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Found 845 Skills
Security audit and vulnerability scanning for AI agent skills before installation. Detects prompt injection in SKILL.md files, dangerous code patterns (eval, exec, subprocess), network exfiltration, credential harvesting, dependency supply chain risks, file system boundary violations, and obfuscation. Produces PASS/WARN/FAIL verdicts with remediation guidance. Use when evaluating untrusted skills, pre-install security gates, or auditing skill repositories.
Helps engineering managers assess and improve team health across morale, cohesion, delivery culture, and engagement — produces Google's 5 Factors (Project Aristotle), a 4-state team health diagnosis (Falling Behind / Treading Water / Repaying Debt / Innovating), a 5-zone intensity model, the Engagement Stack, the Trust Battery, Teamicide patterns (Peopleware), a blameless postmortem format, and a library of team activities organized by driver. Use when the user says "team morale," "team is struggling," "burnout," "engagement," "attrition risk," "psychological safety," "team dynamics," "something feels off," "team culture," "team is unhappy," "retros aren't working," "team isn't working hard enough," "ideas for team activities," or "how do I run a team offsite." Do NOT use for individual performance concerns (use `managing-high-performers`), team staffing or hiring (use `team-composition`), or individual motivation interventions (use `engineer-motivation`).
Use when generating a Dockerfile for deploying a project to Zeabur. Use when the user needs help writing a Dockerfile for Node.js, Python, Go, Rust, PHP, Ruby, Java, .NET, or Elixir projects. Use when troubleshooting Dockerfile build failures on Zeabur.
Game building mechanics case studies and decision frameworks. Use when designing building systems, evaluating trade-offs, or learning from existing games. Reference-only skill with detailed analysis of Fortnite, Rust, Valheim, Minecraft, No Man's Sky, and Satisfactory building systems.
Optional skill. Reconstruct a human-review-preparation file from an existing pull request, merge request, branch diff, or commit range in a repository the user trusts. Use when the user wants retrospective understanding of already-implemented changes, AI-side assessment and recommendations, and an optional provider-specific sharing variant written to a local file when needed.
Apply a structured judgment and discernment framework to any high-stakes decision, recommendation, or AI-generated output. Use this skill whenever the user wants to think more carefully before committing to something — a people decision, a strategic call, a piece of writing they're about to send, or an AI output they're not sure whether to trust. Trigger on phrases like "is this right?", "am I confident about this?", "help me think this through", "run the discernment framework", "judgment check", "calibration check", "am I being overconfident", "should I trust this output", "premortem", or any time someone is wrestling with whether their thinking is sound. Also trigger proactively when someone appears to be accepting a claim, recommendation, or AI-generated output without questioning it — especially on high-stakes topics like hiring, restructuring, or communications that will reach many people.
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Autonomous polyglot monorepo bootstrap meta-prompt. TRIGGERS - new monorepo, polyglot setup, scaffold Python+Rust+Bun, monorepo from scratch.
Production backend systems development. Stack: Node.js/TypeScript, Python, Go, Rust | NestJS, FastAPI, Django, Express | PostgreSQL, MongoDB, Redis. Capabilities: REST/GraphQL/gRPC APIs, OAuth 2.1/JWT auth, OWASP security, microservices, caching, load balancing, Docker/K8s deployment. Actions: design, build, implement, secure, optimize, deploy, test APIs and services. Keywords: API design, REST, GraphQL, gRPC, authentication, OAuth, JWT, RBAC, database, PostgreSQL, MongoDB, Redis, caching, microservices, Docker, Kubernetes, CI/CD, OWASP, security, performance, scalability, NestJS, FastAPI, Express, middleware, rate limiting. Use when: designing APIs, implementing auth/authz, optimizing queries, building microservices, securing endpoints, deploying containers, setting up CI/CD.
Detect the divergence phenomenon where commodity prices rise but the holdings of corresponding physical ETFs/trusts decline, and use multi-indicator cross-validation to assess the risk of physical supply tightness/delivery pressure.
Guides the agent through Python project management with uv, the fast Rust-based package and project manager. Triggered when users say "create a Python project", "init a Python project with uv", "add a dependency", "manage Python packages", "sync dependencies", "lock dependencies", "run a Python script", "set up pyproject.toml", or mention uv, package management, virtual environments, or Python project initialization.
Evaluate product desirability, market positioning, and emotional resonance—the complement to friction analysis. Assess whether users will WANT a product (not just use it), identity fit, trust signals, and value proposition clarity. Activate on "will they like it", "market positioning", "appeal analysis", "product desirability", "value proposition", "why would someone choose this", "landing page review", "conversion optimization", "messaging strategy". NOT for UX friction analysis (use ux-friction-analyzer), visual design implementation (use web-design-expert), or A/B test setup (use frontend-developer).