Discover flows here. Run them on Flows.

Integrate configurable, signed webhooks that fire on key app events with reliable persistence and retry.

Integrate real usage tracking, billing calculations, and Stripe metered billing into the existing app.

Integrate real TOTP two‑factor authentication into an existing app with full persistence and verification.

Integrate dynamic meta tags, Open Graph, sitemap.xml, and robots.txt with real persistence

Implement a real, persistent search feature across the app’s primary data list.

Integrate a real, persistent rate limiting system into the current application.

Integrate Firebase Cloud Messaging so users receive real push notifications and tokens are persisted in the backend.

Integrate real Google and GitHub OAuth flows into the existing Node/Express app with persistent user records and session handling.

Integrate a secure email‑based magic‑link authentication flow into the existing Node/Express app.

Integrate multilingual support with language selection and persisted user preference.

Integrate a backend image upload endpoint that resizes images and stores them, plus a front‑end component to use it.

Integrate a real, searchable index into the current app with a backend API and UI component.

Integrate real transactional email capability into the existing app using SendGrid (or similar) with persistent templates and an API endpoint.

Implement a real, persistent one‑to‑one chat system in the existing app

Implement a persistent dark mode switch in the existing app.

Integrate a full‑stack commenting and threading feature into the existing app with real database persistence and UI.

Add a real background job queue with persistence to the existing app.

Implement a persistent activity timeline with backend storage, API, and UI integration

Build a real signup, login, session, and protected route flow - not a form that only looks functional.

Build a multi-step onboarding that captures real user data, persists progress, and lands users in a personalized app.

Design a correct Supabase schema: tables, relationships, RLS policies, and verification queries.

Add real Stripe payments with checkout, webhooks, and payment status - not a button that pretends to charge.

Build a real settings area: profile edits that persist, password change, plan display, and danger zone.

Go from idea to a working SaaS MVP: auth, core feature, billing readiness, and a landing page that converts.

Add real roles and permissions: enforced on backend endpoints and reflected honestly in the UI.

Design and build a pricing page that converts: plan structure, value anchoring, FAQs, and a working upgrade path.

Build an in-app notification system with real triggers, unread counts, and a working notification center.

Design a MongoDB data model that fits your queries: embed vs reference, indexes, and validation.

Build a two-sided marketplace core: listings, browsing, and a real transaction/request flow between buyers and sellers.

A complete website for a local service business: services, trust, working booking requests, and local SEO basics.

Crie um fluxo de inscrição realmente funcional com formulário, validação, persistência, prevenção de duplicidade, estados de sucesso/erro e testes de ponta a ponta.

Build real file uploads with validation, progress, storage, and display - files must survive refresh.

Build an in-app support system: ticket submission, status tracking, and an admin reply inbox.

A step-by-step flow to create a real playable first-person horror game with movement, interactions, enemy behavior, atmosphere, objectives, fail states, and testing.

A step-by-step flow to implement real signup, login, logout, session persistence, password reset, validation, and protected routes with visible UI states and mobile responsiveness.

A step-by-step flow to create a real, playable 2D browser game with movement, collisions, scoring, win/lose states, and basic polish using an AI app builder.

Create a real login page with working authentication and instrument it to capture useful investment-app auth metrics such as signups, login attempts, success rate, failures, and drop-off.

A step-by-step flow to build a real, working coffee shop website with menu, location, hours, contact form, and mobile-friendly pages using AI app builders.

A step-by-step flow for building a real habit tracker app in React with working habit creation, daily check-ins, streak tracking, persistence, validation, and basic testing.

Create a working React habit tracker app with habit creation, completion toggles, streak tracking, local persistence, validation, and basic QA checks.

Build a real booking system with availability, slot selection, and double-booking prevention.

Add a real AI chat feature with streaming responses, conversation history, and graceful failure handling.

Create a working AI agent system that discovers affiliate opportunities, generates compliant content, publishes or prepares campaign assets, and tracks clicks, conversions, and revenue in a usable dashboard.

Build a real admin dashboard with live data tables, stats, filters, and actions that actually mutate data.

Build a real team invitation system for a SaaS app, including invite creation, email/share flow, acceptance, membership creation, role assignment, validation, and end-to-end testing.

A step-by-step flow for implementing real-time online, offline, and typing presence in a chat app using WebSockets, including backend presence tracking, frontend updates, reconnect handling, and verification.

Build a real invite flow for a SaaS app so existing users can invite teammates by email, store invite records, validate tokens, accept invitations, and join the correct workspace with proper permissions.

Clean up AI-generated spaghetti safely: dedupe, extract, and organize without breaking working features.

Escape the default-AI look: define a real visual identity and apply it systematically across your app.

