Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 600 files from 1 763 authors, of which 61 947 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Web browser automation with AI-optimized snapshots for claude-flow agents
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
Drive a PR to an approved code review by looping OCR's multi-agent review and address steps. Runs /ocr:review then /ocr:address repeatedly until the review verdict is APPROVE, then one final /ocr:address for leftover suggestions, posting every review and every address round to the GitHub PR as comments. Use when the user asks to 'review and address in a loop', 'iterate review until approved', 'auto review-and-fix this PR', 'loop /ocr:review and /ocr:address', or to take a changeset all the way to a clean APPROVE with the default reviewer team. Wraps the /ocr:review (.ocr/commands/review.md) and /ocr:address (.ocr/commands/address.md) skills; needs a feature branch with an open GitHub PR and the gh CLI for posting.
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.
CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.
Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.
Complete security architecture overhaul for claude-flow v3. Addresses critical CVEs (CVE-1, CVE-2, CVE-3) and implements secure-by-default patterns. Use for security-first v3 implementation.
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
| AI-powered multi-agent code review. Simulates a team of Principal Engineers reviewing code from different perspectives. Use when asked to review code, check a PR, analyze changes, or perform code review.
> Closed-loop fault injection and attribution accuracy benchmark. Draws from a prioritized pool of (fault_type, rank, iter, nodes) experiments and submits them 2 at a time via sbatch — waiting for each pair to finish before submitting the next — to bound filesystem load. GPU-related faults are front-loaded in the pool. After all jobs complete, runs /log-analysis and /fr-analysis on every experiment, scores attribution vs. ground truth, aggregates gaps, and iterates on attribution modules to close them.
> Orchestration layer over nvidia_resiliency_ext attribution modules. Provides log-analysis, fr-analysis, and a Megatron-LM-oriented fault-injection feedback loop for benchmarking attribution quality on SLURM workloads.
> Analyze a SLURM job log file for failure root-cause attribution and restart decisions using NVRxLogAnalyzer. Use when you have a SLURM training job log and need to determine why the job failed and whether it should be restarted. Performs per-cycle chunking, fast-path pattern matching, and LLM-based classification.
> Analyze PyTorch NCCL flight-recorder (FR) dumps to identify collective operation hangs and isolate the responsible ranks using CollectiveAnalyzer. Use when a distributed training job hangs due to an NCCL collective timeout and FR dump files are available. Detects the wavefront process group where collectives diverge and returns the root-cause suspect ranks.
Find, evaluate, and download low-level common standard CAD parts from step.parts, such as screws, bolts, nuts, washers, bearings, standoffs, electronics parts, motors, connectors, and other off-the-shelf components. Use when Codex needs to search the hosted step.parts catalog, resolve fuzzy part names, standards, aliases, or dimensions, choose a matching part, fetch a canonical .step file, verify checksums, or use the step.parts API/OpenAPI/catalog endpoints for standard part discovery.
Use when reviewing code for security vulnerabilities, implementing authentication/authorization, handling user input, or discussing web application security. Covers OWASP Top 10:2025, ASVS 5.0, LLM Top 10 (2025), and Agentic AI security (2026).
Host-side setup, configuration, customization, builds, migration, and troubleshooting for the Aerovato Container CLI. Use when working with Aerovato Container, settings.json, Dockerfile.User, build stages, V2-to-V3 migration, mounts, harnesses, tools, permissions, Docker, or Podman. Do not use it to expose host Container configuration inside managed containers.
Audit inbound lead handling and map the customer experience from source event to follow-up outcome.
Resolve the right account owner for a lead using contact, company, and deal ownership evidence.
Maintain a daily review cockpit for high-intent leads and hand-raisers.
Triage support tickets by pulling helpdesk context, searching issue trackers and knowledge bases in parallel, classifying the issue, and giving reps a recommended next action.
Draft customer stories from approved transcripts, notes, briefs, quotes, and proof points. Use for narrative customer stories, campaign stories, and metric-forward customer story pages.
Find approved customer proof for campaigns, pages, decks, emails, and sales conversations. Use when a team needs relevant case studies, quotes, stats, slides, logos, or peer examples.
Keep Zapier Tables or another MCP-connected record store as the source of truth while Google Sheets-based tools read an event-driven cache tab.
