: API Endpoint ==="
if [ "$HTTP_CODE" = "200" ]; then
TOKEN=$(curl -s -X POST 'https://ims-na1.adobelogin.com/ims/token/v3' \
-d "client_id=${ADOBE_CLIENT_ID}&client_secret=${ADOBE_CLIENT_SECRET}&grant_type=client_credentials&scope=${ADOBE_SCOPES}" | python3 -c "import sys,json; print(json.load(sys.stdin)['access_token'])")
API_CODE=$(curl -s -o /dev/null -w "%{http_code}" -X POST \
'https://firefly-api.adobe.io/v3/images/generate' \
-H "Authorization: Bearer $TOKEN" \
-H "x-api-key: $ADOBE_CLIENT_ID" \
-H "Content-Type: application/json" \
-d '{"prompt":"test","n":1,"size":{"width":512,"height":512}}')
echo "Firefly API: HTTP $API_CODE"
fi
// Always capture x-request-id from Adobe responses for support escalation
async function debugAdobeCall(url: string, options: RequestInit) {
const response = await fetch(url, options);
const debugInfo = {
url,
status: response.status,
requestId: response.headers.get('x-request-id'),
retryAfter: response.headers.get('Retry-After'),
contentType: response.headers.get('content-type'),
date: response.headers.get('date'),
};
if (!response.ok) {
const body = await response.text();
console.error('Adobe API Error:', { ...debugInfo, body: body.slice(0, 500) });
// Include x-request-id in support ticket for Adobe to trace the request
}
return { response, debugInfo };
}
Adobe Support Ticket
Severity: P[1-4]
x-request-id: [from response header]
Timestamp: [ISO 8601]
Client ID: [first 8 chars only]
API: [Firefly / PDF Services / Photoshop]
Endpoint: [full URL]
HTTP Status: [status code]
Issue Summary: [1-2 sentences]
Steps to Reproduce:
1. [Step]
2. [Step]
Evidence:
- Layer test results attached
- Verbose curl output attached
- JWT token claims (non-sensitive fields only)
Workarounds Attempted:
1. [What you tried] - [Result]
If a step fails, stop before applying follow-on changes, retain sanitized diagnostic evidence, and use the troubleshooting or escalation guidance already in this skill. Treat authentication and vendor-service failures separately from local configuration errors.
Start with the smallest applicable command or code example already provided in this guide, using a non-production Adobe environment and credentials. Confirm the documented response or validation result before applying the pattern to production.
For load testing, see adobe-load-scale.
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