Make your app genuinely usable on phones: audit at real widths, fix layouts, and verify touch targets.

Repair broken links, 404s, back-button traps, and guard gaps so navigation works like users expect.

Your login 'works' but nothing is real: convert visual-only auth into persisted users, sessions, and guards.

Forms that validate, submit, persist, and recover: repair every broken form in your app end to end.

Hunt down every dead button in your AI-built app and wire each one to real, verified behavior.

The systematic recovery flow for beautiful-but-broken AI builds: audit honestly, then fix by functional priority.

Eliminate silent failures and frozen screens: add loading, empty, and error states across the whole app.

Your app forgets everything on refresh: move state into a real database with working CRUD.

Launch a waitlist that actually converts: page, incentives, referral loop, and the nurture sequence.

A repeatable short-video system for your product: hooks, formats, scripts, and a 2-week content calendar.

Launch on Reddit without getting banned: subreddit research, value-first posts, and comment strategy.

Plan and execute a Product Hunt launch: positioning, assets, launch-day playbook, and follow-through.

Create an announcement page and press kit that makes your launch easy to cover and share.

Write conversion-focused landing page copy: headline, sections, social proof, CTAs, and objection handling.

Write a tight investor pitch: narrative arc, numbers that matter, deck structure, and Q&A prep.

Package your hackathon project to win: narrative, demo plan, submission copy, and judge-proofing.

Write a README that makes people use your project: hook, quickstart, visuals, and honest docs.

Get your first users by hand: build a target list, write outreach that gets replies, and run the follow-up loop.

Script a tight product demo video: hook, narrative arc, screen directions, and CTA - ready to record.

Launch a creator marketplace: solve the cold-start, seed supply, launch demand, and prove the loop.

The anti-AI-slop audit: find and fix everything that makes your app look and feel like a generic AI build.

Systematically walk every path a user can take and verify each works - the complete functional test pass.

The essential security pass for AI-built apps: secrets, auth enforcement, input handling, and data exposure.

تدفق عملي لمساعدة المستخدم على التمييز بوضوح بين الأسئلة البحثية والمساهمات، ثم صياغتهما بشكل صحيح وقابل للاستخدام في مقترح أو ورقة بحثية.

The pre-ship gate: environment, errors, data safety, performance, and the go/no-go checklist.

Trust-level testing for payments: success, decline, cancel, idempotency, and state consistency.

A rigorous mobile QA pass: layouts, touch, keyboards, and the full core journey on phone-sized screens.

Evaluate a hackathon project like a rigorous judge: criteria scoring, evidence checks, and structured feedback.

Prove your app works with evidence: build a claim-by-claim verification pack with receipts.

Dedicated auth testing: signup, login, sessions, guards, reset, and the edge cases that expose fake auth.

Verify every API contract and failure path: status codes, error shapes, timeouts, and frontend recovery.

Make your app usable by everyone: keyboard, contrast, labels, focus, and screen-reader basics.

Delegate bounded work to sub-agents that run in fresh context and return only distilled results - the pattern behind scalable long tasks.

Stream model output and tool progress so users see the agent think and act - the UX baseline every serious terminal agent has set.

A decision flow for the most expensive architecture choice in agent systems - grounded in what actually ships versus what demos well.

Byte-identical request prefixes are why forked workers cost a fraction of spawned ones - engineer your dispatch so every parallel child hits the same cache entry.

Design the retry, backoff, and give-up behavior that separates production agents from demos that loop forever.

Build the core agent loop the leading coding agents use: one model, one loop, tools in, results folded back into context.

Apply SWE-agent's key finding: agents perform dramatically better when their commands, viewers, and feedback are designed for models, not humans.

Grow a working agent loop into a product: sessions, config, resumability, and the packaging polish the successful terminal agents share.

Three ways to hand work to a sub-agent - fresh-context spawn, cache-sharing fork, or just doing it yourself - and the decision matrix for picking per task.

Structure your agent's system prompt the way leading harnesses do: layered sections, dynamic context, and enforceable rules.

Connect two agents over an explicit protocol - task handoff, acknowledgment, and failure handling - instead of hoping chat works out.

Worker pool, pipeline, supervisor-worker, adversarial, fork-join, resume chain - build the reusable orchestration patterns every serious multi-agent system converges on.

Give your agent the planning discipline of the leading harnesses: an explicit, model-visible todo list that survives long tasks.

Choose your memory backend on evidence: when plain files win, when vectors earn their complexity, and how to test it on your own data.

Write durable session summaries at the right moments so knowledge outlives compaction and process restarts.

Structure your agent's context for prefix caching: stable prefixes, append-only history, and cache-friendly dynamic content.

