figmaAug 13, 2026

Try These 10 Skills—And Show Off Your Own | Figma Blog (opens in new tab)

Skills are plain-text Markdown instructions that help Figma’s agent apply a designer’s preferences, workflows, and judgment. The post argues that skills are useful not only for engineers but also for codifying creative approaches, such as easing curves, typography, image audits, and motion principles. Figma now lets users create, preview, edit, and iterate on skills directly in the product, with some also compatible with Figma’s MCP server. ## What Figma skills enable - Designers can create skills from: - A reference design frame - Specific instructions or guidelines - Repeated personal workflows - The agent can draft a skill, which users can preview and test in chat. - Skills can be refined by: - Editing instructions and formatting - Asking the agent for recommendations - Opening the Markdown editor for manual changes - Skills can capture individual design judgment and help teams reproduce a consistent point of view. - Some skills work with Figma’s MCP server and third-party agents. ## Examples of personal design skills - `/image-audit` checks whether images in a file can be exported and places a report on the canvas. - `/shader-starter` helps users write more effective shader prompts. - `/make-a-type-scale` creates a type scale from selected text and generates corresponding font variables. - `/ease-like-miggi` applies the author’s preferred easing curves to motion keyframes, allowing teammates to use the same motion style. ## `/xray-scan`: Create radiographic image effects Created by Adele Ix, this skill transforms a photo or description into a realistic X-ray image. - Produces two visual treatments: - Glowing white lines on black - Darker tones on white - Supports layered monochrome collages with glowing edges. - Is especially suited to combining multiple scans into a unified composition. ## `/find-animation-opportunities`: Identify missing motion Created by Emil Kowalski of Linear, this skill reviews a design and suggests where animation could improve the experience. - Identifies elements that should animate and areas that may be over-animated. - Recommends: - What should move - Where the motion should occur - Exact animation values - Suggestions can be implemented manually in Figma Motion or applied by the agent. - Works best when focused on one frame with contextual information, such as the target persona. - It can also be asked which elements should remain static. ## `/camera-pan-and-zoom`: Animate static compositions Created by Mallory Dean, this skill adds camera-like movement to a static design. - Builds a nested camera rig around selected content. - Supports pan, zoom, and roll animations. - Can create directed sequences, such as touring a grid of cards before zooming out to reveal the full composition. - The skill is intended to make static compositions feel more cinematic and dynamic. ## Practical takeaway Designers can use skills to turn personal techniques and repeatable creative decisions into reusable tools. Start with a focused workflow, test the generated skill in Figma, and refine its Markdown instructions until it reliably produces the desired result.

mcp-serverfigmadesign-systemsai-agent+2

figmaAug 3, 2026

Code, Craft, and the Making of Nested Folders | Figma Blog (opens in new tab)

Figma’s nested folders feature required much more than a file-browser update: it involved rethinking content structure, permissions, sharing, administration, and core infrastructure. The team responded to changing priorities and faster AI-assisted development by replacing a sequential product process with code-led experimentation, flexible roles, and continuous collaboration. This approach helped them turn early ideas into working solutions and ship a complex, long-requested feature. ## Nested folders required foundational changes - Nested folders help growing teams organize files within shared projects. - Implementing them affected: - The file browser - Admin controls - Sharing and permissions - Figma’s underlying content model - Core infrastructure - The feature was therefore treated as a systems-level redesign rather than a simple quality-of-life improvement. ## Moving beyond the traditional product process - Initially, the team followed a conventional sequence: - Product defined requirements. - Design mapped the user experience. - Engineering started implementation after the concepts were considered complete. - Midway through the project, priorities shifted as resources were also needed for AI-native initiatives. - Tools such as Figma Make, the Figma MCP server, agent skills, codebase prototyping, and context-aware agents made it possible to begin work from prototypes, code, or even informal ideas shared in Slack. ## Leading with code - As code became faster and cheaper to produce, the team used pull requests as a way to explore product ideas. - Working prototypes and PRs helped the team test assumptions directly instead of spending too long debating theoretical solutions. - Code became a proposal and a communication tool, not merely the final implementation step. ## Flexible roles and shared ownership - Responsibilities shifted according to the problem being solved: - Engineers made design decisions. - The designer contributed code. - The product manager focused less on daily execution and more on strategic questions. - This flexibility allowed the team to respond quickly as technical and product requirements evolved. ## Replacing handoffs with conversations - Blurred roles turned team members into both students and teachers. - Instead of relying on formal handoffs between product, design, and engineering, the team collaborated continuously. - Sharing specialized knowledge and previously guarded parts of each craft helped build the trust needed to make decisions quickly. The broader lesson is that complex product work benefits from treating code, design, and product thinking as shared responsibilities. For teams building foundational features under changing conditions, early prototypes, cross-functional ownership, and ongoing conversation can be more effective than rigid sequential handoffs.

