Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
88
Categories
9
Compatible tools
7
Contributors
2
Showing 22–42 of 88 skills
Defines data contracts between producers and consumers to prevent breaking changes in pipelines. Covers schema definitions, semantic types, freshness and quality SLAs, ownership, versioning, and backward/forward compatibility rules. Generates contract specs (e.g., ODCS-style) and CI checks that fail builds when a producer violates the contract.
Designs and scaffolds ergonomic command-line tools with subcommands, flags, config files, shell completions, and helpful error output. Covers argument parsing conventions, exit codes, stdin/stdout piping, colored output, progress indicators, and cross-platform packaging. Produces maintainable CLIs that follow POSIX conventions and feel great to use.
Designs data quality checks for tables, pipelines, and warehouses. Generates expectation suites covering schema conformance, null and uniqueness constraints, referential integrity, freshness, and statistical drift, then wires them into pipelines so bad data is caught before it reaches dashboards or models.
Turns reliability goals into concrete SLIs, SLOs, and error budgets. Helps choose the right service level indicators, set realistic targets from historical data, compute burn rates, and design multi-window multi-burn-rate alerting so teams get paged before the budget is exhausted.
Turns a script, concept, or product idea into a shot-by-shot storyboard. Breaks the narrative into scenes and panels with framing, camera movement, action, and dialogue notes, and generates image-model prompts for each panel so teams can visualize a video, ad, or explainer before production.
Analyzes cloud spend and produces a FinOps optimization plan. Covers rightsizing, autoscaling, spot/reserved/savings plans, storage tiering, idle resource cleanup, data transfer reduction, and tagging for cost allocation. Prioritizes actions by savings and risk, and defines guardrails and budgets to prevent regressions.
Drafts clear product requirements documents from rough ideas or stakeholder notes. Structures the problem statement, goals and non-goals, user stories, acceptance criteria, success metrics, and open questions, and surfaces ambiguities and edge cases that need decisions before engineering starts.
Configures HTTP security headers to harden web applications. Covers Content-Security-Policy (including nonces and strict-dynamic), HSTS, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, COOP/COEP/CORP, and cookie flags. Produces server/CDN configuration, explains tradeoffs, and provides a rollout plan using report-only mode to avoid breakage.
Adds production-grade observability to a codebase by instrumenting it with structured logs, metrics, and distributed traces. Recommends span boundaries, cardinality-safe labels, and OpenTelemetry conventions, then generates the wiring code and dashboards needed to make a service debuggable in production.
Helps design and implement features for machine learning models from raw tabular, time-series, or text data. Suggests transformations, encodings, aggregations, and leakage-safe splits, explains the rationale, and generates reproducible feature pipeline code with validation.
Diagnoses and fixes flaky tests by analyzing test code and CI failure history for common sources of nondeterminism such as time and timezone dependence, order dependence, shared mutable state, race conditions, and unmocked network calls. Proposes targeted fixes and quarantine strategies to keep the suite trustworthy.
Writes reusable, well-structured Terraform modules with clean input/output interfaces, sensible defaults, validation rules, and examples. Covers module composition, variable typing and validation, remote state and backends, provider version pinning, and testing with terraform validate/plan and tools like Terratest. Emphasizes least-privilege IAM and safe defaults.
Advises on how to evolve APIs without breaking clients. Compares versioning strategies (URI, header, media-type), classifies changes as breaking or non-breaking, and produces deprecation timelines, migration guides, and compatibility shims so teams can ship changes safely.
Models complex application logic as explicit finite state machines and statecharts. Identifies states, events, guards, and side effects; prevents impossible states; and generates implementations (e.g., XState-style) with diagrams. Ideal for wizards, checkout flows, connection lifecycles, and any feature where implicit boolean flags cause bugs.
Writes end-to-end UI tests with Playwright or Cypress that mirror real user journeys. Covers resilient selectors (roles/test-ids), network stubbing, auth setup, fixtures, waiting strategies that avoid flakiness, visual and accessibility assertions, and CI integration. Produces maintainable specs and a page-object structure.
Generates realistic synthetic datasets that preserve the statistical properties and relationships of source data without exposing real records. Covers schema-aware generation, correlated and time-series fields, class balancing for ML training, and constraint preservation, with code for tools like SDV, Faker, or custom generators.
Plans and executes safe dependency upgrades across a project. Analyzes current versions, reads changelogs and release notes for breaking changes, sequences upgrades to minimize risk, applies required code migrations, and verifies with builds and tests. Works across npm, pip, Maven, Cargo, and Go modules.
Designs conversational scripts and dialog flows for voice assistants, IVR systems, and chatbots. Crafts natural prompts and responses, handles confirmations, errors, and re-prompts, designs branching flows and fallbacks, and adapts tone for the brand while keeping turns concise and accessible for audio-first interfaces.
Builds retention and behavioral cohort analyses from event or transaction data. Defines cohorts by acquisition date or attributes, computes retention and churn curves, generates the SQL or pandas code to produce cohort tables, and interprets the results into actionable insights about engagement and lifecycle.
Reviews and rewrites Dockerfiles for smaller images, faster builds, and better security. Applies multi-stage builds, optimal layer ordering and caching, minimal base images, non-root users, and .dockerignore tuning, and flags vulnerabilities and bloat while keeping the build reproducible.
Generates and refactors Helm charts to package Kubernetes applications. Produces templated manifests, values.yaml with sensible defaults, helpers, chart dependencies, and hooks, and parameterizes images, resources, probes, and ingress so a workload can be deployed consistently across environments.
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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