AgenticNode is the composition and memory layer on top of Claude Code, Cursor, and Copilot. Design the task as a visual workflow, and it composes one rich hand-off prompt — from your registered prompts, project context, house rules, and past decisions. Copy it. Paste it. Your agent does the rest.
47
workflow templates
4
target platforms
0
tokens spent on our side
Prompt Composer — Bug Fix Workflow
47 built-in templates
Bug fixing, security audits, CI/CD, docs, migrations — each composes into a ready-to-paste prompt.
Platform-aware output
The same workflow composes differently for Claude Code, Cursor, Copilot, or plain Markdown.
We never spend your tokens
Prompts are composed client-side. Your agent executes on your machine, with your subscription.
Design the task as a node graph — phases, tools, success criteria. The composer transcribes it into one structured, copy-pasteable prompt.
Bind workflow nodes to registered prompts by slug and environment. The composer inlines the deployed, evaluated version at compose time.
House rules, tone, project context, and decisions pasted back from past hand-offs fold into every composition — your agent stops re-litigating settled choices.
The Hand-off Loop
Every editor runs a model now — the scarce skill is composing the right prompt. AgenticNode turns that ad-hoc craft into a first-class, visual, versioned artifact: the hand-off. One ready-to-paste prompt that carries the full task structure, your conventions, and your history.
What gets folded into a hand-off
Prompt Management
Most prompt tools live outside your workflow — a separate dashboard you paste from by hand. AgenticNode's registry is a first-class node type: bind a workflow step to a registered prompt by slug and environment, and the composer inlines the deployed, versioned, evaluated content into the hand-off at compose time. Promote a version to production in Studio and every future composition picks it up — no redeploy, no stale copies. Your own apps can fetch the same deployed prompts through the runtime API.
Node inspector — AI step
v4·production·claude-sonnet-4-6
Usage (7d)
1,284 requests
Eval pass rate
94%
Avg latency
612ms
Environments
prod · staging
Start from 47 battle-tested workflows. Each one composes into a ready-to-paste hand-off prompt for your agent.
Investigate bug reports, diagnose root cause, implement fix with tests
Comprehensive code review with security scan and test verification
Fast code review of recent changes with quality and security checks
Scaffold a new project with structure, configs, and initial code
Analyze code quality, identify improvements, refactor with tests
Analyze coverage gaps and generate comprehensive tests
Generate unit and integration tests for existing code
OWASP Top 10 scan, dependency audit, and vulnerability report
Check for vulnerable, outdated, or unused dependencies
Auto-generate API docs, README, and inline documentation
Profile code, identify bottlenecks, suggest optimizations
Version bump, changelog, build verification, tag creation
Design, implement, and test API integrations with external services
Plan and generate database migrations with rollback support
Generate CI/CD pipeline configs for GitHub Actions, GitLab CI, and more
Deep dive into any codebase — explain architecture, patterns, and flow
Convert a PRD into a structured, ready-to-execute prompt for your coding agent
Turn a feature description into a PR-ready plan with file changes, tests, and rollout notes
Migrate JS or loose-TS to strict TypeScript file by file with type fixes
Add Stripe subscription billing with checkout, webhooks, and tier gating
Wire Supabase auth with email + OAuth, RLS policies, and session middleware
Add theme toggle with persistence, system preference, and no flash on load
WCAG 2.1 AA audit with prioritized fix plan
Add Sentry with source maps, release tracking, and performance monitoring
Find N+1 patterns in your ORM and propose batched/joined fixes
Find unused exports, unreferenced files, and dead branches
Identify and diagnose flaky tests; propose deterministic fixes
Generate a CHANGELOG.md entry from git commits, grouped by type
Design and scaffold a new MCP tool with schema, handler, and tests
Design a RAG pipeline: chunking, embeddings, vector store, retrieval, reranking
Generate reviewer-friendly PR descriptions from a diff
Initialize a monorepo with workspaces, shared configs, and build pipeline
Hand off mid-task work between Cursor, Claude Code, Copilot — produces clean state file and tailored prompt
Generate the .well-known/mcp-server-card.json required for MCP v2.1 registry discoverability
Add token-by-token streaming with cancellation, error recovery, and progressive markdown
Scan dependencies for malicious packages, typosquatting patterns, and CI/CD secret exposure risk
Wire persistent cross-session memory into an existing AI agent using Mem0 — store, retrieve, and summarize
Instrument an AI application with OTel spans for LLM calls, agent steps, and tool invocations
Find cacheable prompt prefixes in your AI app and add provider caching to cut LLM costs 60-80%
Diagnose CI build failures with AI — root cause analysis, breaking change location, and a fix plan or patch
Audit your release workflow for branch-hijack and import-time payload risks that postinstall-only scanners miss
Diagnose why an autonomous coding agent run went wrong and produce a root-cause fix from its tool-call transcript
Install a pre-execution hook that blocks destructive shell, git, and cloud commands before your AI coding agent runs them
Add the MCP Tasks extension to an existing server for long-running ops and human-in-the-loop approvals
Wrap your AI SDK calls with automatic multi-provider fallback so an outage or rate limit on one provider does not take the feature down
Have an AI agent find, confirm real exploitability of, and patch vulnerabilities in your changed code
Audit and speed up the lint/typecheck/test loop your coding agent depends on to self-correct
Open the editor, sketch the task as nodes, and copy a composed prompt straight into the agent you already use.
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