mcp-serverfigmaai-agentcode-prototyping+2

lineApr 21, 2026

ODW #3: Boosting Development Efficiency by Safely Utilizing MCP Servers (opens in new tab)

LY Corporation is expanding AI use across its engineering organization through MCP servers, which connect AI assistants with internal and external tools through a common protocol. The company combines this flexibility with allowlists, automated security checks, and internal standards to reduce risk. Its Orchestration Development Workshop demonstrates practical applications such as Jira ticket automation and multi-agent code reviews, while emphasizing shared learning and experimentation as AI practices evolve. ## MCP Servers and Their Benefits - MCP servers act as translators between AI assistants and external systems. - Before MCP, each assistant required a separate integration for every tool. - With MCP, a tool can implement one standardized interface and work with multiple compatible assistants. - This improves interoperability, scalability, and the ability to combine different AI tools. ## Security Risks and LY Corporation’s Controls - A 2025 Astrix Security report found that: - More than 5,200 public MCP servers were analyzed. - 53% relied on long-lived static API keys or personal access tokens. - Only 8.5% used newer authentication methods such as OAuth. - LY Corporation manages externally developed MCP servers through: - An allowlist permitting only approved servers. - Automated security verification based on internal standards. - Internal MCP servers for groupware and business systems are built to meet the company’s security requirements. - Centralized infrastructure lets teams focus on applying AI rather than independently rebuilding integrations and controls. ## Workshop Applications The Orchestration Development Workshop taught participants how to understand, configure, and safely apply MCP servers with AI assistants. - Topics included MCP fundamentals, security risks, internal policies, development rules, and configuration in Claude and Cline. - The internal plugin marketplace was introduced as a way to distribute MCP configurations. - Participants practiced using Claude Code with the internal groupware MCP server to: - Generate a Jira ticket title and summary. - Create the ticket automatically. - The exercise showed how AI can remove repetitive administrative work and free time for higher-value tasks. ## Multi-Agent Code Review Demonstration - A demonstration combined Claude Code, Codex CLI, Context7 MCP, and Codex MCP. - A Sonnet-based agent first analyzed a pull request, including: - Technical stack and relevant documentation. - Code changes and repository context. - Security, performance, and code-quality concerns. - GPT-5 then validated the initial review, identifying missed issues and checking the prioritization of findings. - Using different models provided more varied and potentially objective perspectives on the same code. ## Results and Organizational Learning - Around 1,600 people attended the workshop in real time. - 31.5% had already applied related techniques before the event. - Another 55.7% planned to try them soon. - LY also created “Help LY MCP,” a GPTs-based tool that explains internal MCP rules and helps teams assess whether proposed uses are suitable, including for global subsidiaries. - The workshop’s broader purpose was to create a shared understanding of: - What AI and MCP can currently do. - What risks and pitfalls exist. - How to use the technology meaningfully. ## Continuing to Experiment The article concludes that rapidly changing AI technology makes shared experimentation more valuable than simply announcing new tools. MCP may eventually be surpassed by other approaches, such as skills, so teams should continually reassess the best solution. LY recommends creating a culture where employees can safely try small ideas, learn together, and adapt as new practices emerge.

mcp-serverdatabase-designgithubclaude-code+4

datadogMar 4, 2026

Designing MCP tools for agents: Lessons from building Datadog's MCP server | Datadog (opens in new tab)

Datadog is presented as a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. The provided content, however, consists almost entirely of Datadog’s website navigation rather than the blog post itself, so it does not include Gartner’s evaluation criteria, Datadog’s strengths, or any supporting analysis. ## Gartner Recognition - The page headline announces Datadog’s “Leader” position in the Gartner Magic Quadrant for Observability Platforms. - A link is provided to a Gartner-related resource page. - No ranking details, competitor comparisons, or Gartner commentary are included in the supplied text. ## Datadog’s Product Coverage The navigation indicates that Datadog offers a broad observability and operations platform spanning: - **Infrastructure:** infrastructure, container, network, serverless, GPU, storage, and cloud-cost monitoring. - **Applications:** APM, service monitoring, profiling, dynamic instrumentation, and agent observability. - **Data and logs:** database, data-stream, data-quality, job, log, and sensitive-data monitoring. - **Digital experience:** browser and mobile RUM, session replay, synthetic monitoring, product analytics, and error tracking. - **Security:** code, cloud, workload, vulnerability, compliance, SIEM, and application/API protection. - **Software delivery and service management:** CI visibility, testing, developer portals, incident response, SLOs, workflows, and case management. - **AI:** agent observability, GPU monitoring, AI integrations, Bits AI agents, and an MCP server. ## Limitations of the Provided Content - The actual article body is absent. - The text does not explain why Gartner recognized Datadog as a Leader. - It provides no technical findings, customer examples, methodology, or conclusions beyond the headline. The supplied excerpt supports only the conclusion that Datadog announced Gartner recognition and positions itself as a comprehensive observability platform. A substantive summary would require the full article text.

mcp-serverk8sai-agentdatadog+3