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: attio-cost-tuning
description: 'Optimize Attio API usage costs -- reduce request volume, select the
right plan, monitor usage, and implement budget alerts.
Trigger: "attio cost", "attio billing", "reduce attio costs",
"attio pricing", "attio expensive", "attio budget", "attio usage".
'
allowed-tools: Read, Grep
version: 1.7.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- crm
- attio
compatibility: Designed for Claude CodeAttio pricing is based on workspace seats, not API calls. However, API rate limits effectively cap throughput, so optimizing request volume improves both performance and cost efficiency. This skill covers practical strategies to reduce unnecessary API calls.
| Plan | Price | Key Limits |
|---|---|---|
| Free | $0/user/mo | 3 users, basic objects, limited automations |
| Plus | $29/user/mo | Unlimited objects, lists, advanced reporting |
| Pro | $59/user/mo | Advanced automations, API access, webhooks |
| Enterprise | Custom | SSO, audit logs, dedicated support, custom rate limits |
API access requires Plus plan or higher. Rate limits are per-workspace, not per-seat.
// Instrument all API calls to measure usage patterns
class AttioUsageTracker {
private calls: Array<{
method: string;
path: string;
timestamp: number;
durationMs: number;
cached: boolean;
}> = [];
async track<T>(
method: string,
path: string,
operation: () => Promise<T>,
cached = false
): Promise<T> {
const start = Date.now();
try {
const result = await operation();
this.calls.push({
method, path, timestamp: start,
durationMs: Date.now() - start, cached,
});
return result;
} catch (err) {
this.calls.push({
method, path, timestamp: start,
durationMs: Date.now() - start, cached: false,
});
throw err;
}
}
report(windowMs = 3600_000): {
totalCalls: number;
cachedCalls: number;
topEndpoints: Array<{ path: string; count: number }>;
} {
const cutoff = Date.now() - windowMs;
const recent = this.calls.filter((c) => c.timestamp > cutoff);
const cached = recent.filter((c) => c.cached).length;
const endpointCounts = new Map<string, number>();
for (const call of recent) {
const key = `${call.method} ${call.path}`;
endpointCounts.set(key, (endpointCounts.get(key) || 0) + 1);
}
const topEndpoints = [...endpointCounts.entries()]
.sort((a, b) => b[1] - a[1])
.slice(0, 10)
.map(([path, count]) => ({ path, count }));
return { totalCalls: recent.length, cachedCalls: cached, topEndpoints };
}
}
The five biggest cost/rate-limit savers:
| Strategy | Reduction | Implementation |
|---|---|---|
| Cache object schemas | 50-90% of schema reads | Cache GET /objects and /attributes for 30 min |
Batch with $in filter | N:1 on lookups | Single query instead of N individual fetches |
Use limit: 500 | 5x fewer pagination requests | Max page size per request |
| Webhook-driven sync | Eliminate polling | React to changes instead of polling every N seconds |
| Cache records | 30-80% of record reads | LRU cache with webhook invalidation |
// BAD: Polling every 30 seconds for changes
setInterval(async () => {
const records = await client.post("/objects/people/records/query", {
filter: { updated_at: { $gt: lastCheck.toISOString() } },
limit: 500,
});
for (const record of records.data) await processUpdate(record);
lastCheck = new Date();
}, 30_000);
// Cost: 2,880 requests/day MINIMUM (even with no changes)
// GOOD: Webhook-driven (0 requests when no changes)
app.post("/webhooks/attio", async (req, res) => {
res.status(200).json({ received: true });
const event = req.body;
if (event.event_type === "record.updated") {
const record = await client.get(
`/objects/${event.object.api_slug}/records/${event.record.id.record_id}`
);
await processUpdate(record);
}
});
// Cost: 1 request per actual change
import { LRUCache } from "lru-cache";
// Tier 1: Schema data (changes very rarely)
const schemaCache = new LRUCache<string, unknown>({
max: 100,
ttl: 30 * 60 * 1000, // 30 minutes
});
