Loading...
Loading...
Found 1,747 Skills
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
Use this when you need to evaluate the risks and benefits of accepting, negotiating before accepting, pausing, or rejecting outsourcing projects, internal projects, or requirements. It is particularly suitable for scenarios with ambiguity in scope, acceptance criteria, payment terms, compliance, project timelines, or dependencies, as well as high-uncertainty situations such as emergency task insertion, contract renewal/modification, multi-requirement prioritization, or AI/LLM-related initiatives.
Tests OAuth 2.0 and OpenID Connect implementations for security flaws including authorization code interception, redirect URI manipulation, CSRF in OAuth flows, token leakage, scope escalation, and PKCE bypass. The tester evaluates the authorization server, client application, and token handling for common misconfigurations that enable account takeover or unauthorized access. Activates for requests involving OAuth security testing, OIDC vulnerability assessment, OAuth2 redirect bypass, or authorization code flow testing.
Check for security risks in Skills/code repositories. When the user wants to check if a skill, GitHub repository, npm package, or local code is safe to download or use. This includes detecting malicious code, malware, key stealing, environment variable modification, suspicious network behavior, and evaluating repository reputation (stars, forks, contributors, age). Use this skill whenever the user mentions checking skills for security risks, scanning repositories for malware, verifying code safety, checking npm packages for threats, or asking if a download is safe.
Consult this skill for Python testing implementation and patterns. Use when writing unit tests, setting up test suites, implementing TDD, configuring pytest, creating fixtures, async testing, writing integration tests, mocking dependencies, parameterizing tests, setting up CI/CD testing. Do not use when evaluating test quality - use pensive:test-review instead. DO NOT use when: infrastructure test config - use leyline:pytest-config.
Conduct a comprehensive SWOT analysis to audit organizational position. Use when assessing strategic fit, evaluating competitive position, or informing strategic direction.
Crypto.com Exchange Spot request using the Crypto.com Exchange API. Authentication requires API key and secret key. Supports production and UAT sandbox.
Invoke MassGen's multi-agent system for general-purpose tasks, evaluation, planning, or spec writing. Use whenever you want multiple AI agents to tackle a problem, need outside perspective on your work, a thoroughly refined plan, or a well-specified set of requirements. Perfect for: writing, code generation, research, design, analysis, pre-PR review, complex project planning, feature specification, architecture decisions, or any task where multi-agent iteration produces better results than working alone.
Weighted decision scoring framework for architectural and technology choices. Frames decisions with 2-4 options, scores against weighted criteria, detects close calls, and records decisions in the active ADR or task plan. Use when: "should I use X or Y", "which approach", "compare options", "trade-offs between", "help me decide", "evaluate alternatives"
Prioritization frameworks — RICE, WSJF, ICE, MoSCoW, and opportunity cost scoring for backlog ranking. Use when prioritizing features, comparing initiatives, justifying roadmap decisions, or evaluating trade-offs between competing work items.
Porter's Five Forces, SWOT analysis, and competitive landscape mapping. Use when analyzing market position, evaluating competitive threats, building battlecards, or assessing industry dynamics.
A/B test evaluation, cohort retention analysis, funnel metrics, and experiment-driven product decisions. Use when analyzing experiments, measuring feature adoption, diagnosing conversion drop-offs, or evaluating statistical significance of product changes.