Skip to main content الرئيسية المنشئون jeremylongshore tons-of-skills-marketplace linear-performance-tuning
linear-performance-tuning Optimize Linear API queries, caching, and batching for performance.
Use when improving response times, reducing API calls,
or implementing caching strategies for Linear data.
Trigger: "linear performance", "optimize linear", "linear caching",
"linear slow queries", "speed up linear", "linear N+1".
الانتقال إلى التثبيت سوق المهارات اكتشف واستكشف مهارات الذكاء الاصطناعي التي بناها المجتمع.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill linear-performance-tuningيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المزيد من هذا المستودع langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
langchain-langgraph-agents Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".
langchain-langgraph-human-in-loop Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before /
interrupt_after and Command(resume=...) — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
name linear-performance-tuning description Optimize Linear API queries, caching, and batching for performance.
Use when improving response times, reducing API calls,
or implementing caching strategies for Linear data.
Trigger: "linear performance", "optimize linear", "linear caching",
"linear slow queries", "speed up linear", "linear N+1".
allowed-tools Read, Write, Edit, Grep version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","linear","api","performance"] compatibility Designed for Claude Code
Linear Performance Tuning
Overview
Optimize Linear API usage for minimal latency and efficient resource consumption. The three main levers are: (1) query flattening to avoid N+1 and reduce complexity, (2) caching static data with webhook-driven invalidation, and (3) batching mutations into single GraphQL requests.
Key numbers:
Query complexity budget: 250,000 pts/hour, max 10,000 per query
Each property: 0.1 pt, each object: 1 pt, connections: multiply by first
Best practice: sort by updatedAt to get fresh data first
Prerequisites
Working Linear integration with @linear/sdk
Understanding of GraphQL query structure
Optional: Redis for distributed caching
Instructions
Step 1: Eliminate N+1 Queries
The SDK lazy-loads relations. Accessing .assignee on 50 issues makes 50 separate API calls.
import { LinearClient } from "@linear/sdk" ;
const client = new LinearClient ({ apiKey : process.env .LINEAR_API_KEY ! });
const issues = await client.issues ({ first : 50 });
for (const i of issues.nodes ) {
const assignee = await i.assignee ;
const state = await i.state ;
console .log (`${i.identifier} : ${assignee?.name} [${state?.name} ]`
const
await
client
rawRequest
`
query TeamDashboard($teamId: String!) {
team(id: $teamId) {
issues(first: 50, orderBy: updatedAt) {
nodes {
id identifier title priority estimate updatedAt
assignee { name email }
state { name type }
labels { nodes { name color } }
project { name }
}
pageInfo { hasNextPage endCursor }
}
}
}
`
teamId
"team-uuid"
Step 2: Cache Static Data Teams, workflow states, and labels change rarely. Cache them with appropriate TTLs.
interface CacheEntry <T> {
data : T;
expiresAt : number ;
}
class LinearCache {
private store = new Map <string , CacheEntry <any >>();
get<T>(key : string ): T | null {
const entry = this .store .get (key);
if (!entry || Date .now () > entry.expiresAt ) {
this .store .delete (key);
return null ;
}
return entry.data ;
}
set<T>(key : string , data : T, ttlSeconds : number ): void {
this .store .set (key, { data, expiresAt : Date .now () + ttlSeconds * 1000 });
}
invalidate (key : string ): void {
this .store .delete (key);
}
}
const cache = new LinearCache ();
async function getTeams (client : LinearClient const cached = cache.get <any []>("teams" );
if (cached) return cached;
const teams = await client.teams ();
cache.set ("teams" , teams.nodes , 600 );
return teams.nodes ;
}
async function getStates (client : LinearClient , teamId : string const key = `states:${teamId} ` ;
const cached = cache.get <any []>(key);
if (cached) return cached;
const team = await client.team (teamId);
const states = await team.states ();
cache.set (key, states.nodes , 1800 );
return states.nodes ;
}
async function getLabels (client : LinearClient const cached = cache.get <any []>("labels" );
if (cached) return cached;
const labels = await client.issueLabels ();
cache.set ("labels" , labels.nodes , 600 );
return labels.nodes ;
}
Step 3: Webhook-Driven Cache Invalidation Replace polling with webhooks. Invalidate cache when relevant entities change.