// Tier 2: Record data (changes occasionally)
const recordCache = new LRUCache<string, unknown>({
max: 5000,
ttl: 5 * 60 * 1000, // 5 minutes
});
// Tier 3: List/query results (changes frequently)
const queryCache = new LRUCache<string, unknown>({
max: 200,
ttl: 30 * 1000, // 30 seconds
});
function getCacheForPath(path: string): LRUCache<string, unknown> {
if (path.includes("/attributes") || path === "/objects") return schemaCache;
if (path.includes("/records/") && !path.includes("/query")) return recordCache;
return queryCache;
}
class AttioRequestBudget {
private requestsToday = 0;
private dayStart = this.todayStart();
private readonly dailyBudget: number;
private readonly warningThreshold: number;
constructor(dailyBudget = 10_000) {
this.dailyBudget = dailyBudget;
this.warningThreshold = dailyBudget * 0.8;
}
private todayStart(): number {
const d = new Date();
d.setHours(0, 0, 0, 0);
return d.getTime();
}
recordRequest(): void {
const today = this.todayStart();
if (today !== this.dayStart) {
this.dayStart = today;
this.requestsToday = 0;
}
this.requestsToday++;
if (this.requestsToday === Math.floor(this.warningThreshold)) {
console.warn(`Attio budget warning: ${this.requestsToday}/${this.dailyBudget} requests today`);
}
if (this.requestsToday >= this.dailyBudget) {
console.error(`Attio daily budget exceeded: ${this.requestsToday} requests`);
}
}
getUsage(): { today: number; budget: number; percentUsed: number } {
return {
today: this.requestsToday,
budget: this.dailyBudget,
percentUsed: Math.round((this.requestsToday / this.dailyBudget) * 100),
};
}
}
If you log API calls to a database:
-- Daily request volume (last 30 days)
SELECT
DATE(timestamp) AS day,
COUNT(*) AS total_requests,
COUNT(CASE WHEN cached THEN 1 END) AS cache_hits,
ROUND(COUNT(CASE WHEN cached THEN 1 END) * 100.0 / COUNT(*), 1) AS cache_hit_pct,
ROUND(AVG(duration_ms), 0) AS avg_latency_ms
FROM attio_api_log
WHERE timestamp >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY DATE(timestamp)
ORDER BY day DESC;
-- Top endpoints by volume (identify optimization targets)
SELECT
method || ' ' || path AS endpoint,
COUNT(*) AS calls,
ROUND(AVG(duration_ms), 0) AS avg_ms,
COUNT(CASE WHEN status = 429 THEN 1 END) AS rate_limited
FROM attio_api_log
WHERE timestamp >= CURRENT_DATE - INTERVAL '7 days'
GROUP BY method, path
ORDER BY calls DESC
LIMIT 10;
[ ] Object schema calls cached (30-minute TTL)
[ ] Record lookups cached (5-minute TTL with webhook invalidation)
[ ] Polling replaced with webhooks where possible
[ ] Bulk operations use $in filter (1 request instead of N)
[ ] Pagination uses limit: 500 (max page size)
[ ] Unnecessary API calls identified and eliminated
[ ] Usage monitoring in place with daily budget alerts
[ ] Cache hit rate > 50% on read-heavy workloads
Confirm that you have an Attio workspace appropriate to the task, a dedicated non-production record or workspace for testing, and only the API token scopes or administrative access required by the procedure.
Following this guide produces the Attio integration outcome for its topic—configuration, validation evidence, operational recovery, or a documented migration result. Record command output and relevant identifiers so a failed step is traceable.
Start with the smallest applicable command or code example in the relevant section, using a dedicated test record or workspace and non-production credentials. Confirm the expected response or validation result before applying the pattern to production.
| Cost issue | Root cause | Fix |
|---|---|---|
| High request volume | Polling loop | Switch to webhooks |
| Low cache hit rate | Short TTL or no cache | Increase TTL, add webhook invalidation |
| Rate limiting (429s) | Burst without throttling | Add PQueue with intervalCap |
| N+1 queries | Individual record fetches | Batch with $in filter |
For architecture patterns, see attio-reference-architecture.
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