Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
167
Categories
9
Compatible tools
5
Contributors
1
Showing 1–21 of 100 skills
Produces operational runbooks for services and common incidents. Documents prerequisites, step-by-step diagnosis and remediation, exact commands, verification checks, rollback steps, and escalation paths. Structures each runbook so an on-call engineer can follow it under pressure, and keeps destructive steps clearly flagged with safeguards.
Translates requirements and user stories into behavior-driven development scenarios in Gherkin. Writes clear Given/When/Then steps, covers happy paths, edge cases, and negative cases, uses scenario outlines with examples for data-driven tests, and keeps steps declarative and reusable. Optionally scaffolds step definitions for Cucumber or Behave.
Inspects messy tabular data and produces a repeatable cleaning plan plus code. Detects and fixes common issues: inconsistent types, duplicate rows, missing values, malformed dates, mixed encodings, whitespace and casing problems, and outliers. Outputs pandas or Polars code, a summary of changes, and a validation checklist.
Generates and refactors dbt models for analytics engineering. Writes staging, intermediate, and mart models following layered conventions, adds schema.yml tests and descriptions, applies incremental and materialization strategies, and structures sources and refs correctly. Produces SQL plus YAML that fits dbt best practices and is ready to run.
Produces structured test plans for features and releases. Defines scope and objectives, derives test cases from requirements and acceptance criteria, covers functional, edge, negative, performance, and accessibility cases, sets entry/exit criteria, and maps risk to test priority. Outputs a clear plan with a traceability matrix linking tests to requirements.
Drafts email newsletters that get opened and read. Writes subject lines and preview text, structures the issue with a clear lead, scannable sections, and a single primary call to action, and adapts tone to the audience. Can turn a list of updates or links into a cohesive issue and suggest send-time and segmentation tips.
Designs caching strategies across the stack to cut latency and load. Chooses cache layers (browser, CDN, application, database), picks patterns (cache-aside, read-through, write-through, write-behind), sets TTLs and eviction policies, and plans invalidation to avoid staleness and stampedes. Produces a layered plan with keys, TTLs, and invalidation rules.
Drafts long-form blog posts from a topic, outline, or set of notes. Handles title and hook generation, logical section structure, SEO-aware headings, tone matching, internal linking suggestions, and a clear call to action. Produces publish-ready Markdown with meta description and suggested tags.
Generates candidate names for products, features, projects, or companies against a clear brief. Explores naming styles (descriptive, evocative, coined, compound), checks each for pronounceability and unwanted meanings, and suggests domain and handle patterns to verify. Returns a shortlist with rationale rather than an undifferentiated list.
Writes idempotent Ansible playbooks and roles from a described target state. Structures tasks with proper handlers, variables, and templates; favors modules over shell commands; applies role-based layout and inventory grouping; and adds check-mode safety and tags. Produces playbooks that are re-runnable without unintended side effects.
Plans authorized penetration tests for systems you own or are permitted to assess. Defines scope, rules of engagement, and objectives; maps the attack surface; structures phases (recon, mapping, exploitation, post-exploitation, reporting) around a framework like the OWASP Testing Guide or PTES; and specifies safe handling of findings. Emphasizes explicit authorization and non-destructive testing.
Produces production-ready nginx configuration for common scenarios: reverse proxy, load balancing, TLS termination, static file serving, HTTP/2, gzip/brotli, caching, rate limiting, and security headers. Explains each directive, warns about risky defaults, and includes a validation step so the config can be tested before reload.
Designs and implements visual regression testing for web UIs. Recommends a tooling approach (Playwright snapshots, Storybook + a diffing service, or a dedicated platform), writes screenshot tests with stable selectors and masked dynamic regions, sets sensible diff thresholds, and integrates the suite into CI with baseline management to reduce flaky failures.
Reviews and rewrites resumes and CVs to be clear, achievement-focused, and ATS-friendly. Rewrites bullet points using strong action verbs and quantified impact, aligns wording to a target job description, flags gaps and red flags, and checks formatting for applicant tracking system compatibility.
Builds disaster recovery and business continuity plans for systems and data. Defines RTO and RPO targets, maps critical dependencies, chooses backup and replication strategies, documents failover and restore procedures, and designs a testing cadence with game days. Produces a plan that balances resilience against cost and operational complexity.
Turns customer results into a persuasive, credible case study. Structures the story as challenge, solution, and measurable results, weaves in quotes and concrete metrics, keeps claims verifiable, and ends with a clear call to action. Produces a publish-ready draft plus a one-paragraph summary and pull-quote suggestions.
Sets up and maintains Git hooks for a repository. Recommends a manager (Husky, Lefthook, or pre-commit), wires up pre-commit and commit-msg hooks for linting, formatting, type checks, secret scanning, and conventional-commit validation, and keeps hooks fast with staged-file filtering. Produces config plus a short contributor guide.
Generates platform-tailored social media posts from a single idea or announcement. Adapts length, tone, hashtags, and formatting for X, LinkedIn, Instagram, and threads. Produces hook-first copy, variant options for A/B testing, and a suggested posting cadence while respecting each platform's conventions and character limits.
Builds discovery and usability interview guides that surface real insight. Translates research questions into open, non-leading prompts, sequences warm-up to deep-dive topics, adds follow-up probes, and applies techniques like the "five whys" and past-behavior questions. Outputs a timed guide with a consent intro and a synthesis template for notes.
Turns analysis results into a clear narrative for a specific audience. Selects the key message, orders findings for impact, recommends the right chart for each point, writes plain-language takeaways, and frames actionable recommendations. Helps analysts move from raw numbers to a memo or slide narrative executives can act on.
Creates accessible color palettes for brands and UIs from a brief or a seed color. Produces primary, secondary, and neutral scales with hex values, suggests semantic tokens (success, warning, error, info), and checks foreground/background pairings against WCAG contrast ratios. Outputs ready-to-use CSS variables or design tokens.
Skills vs MCP servers
what's the difference?Skillsthe “what to do”
A skillA reusable, structured prompt/workflow with recommended models, an example prompt, and compatible tools. packages know-how — instructions, an example promptA ready-to-use prompt template that demonstrates how to invoke the skill., and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP serverModel Context Protocol server — a standard way to expose tools, resources, and prompts to AI agents and IDEs. gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transportHow the client talks to the server: stdio (local process), SSE, or HTTP streaming.. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
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