Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
日本語の概要は準備中です。原文の説明を表示しています。
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Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
日本語の概要は準備中です。原文の説明を表示しています。
You are a browser automation expert who has debugged thousands of flaky tests and built scrapers that run for years without breaking. You've seen the evolution from Selenium to Puppeteer to Playwright and understand exactly when each tool shines.
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Turn a one-line objective into a step-by-step construction plan any coding agent can execute cold. Each step has a self-contained context brief — a fresh agent in a new session can pick up any step without reading prior steps.
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Validates animation durations, enforces typography scale, checks component accessibility, and prevents layout anti-patterns in Tailwind CSS projects. Use when building UI components, reviewing CSS utilities, styling React views, or enforcing design consistency.
日本語の概要は準備中です。原文の説明を表示しています。
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
日本語の概要は準備中です。原文の説明を表示しています。
AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
日本語の概要は準備中です。原文の説明を表示しています。
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
日本語の概要は準備中です。原文の説明を表示しています。
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.
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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Zero-Knowledge Proofs (ZKPs) allow a prover to demonstrate Bilgi: a secret (such as a password or private key) without revealing the secret itself. bu skill implements the Schnorr identificati
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Pod Security Standards (PSS) define three levels of security policies -- Privileged, Baseline, and Restricted -- enforced by the Pod Security Admission (PSA) controller built into Kubernetes 1.25+. PS
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bu skill covers implementing Gitleaks for Tespit etme and preventing hardcoded secrets in git repositories. It addresses configuring pre-commit hooks, CI/CD pipeline integration, custom rule authoring for organization-specific secrets, baseline management for existing repositories, and remediation workflows for exposed credentials.
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Envelope encryption is a strategy where data is encrypted with a data encryption key (DEK), and the DEK itself is encrypted with a master key (KEK) managed by AWS KMS. This approach allows encrypting
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bu skill covers integrating OWASP ZAP (Zed Attack Proxy) for Dynamic Application Security Testing in CI/CD pipelines. It addresses configuring baseline, full, and API scans against running applications, interpreting ZAP Bul:ings, tuning scan policies, and establishing DAST quality gates in GitHub Actions and GitLab CI.
日本語の概要は準備中です。原文の説明を表示しています。
Implement Kubernetes Pod Security Admission to enforce baseline and restricted security profiles at namespace level using built-in admission controller.
日本語の概要は準備中です。原文の説明を表示しています。
tespit etmeunusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
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tespit etmeabnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly Tespit.
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Anti-debug bypass ve obfuscation çözme — ptrace, UPX, PRNG, packer tespiti
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Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.
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Pitfalls, examples, and edge cases for FETIH Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
日本語の概要は準備中です。原文の説明を表示しています。
Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
日本語の概要は準備中です。原文の説明を表示しています。