Agent Skills
Instruction packs that give your AI agent know-how. Three different kinds — pick the right one below.
✦ Standalone skills4,610
Self-contained. Install one into any project and it works on its own — no other software needed.
🧰 Tool add-ons1,006
Come bundled with a specific tool and only work together with it — they teach your agent how to operate that tool.
getsentry · Official
2,091 standalone skillsworkiq-copilot
✓★ 36,202by github
Guides the Copilot CLI on how to use the WorkIQ CLI/MCP server to query Microsoft 365 Copilot data (emails, meetings, docs, Teams, people) for live context, summaries, and recommendations.
write-coding-standards-from-file
✓★ 36,202by github
Write a coding standards document for a project using the coding styles from the file(s) and/or folder(s) passed as arguments in the prompt.
pester-should-migration
✓★ 36,202by github
Experimental (preview) Pester skill for migrating classic Should -Be (v5) assertion syntax to the new Should-* (v6) assertions (note the hyphen, no space), e.g. `Should -Be` -> `Should-Be`, `Should -Not -Be` -> `Should-NotBe`. Tracks Pester 6, which is still a release candidate, so this guidance may change; verified against Pester 6.0.0-rc2. Use when converting Pester v5 assertions to Pester v6 Should-* operators, modernizing a Pester test suite, or when a user asks to migrate, convert, or rewri
prompt-optimizer
✓★ 36,202by github
Turn any rough prompt, half-formed idea, or task description into a finished, ready-to-send prompt optimized for any LLM model inside a chat interface — NOT the API. Use this skill whenever the user wants to write, rewrite, optimize, improve, sharpen, or polish a prompt for chat. Trigger phrases include "rewrite this prompt", "make this a better prompt", "optimize this prompt", "turn this into a prompt", "help me prompt this", "draft a prompt that...", "I want to ask...", or whenever the user pa
screen-recording
✓★ 36,202by github
Create annotated animated GIF demos and screen recordings for pull requests and documentation. Covers frame capture, timing, imageio-based GIF creation, and per-frame annotation workflows.
technical-job-search
✓★ 36,202by github
Use this skill when a software engineer asks for help with job search tasks: parsing or analyzing a job description, tailoring a CV/resume, writing a cover letter, evaluating a job offer, or drafting a post-interview follow-up email. Do not activate for general career advice unrelated to an active job search action.
lsp-setup
✓★ 36,202by github
Enable code intelligence (go-to-definition, find-references, hover, type info) for any programming language by installing and configuring an LSP server for Copilot CLI. Detects the OS, installs the right server, and generates the JSON configuration (user-level or repo-level). Use when you need deeper code understanding and no LSP server is configured, or when the user asks to set up, install, or configure an LSP server.
power-bi-report-design-consultation
✓★ 36,202by github
Power BI report visualization design prompt for creating effective, user-friendly, and accessible reports with optimal chart selection and layout design.
acquire-codebase-knowledge
✓★ 36,202by github
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
add-educational-comments
✓★ 36,202by github
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
agent-governance
✓★ 36,202by github
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrict
agent-supply-chain
✓★ 36,202by github
Verify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verif
apple-appstore-reviewer
✓★ 36,202by github
Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.
ai-team-orchestration
✓★ 36,202by github
Bootstrap and run a multi-agent AI development team. Use when: starting a new software project with AI agents, setting up parallel dev/QA teams, creating sprint plans, writing brainstorm prompts with distinct agent voices, recovering a project workflow, or planning sprints.
breakdown-test
✓★ 36,202by github
Test Planning and Quality Assurance prompt that generates comprehensive test strategies, task breakdowns, and quality validation plans for GitHub projects.
arize-evaluator
✓★ 36,202by github
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
arize-experiment
✓★ 36,202by github
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
create-agentsmd
✓★ 36,202by github
Prompt for generating an AGENTS.md file for a repository
arize-instrumentation
✓★ 36,202by github
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
arize-link
✓★ 36,202by github
Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.
create-implementation-plan
✓★ 36,202by github
Create a new implementation plan file for new features, refactoring existing code or upgrading packages, design, architecture or infrastructure.
arize-prompt-optimization
✓★ 36,202by github
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
arize-trace
✓★ 36,202by github
Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
autoresearch
✓★ 36,202by github
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy''s autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, auto
agentic-workflows
✓★ 36,202by github
Route gh-aw workflow design/create/debug/upgrade requests to the right prompts.
csharp-nunit
✓★ 36,202by github
Get best practices for NUnit unit testing, including data-driven tests
aws-cloudwatch-investigation
✓★ 36,202by github
Reusable investigation patterns for AWS CloudWatch: Logs Insights query templates, alarm-to-deployment correlation, blast-radius narrowing decision tree, and PromQL-style metric query patterns for structured incident triage.
aws-cost-optimize
✓★ 36,202by github
Analyze AWS resources used in the app (IaC files and/or resources in a target account/region) and optimize costs - creating GitHub issues for identified optimizations.
aws-resource-health-diagnose
✓★ 36,202by github
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.
aws-resource-query
✓★ 36,202by github
Query AWS resources using natural language. Covers EC2, S3, RDS, Lambda, ECS, EKS, Secrets Manager, IAM, VPC, networking, messaging, and more. Strictly read-only — no writes, deletes, or mutations.