Jeden Skill in Manus ausführen
mit einem Klick

Jeden Skill in Manus mit einem Klick ausführen

realtime-analytics-api

Sterne4

Forks0

Aktualisiert4. April 2026 um 12:14

Define backend-to-frontend delivery patterns for near-realtime vacancy analytics, freshness indicators, and streaming updates.

Installation

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

In Manus ausführen

Quelle

ivanshamaev

ivanshamaev/ai-agent-codex

GitHub-Repository öffnen Creator-Repositorys ansehen

Download

In Manus ausführen

Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

SoftwareentwicklerInformatik- und Mathematikberufe·SOC 15-1252

SKILL.md

readonly

name	realtime-analytics-api
description	Define backend-to-frontend delivery patterns for near-realtime vacancy analytics, freshness indicators, and streaming updates.
license	MIT
compatibility	opencode
metadata	{"audience":"frontend-and-backend-engineers","domain":"realtime-analytics"}

What I do

Define when polling, SSE, or websocket delivery is appropriate.
Clarify freshness, update frequency, and client expectations.
Shape incremental update payloads so the UI can merge them predictably.
Identify which metrics should stream and which should stay request-based.

Decision guide

polling for simple periodic refresh
SSE for one-way metric updates and notifications
websocket for bidirectional or high-frequency interactions

Required semantics

current freshness timestamp
update cadence
replay or reconnect behavior
filter context tied to the update stream
stale-state behavior in the UI

Mehr aus diesem Repository

gleiches Repository

code-review-checklist

ivanshamaev/ai-agent-codex

Apply layer-specific quality checklists for Python ETL, Airflow DAGs, dbt models, SQL, FastAPI, Vue.js, and Docker Compose changes in the HH data platform.

2026-05-254

git-diff-analysis

ivanshamaev/ai-agent-codex

Inspect git history and diffs to identify what changed, which platform layers are affected, and what risk signals to prioritise before applying review checklists.

2026-05-254

airflow-dag-design

ivanshamaev/ai-agent-codex

Design small, maintainable Airflow DAGs with clear task boundaries, retry policy, and runtime assumptions.

2026-04-044

asyncio-pipeline-patterns

ivanshamaev/ai-agent-codex

Apply asyncio patterns for concurrent IO, background work, and coordinated refresh flows in Python analytics services.

2026-04-044

dbt-modeling

ivanshamaev/ai-agent-codex

Design dbt source, staging, and mart models with explicit schemas and clear layer responsibilities.

2026-04-044

delivery-planning

ivanshamaev/ai-agent-codex

Break down a data-platform request into sequenced work items, dependencies, risks, and definition of done.

2026-04-044

name	realtime-analytics-api
description	Define backend-to-frontend delivery patterns for near-realtime vacancy analytics, freshness indicators, and streaming updates.
license	MIT
compatibility	opencode
metadata	{"audience":"frontend-and-backend-engineers","domain":"realtime-analytics"}

What I do

Define when polling, SSE, or websocket delivery is appropriate.
Clarify freshness, update frequency, and client expectations.
Shape incremental update payloads so the UI can merge them predictably.
Identify which metrics should stream and which should stay request-based.

Decision guide

polling for simple periodic refresh
SSE for one-way metric updates and notifications
websocket for bidirectional or high-frequency interactions

Required semantics

current freshness timestamp
update cadence
replay or reconnect behavior
filter context tied to the update stream
stale-state behavior in the UI