Anthropic's Claude Opus 4.5 agents automate social posts with new Skills

A recent walkthrough demonstrates how Anthropic's Claude Opus 4.5 agents automate social posts with new Skills, pairing the model with a light wrapper called "Claude Code." This article unpacks the demo, highlights key performance gains, and provides a roadmap for teams looking to build functional agentic prototypes.

Inside the live demo

The agentic workflow uses modular Skills - discrete, reusable functions like "WriteTweet" or "UploadMedia" - exposed via an OpenAPI schema. The Opus 4.5 model plans a strategy, selects the appropriate Skills in sequence, and executes the task, adapting its plan if it encounters errors like missing image captions.

In the demo, developers start with an Opus 4.5 instance on a medium "effort" setting. This configuration balances performance and efficiency, resolving 80.9 percent of SWE-bench bugs with 48-76 percent fewer tokens than Sonnet 4.5, according to Claude Opus 4.5 Benchmarks. The lower token usage ensures latency stays below two seconds for chained tool calls. The agent then uses a container of Skills ("WriteTweet," "UploadMedia," "PostTweet") to plan and execute a social media post, successfully handling dynamic issues like missing alt-text before publishing to X.

Why Skills matter

Using modular Skills is crucial for building reliable and secure agents. Research from AIMultiple indicates that enterprises see productivity gains of 20-30 percent by adopting skill-based agents over monolithic scripts Agentic AI Trends. The demo highlights three core benefits of this approach:

• Hot-swap design: Teams can update individual Skills without taking the entire agent offline.
• Fine-grained permissions: Each Skill can be assigned narrow OAuth scopes, enhancing security over a single, broad API key.
• Enhanced tracing: Every Skill logs its inputs and outputs, providing clear observability for debugging and audits.

Performance levers in Opus 4.5

Opus 4.5 includes several architectural upgrades that boost agentic performance:

1. Persistent thinking blocks maintain context across long, complex task chains, preventing the contextual drift seen in previous models.
2. A zoom action enables the agent to inspect small UI elements at full resolution, overcoming issues from minor HTML changes.
3. Built-in prompt-injection defenses harden the agent against malicious inputs, reducing the reliance on custom sanitization layers.

Reproducing the workflow

Teams can replicate this social media automation workflow in under an hour by following these steps:

• Enable Opus 4.5 access and generate an Anthropic API key.
• Create a Node script that provides the model with a JSON schema defining your custom Skills.
• Develop three basic Skills: one to draft copy, one to upload media, and one to publish the post.
• Provide the agent with a clear goal, such as: "Share today's patch release on X with an image and alt-text."
• Review the agent's intermediate reasoning logs to ensure its plan aligns with compliance requirements.

Looking ahead

This agentic model, where business logic is encapsulated in Skills and core reasoning is handled by the LLM, creates a highly extensible system. It allows for future enhancements like post scheduling, content localization, or adding a human review queue without requiring a full rewrite of the agent's core architecture.

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How does Opus 4.5 turn a single prompt into a finished social post on X?

Inside the walkthrough, the agent receives a natural-language goal such as "Promote our new product launch on X this afternoon."
- It breaks the goal into subtasks - generate copy, pick a visual, choose the best time, and publish
- Each subtask is handed to a pre-built Skill (one Skill can call X's API, another can read your media folder, etc.)
- The agent keeps context across turns, so if you change the hashtag strategy at the last minute it rewrites only the copy, not the whole plan

Builders see the whole sequence in Claude Code's trace panel, making it easy to swap Skills or insert approval steps.

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What exactly is a "Skill" in Claude-land, and how is it different from a plain function call?

A Skill is a packaged, reusable module that lives in the agent's workspace:
- It exposes a short manifest (name, input schema, required scopes) so the agent can discover and chain it like a LEGO brick
- Skills can wrap external APIs, local scripts, or browser automation drivers
- They support version pins and shared secrets, letting ops teams update credentials once and every agent benefit

This beats ad-hoc code because the agent reasons about when to invoke a Skill and falls back gracefully if an API times out.

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How reliable is the automation when the agent is left unsupervised for 30-minute windows?

Opus 4.5 scores 80.9% on SWE-bench and 59.3% on Terminal-Bench, showing strong autonomous accuracy.
- During long sessions, the model keeps a persistent thinking block that survives turns, so it remembers earlier decisions and avoids duplicate posts
- If a Skill returns an unexpected code (e.g., X replies with "duplicate status"), the agent replans instead of hard-failing, often inserting a slight text variation or new emoji

Early adopters report 50-75% fewer manual interventions compared with previous Sonnet-based agents.

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Can I prototype an agent today without rewriting my whole stack?

Yes. The walkthrough starts in Claude Code (a local CLI wrapper) and re-uses everyday tools:
- A one-line npm install pulls the Anthropic SDK
- Skills are stored as YAML + Python files in a /skills folder; drop your own scripts there and they appear in the planner immediately
- Because the agent respects an "effort" parameter (low/medium/high), you can stay frugal on tokens while experimenting, then flip to "high" for production runs

No cloud overhaul needed - the demo posts to X from a laptop in under 15 minutes.

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Where is this headed in 2026, and why should operators care?

Industry analysts expect 20-30% productivity gains in enterprises that deploy modular agents across marketing, finance, and support.
- Modular agents like those shown with Opus 4.5 already handle multi-day dev projects in hours; social posting is the gateway use-case
- Forward-thinking teams are budgeting for "agent ops" - monitoring, Skill versioning, and audit trails - rather than one-off scripts

Early movers gain pricing leverage: platforms are starting to meter by task completion, so efficient agent design lowers run-time bills as well as head-count.