Decide what gets remembered: write gates, provenance, confidence, and validation - because memory quality is set at write time.

Make recall surface the right memories: hybrid lexical+semantic search, rank fusion, and explainable retrieval.

Keep agent memory healthy over months: scheduled consolidation, decay, promotion between layers, and safe pruning.

Keep long agent sessions alive by compacting old conversation into structured summaries before the context window fills.

Organize agent memory by kind - what happened, what is true, and how to do things - instead of one undifferentiated pile.

Build the pattern behind minimalist memory systems: a compact index file the agent always loads, pointing to detail files it loads on demand.

Keep long-horizon work coherent across many sessions: externalized project state that any session can load, advance, and hand off.

Give your agent durable memory with plain files - the radical-minimalism approach production practitioners keep converging on.

Stop tool outputs from flooding context: budgets per tool, smart truncation, pagination, and reference-based results.

Classify tools by risk and enforce tiered permissions: auto-allow, session-allow, always-ask, and never - enforced in the harness.

Treat tool descriptions as prompt engineering: when-to-use guidance, negative cases, and worked examples that fix tool-choice errors.

Bring TDD discipline to MCP servers: unit-test handlers, protocol-test the surface, and contract-test against a real host.

Make every tool and agent output validate against a schema: typed results, repair loops, and safe fallbacks.

Write tool schemas that constrain model behavior: tight types, enums over strings, and parameters that make misuse hard.

Stop paying context for tools the task never needs: load a core set eagerly and expand the toolset on demand.

Give your agent compressed, ranked codebase awareness - the tree-sitter repo-map pattern that beats raw file dumps.

Stand up a working MCP server exposing your own tools, connect a client, and validate the full call loop.

Treat prompts, tools, and loop logic as versioned, changelogged artifacts - so behavior changes are decisions, not surprises.

Wire verification into your agent so every edit is checked by machines - syntax, tests, behavior - before it counts as done.

Contain what your agent runs: isolated execution environments with resource limits, network policy, and workspace mounting done right.

Run deterministic policy checks on everything your agent produces: rule files reviewing plans, diffs, and evidence before anything merges.

Cap how much damage any window of agent activity can do: rate limits, change budgets, checkpoints, and undo.

Instrument your agent like production software: traces, session replay, cost tracking, and failure analysis on open standards.

Structure your tool prompts the way the leading harness does: preference chains up front, usage constraints in the middle, NEVER-guarded safety protocols at the end.

Intercept dangerous tool calls with a hook layer: pattern rules, approval gates, and blocks that the model cannot talk its way past.

Build the eval suite that tells you whether harness changes help: task sets, graded rubrics, and regression discipline.

Stop shipping one static mega-prompt: generate tool prompts from environment conditions, inject blocks only when features are on, and dedupe config to save tokens.

Prompts guide, code enforces: inventory your agent's soft rules, back each critical one with an independent deterministic check, and define the allow/ask/deny escalation.

Design approval prompts users actually read: diffs, risk framing, scoped grants, and pacing that prevents approval fatigue.

Route work to the cheapest model that can do it and match reasoning effort to task difficulty - with quality guardrails.

Systematically threat-model an MCP server before it is exploited: assets, entry points, trust boundaries, and mitigations documented and tested.

Keep credentials out of prompts, tool outputs, logs, and traces: detection, redaction, and safe handling across the whole agent pipeline.

Put agent output through a real merge gate: automated review, tests, security scans, and human sign-off proportional to risk.

Systematically attack your own agent using red-teaming frameworks: vulnerability probes, attack methods, and a repeatable adversarial suite.

Systematically defend your agent against prompt injection: trust boundaries, content isolation, and defense-in-depth that assumes injection will happen.

Turn every jailbreak you find into a permanent regression test, so safety boundaries never silently erode across model and prompt changes.

Prepare for the day your agent does damage: detection, containment, rollback, investigation, and prevention - written before you need it.

Stop poisoned data from becoming persistent agent beliefs: validate, attribute, and quarantine memory writes as untrusted input.

Guard against the dependencies your agent adds: vulnerability scanning, hallucinated-package detection, and gated installs.

Catch the attacks your parser cannot see: quote-concatenation flags, ANSI-C quoting, brace expansion, and escaped operators that make bash execute something your checks never inspected.

Replace scattered if-else safety checks with a layered validator pipeline: single-purpose checks, an allow/ask/deny/passthrough contract, and severity-aware ordering.

Record an immutable, queryable trail of every action your agent takes - who, what, when, why, and outcome - for security and accountability.

Stop trusting your agent's own 'it works': dispatch a read-only verifier that must run real commands, probe adversarially, and return an evidence-backed PASS/FAIL verdict.