Tech • AI • Robotics • Game
This Claude Code skill puts an end to your subscriptions (pay-per-call)
7/10TL;DR
Agentic is positioned as a single connection layer that gives AI agents access to hundreds of external tools, aiming to replace many one-off subscriptions with pay-per-use API calls and improve speed, accuracy, and automation.
KEY POINTS
A tool layer instead of manual integrations
The main claim is that AI becomes far less effective when it lacks access to external tools and live data. Rather than connecting services one by one, Agentic offers a hub that links an agent to hundreds of APIs through a single setup. The platform is presented as a way to turn a generic chatbot into an agent able to act on the web, query specialized databases, and automate workflows.
- Fast setup with Claude Desktop
The workflow shown uses agentic.cc with Claude Desktop. A connection prompt is copied into a new chat, a setup command is issued for skill + CLI + API via MCP, and the returned URL is used to complete the link. Once connected, an Agentic tool appears inside the assistant, letting prompts explicitly call external capabilities.
More reliable data than ordinary web search
One example compares a simple currency query with a standard web lookup. Instead of relying on a general search that could return stale or hallucinated information, the connected agent calls the ECB exchange-rate tool to convert 10,000 euros into dollars using the current reference rate. The broader argument is that external tools narrow the model’s freedom and improve factual precision.
Heavy focus on business intelligence and ad research
Much of the catalog targets marketers and operators. AdSpy, Meta Ads, LinkedIn Ads, and ad-library scraping are used to inspect live campaigns, gather ad copy, and audit competitors. In one test, an ad audit found 56 ads for Code Lynx, including 44 videos, along with launch timing and creative variants, while noting that deeper performance metrics would still require platform-level access.
Social, SEO, e-commerce, and app-market data
The connected stack also spans Instagram, TikTok, Amazon, Chinese e-commerce sites, App Store Reviews, SEO datasets, company information, finance sources, browser automation, web scraping, image generation, video generation, and voice tools. The value proposition is breadth: users can tap paid data sources only when needed instead of maintaining a long list of separate subscriptions.
Playbooks package prompts into repeatable audits
A notable feature is a library of ready-made “playbooks” for tasks such as AI visibility audits, keyword research, sponsorship discovery, online reputation checks, product-photo research, and competitor monitoring. Before launch, the system can show an estimated price for a task, making the process resemble a menu of prebuilt agent workflows rather than raw API orchestration.
Low per-task costs in the demo
The economics are framed as a substitute for recurring SaaS fees. One comparison cited an ad-research tool with a $19 subscription, arguing that occasional users may be better served by paying per request. Across multiple tests, total spend was reported at $0.81, while one completed report cost around $0.05, suggesting that lightweight audits can be run cheaply if usage remains modest.
Reputation and visibility audits produced mixed but actionable results
Several trial workflows searched the web for public information on Melvin Malerbe, scanning sources such as Reddit, Twitter, Trustpilot, and search results. The audit judged the overall online reputation as mostly positive, found 54 Trustpilot reviews summarized favorably, and flagged a few practical improvements, including replying to a one-star review and creating an “about” page to better control branded search results.
Limits remain despite the broad tool access
Not every search succeeded. Some competitor research returned weak results for smaller creators, and certain datasets appeared biased toward larger players. In other cases, the agent could identify ad volume and creative formats but not infer campaign performance without extra permissions. The platform’s strength lies in discovery and aggregation, but it does not remove every data-access boundary.
Open-source tradeoff and market positioning
Competing “connect your agent to every tool” products were acknowledged, with Agentic described as stronger on user experience and tool breadth, while some rivals are open source. That leaves a familiar tradeoff: convenience and a polished workflow versus the transparency and flexibility of open implementations.
CONCLUSION
The emerging model for AI agents is shifting from standalone chat to connected execution. Platforms such as Agentic suggest that the real productivity gains may come less from bigger models than from giving those models structured access to the right tools, data, and workflows at the moment they are needed.
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