function handleCacheInvalidation (event : { type : string ; action: string ; data: any }switch (event.type ) {
case "Issue" :
cache.invalidate (`issue:${event.data.id} ` );
break ;
case "WorkflowState" :
cache.invalidate (`states:${event.data.teamId} ` );
break ;
case "IssueLabel" :
cache.invalidate ("labels" );
break ;
case "Team" :
cache.invalidate ("teams" );
break ;
}
}
Step 4: Batch Mutations Combine multiple mutations into one GraphQL request.
async function batchUpdatePriority (
client : LinearClient ,
issueUpdates : Array <{ id: string ; priority: number }>
) {
const chunkSize = 20 ;
for (let i = 0 ; i < issueUpdates.length ; i += chunkSize) {
const chunk = issueUpdates.slice (i, i + chunkSize);
const mutations = chunk.map ((u, j ) =>
`u${j} : issueUpdate(id: "${u.id} ", input: { priority: ${u.priority} }) { success }`
).join ("\n" );
await client.client .rawRequest (`mutation { ${mutations} }` );
}
}
async function batchCreate (
client : LinearClient ,
teamId : string ,
issues : Array <{ title: string ; priority?: number }>
) {
const mutations = issues.map ((issue, i ) =>
`c${i} : issueCreate(input: {
teamId: "${teamId} ",
title: "${issue.title.replace(/"/g, '\\" ')}",
priority: ${issue.priority ?? 3}
}) { success issue { id identifier } }`
).join("\n");
return client.client.rawRequest(`mutation { ${mutations} }`);
}
Step 5: Efficient Pagination
async function * paginateIssues (
client : LinearClient ,
teamId : string ,
pageSize = 50
) {
let cursor : string | undefined ;
let hasNext = true ;
while (hasNext) {
const result = await client.issues ({
first : pageSize,
after : cursor,
filter : { team : { id : { eq : teamId } } },
orderBy : "updatedAt" ,
});
yield result.nodes ;
hasNext = result.pageInfo .hasNextPage ;
cursor = result.pageInfo .endCursor ;
}
}
for await (const batch of paginateIssues (client, "team-uuid" )) {
console .log (`Processing ${batch.length} issues` );
}
const lastSync = "2026-03-20T00:00:00Z" ;
const updated = await client.issues ({
first : 100 ,
filter : { updatedAt : { gte : lastSync } },
orderBy : "updatedAt" ,
});
Step 6: Request Coalescing Deduplicate concurrent identical requests.
const inflight = new Map <string , Promise <any >>();
async function coalesce<T>(key : string , fn : () => Promise <T>): Promise <T> {
if (inflight.has (key)) return inflight.get (key)!;
const promise = fn ().finally (() => inflight.delete (key));
inflight.set (key, promise);
return promise;
}
const team = await coalesce ("team:ENG" , () =>
client.teams ({ filter : { key : { eq : "ENG" } } }).then (r =>nodes [0 ])
);
Error Handling Error Cause Solution Query complexity too highDeep nesting + large first Use rawRequest() with flat fields, first: 50 HTTP 429 Burst exceeding rate budget Add request queue with 100ms spacing Stale cache TTL too long Shorten TTL or use webhook invalidation Timeout Query spanning too many records Paginate with first: 50 + cursor
Examples
Performance Benchmark async function benchmark (label : string , fn : () => Promise <any >const start = Date .now ();
await fn ();
console .log (`${label} : ${Date .now() - start} ms` );
}
await benchmark ("Cold teams" , () => client.teams ());
await benchmark ("Cached teams" , () => getTeams (client));
await benchmark ("50 issues (SDK)" , () => client.issues ({ first : 50 }));
await benchmark ("50 issues (raw)" , () => client.client .rawRequest (
`query { issues(first: 50) { nodes { id identifier title priority } } }`
));
Resources 