Turn a company meeting or event recording into a short, approved, postable clip.
Prioritize inbound client calls by reading transcripts, classifying urgency, matching callers to legal client and matter context, and preparing a callback list for human review.
Help non-designers evaluate work against brand guidelines and know what to fix.
Audit lead handling quality across GTM systems and identify missed or low-quality follow-up.
Install and connect Zapier, routing to the right surface (MCP, SDK, or CLI) for what the person needs. Use when someone says "install Zapier", "add Zapier", "connect Zapier", or "set up Zapier", or when they want an AI client (Claude, ChatGPT, Cursor) to use their apps, want to call Zapier from code, or want Zapier in the terminal.
Find media, partnership, and high-intent requests buried in a shared Gmail inbox, summarize the useful ones, and route them to the right owner through Zapier MCP.
Build sourced campaign retrospectives from goals, launch context, performance data, team feedback, and follow-up actions.
Build self-contained HTML mockups from campaign, product, or page briefs.
Build review-first outbound campaign packages from source context for marketing and sales teams.
Coordinate opportunities and customer records across multiple CRMs while preserving attribution, visibility, commissions, and governance.
Identify named accounts with timely buying or expansion signals and recommend next actions.
Turn account usage data into a concise AI audit outreach package.
Build tailored customer-facing sales or customer success decks from approved account context and generate the presentation with Gamma through Zapier MCP.
Create practical sales sequences from campaign context, account signals, and reference copy.
>- Generate new raster images and looping GIF/WebP animations with the user's ChatGPT subscription through the local one-file chatgpt-imagegen CLI, without an API key or daemon. Use for photos, illustrations, icons, hero banners, mockups, sprites, concept art, animation loops, and figures for documents, proposals, blog posts, or READMEs; save outputs in the workspace. Auto mode prefers the logged-in ChatGPT browser through chrome-use to avoid Codex usage and falls back to the Codex backend only when the web path is unavailable. Proactively propose useful figures while authoring long-form content. Do not use for editing existing images, SVG/vector work, code-native graphics, established icon systems, explicit high-quality or transparent API output, or end-user image-generation services.
Fetch and report CI results for a silk PR. Use when the user asks to investigate, address, or fix CI failures, or refers to a PR without specifying what's broken.
Generate cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell narratives (催泪/亲情/萌宠/离别), or any cinematic video prompt.
Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.
> Provides workflows to write, debug, and optimize prompts for LLMs, including few-shot example selection, chain-of-thought structuring, system prompt design, and template composition. Use when the user asks to write or improve a prompt, wants help with few-shot examples, chain-of-thought, system prompts, prompt templates, or asks how to get better results from an LLM.
Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
Provides AWS CloudFormation patterns for Lambda functions, layers, API Gateway integration, event sources, cold start optimization, monitoring, logging, template validation, and deployment workflows. Use when creating Lambda functions with CloudFormation, configuring event sources, implementing cold start optimization, managing layers, integrating with API Gateway, and deploying Lambda infrastructure.
Provides AWS CloudFormation patterns for EC2 instances, Security Groups, IAM roles, and load balancers. Use when creating EC2 instances, SPOT instances, Security Groups, IAM roles for EC2, Application Load Balancers (ALB), Target Groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
Provides AWS CloudFormation patterns for IAM roles, policies, managed policies, permission boundaries, and trust relationships. Use when modeling least-privilege access, cross-account assumptions, service roles, or reusable IAM stacks that other CloudFormation templates consume.
Provides AWS CloudFormation patterns for ElastiCache Redis or Memcached infrastructure, including subnet groups, parameter groups, security controls, and cross-stack outputs. Use when designing cache tiers, high-availability replication groups, encryption settings, or reusable CloudFormation templates for application caching.
Provides AWS CloudFormation patterns for Amazon S3. Use when creating S3 buckets, policies, versioning, lifecycle rules, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
Provides AWS CloudFormation patterns for Amazon RDS databases. Use when creating RDS instances (MySQL, PostgreSQL, Aurora), DB clusters, multi-AZ deployments, parameter groups, subnet groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.
Claude Skills — questions
Answers built from the skills we actually parsed.