Related Skills:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondata-fetchingExecute the skills CLI command in your project's root directory to begin installation:
Fetches data-fetching from lobehub/lobehub and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate data-fetching. Access via /data-fetching in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Related Skills:
store-data-structures- How to structure List and Detail data in stores (Map vs Array patterns)
┌─────────────┐
│ Component │
└──────┬──────┘
│ 1. Call useFetchXxx hook from store
↓
┌──────────────────┐
│ Zustand Store │
│ (State + Hook) │
└──────┬───────────┘
│ 2. useClientDataSWR calls service
↓
┌──────────────────┐
│ Service Layer │
│ (xxxService) │
└──────┬───────────┘
│ 3. Call lambdaClient
↓
┌──────────────────┐
│ lambdaClient │
│ (TRPC Client) │
└──────────────────┘
store-data-structures skill for List vs Detail patternsstore-data-structures skillNote: For data structure patterns (Map vs Array, List vs Detail), see the
store-data-structuresskill.
// src/services/agentEval.ts
import { lambdaClient } from '@/libs/trpc/client';
class AgentEvalService {
// Query methods - READ operations
async listBenchmarks() {
return lambdaClient.agentEval.listBenchmarks.query();
}
async getBenchmark(id: string) {
return lambdaClient.agentEval.getBenchmark.query({ id });
}
// Mutation methods - WRITE operations
async createBenchmark(params: CreateBenchmarkParams) {
return lambdaClient.agentEval.createBenchmark.mutate(params);
}
async updateBenchmark(params: UpdateBenchmarkParams) {
return lambdaClient.agentEval.updateBenchmark.mutate(params);
}
async deleteBenchmark(id: string) {
return lambdaClient.agentEval.deleteBenchmark.mutate({ id });
}
}
export const agentEvalService = new AgentEvalService();
export const xxxService = new XxxService())Data Structure: See
store-data-structuresskill for how to structure List and Detail data.
// src/store/eval/slices/benchmark/initialState.ts
import type { AgentEvalBenchmark, AgentEvalBenchmarkListItem } from '@lobechat/types';
export interface BenchmarkSliceState {
// List data - simple array (see store-data-structures skill)
benchmarkList: AgentEvalBenchmarkListItem[];
benchmarkListInit: boolean;
// Detail data - map for caching (see store-data-structures skill)
benchmarkDetailMap: Record<string, AgentEvalBenchmark>;
loadingBenchmarkDetailIds: string[];
// Mutation states
isCreatingBenchmark: boolean;
isUpdatingBenchmark: boolean;
isDeletingBenchmark: boolean;
}
For complete initialState, reducer, and internal dispatch patterns, see the
store-data-structuresskill.
// src/store/eval/slices/benchmark/action.ts
import type { SWRResponse } from 'swr';
import type { StateCreator } from 'zustand/vanilla';
import isEqual from 'fast-deep-equal';
import { mutate, useClientDataSWR } from '@/libs/swr';
import { agentEvalService } from '@/services/agentEval';
import type { EvalStore } from '@/store/eval/store';
import { benchmarkDetailReducer, type BenchmarkDetailDispatch } from './reducer';
const FETCH_BENCHMARKS_KEY = 'FETCH_BENCHMARKS';
const FETCH_BENCHMARK_DETAIL_KEY = 'FETCH_BENCHMARK_DETAIL';
export interface BenchmarkAction {
// SWR Hooks - for data fetching
useFetchBenchmarks: () => SWRResponse;
useFetchBenchmarkDetail: (id?: string) => SWRResponse;
// Refresh methods - for cache invalidation
refreshBenchmarks: () => Promise<void>;
refreshBenchmarkDetail: (id: string) => Promise<void>;
// Mutation actions - for write operations
createBenchmark: (params: CreateParams) => Promise<any>;
updateBenchmark: (params: UpdateParams) => Promise<void>;
deleteBenchmark: (id: string) => Promise<void>;
// Internal methods - not for direct UI use
internal_dispatchBenchmarkDetail: (payload: BenchmarkDetailDispatch) => void;
internal_updateBenchmarkDetailLoading: (id: string, loading: boolean) => void;
}
export const createBenchmarkSlice: StateCreator<
EvalStore,
[['zustand/devtools', never]],
[],
BenchmarkAction
> = (set, get) => ({
// Fetch list - Simple array
useFetchBenchmarks: () => {
return useClientDataSWR(FETCH_BENCHMARKS_KEY, () => agentEvalService.listBenchmarks(), {
onSuccess: (data: any) => {
set(
{
benchmarkList: data,
benchmarkListInit: true,
},
false,
'useFetchBenchmarks/success',
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
Keeps context tight: data-fetching is the kind of skill you can hand to a new teammate without a long onboarding doc.
data-fetching is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
data-fetching reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for data-fetching matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for data-fetching matched our evaluation — installs cleanly and behaves as described in the markdown.
data-fetching reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend data-fetching for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
data-fetching has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in data-fetching — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: data-fetching is focused, and the summary matches what you get after install.
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