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qa-api-testing-contracts

API contract testing across REST, GraphQL, and gRPC. Use when you need schema validation, breaking-change detection, and CI quality gates.

vasilyu1983
vasilyu1983
4911

qa-debugging

Systematic debugging for crashes, regressions, flakes, and production bugs. Use when diagnosing stack traces, logs, traces, or profiling data.

vasilyu1983
vasilyu1983
4911

ai-ml-timeseries

Time series forecasting — LightGBM, Transformers, temporal validation, feature engineering, and production deployment. Use when building TS models.

vasilyu1983
vasilyu1983
4911

qa-docs-coverage

Audit and enforce doc quality. Use when checking coverage, freshness, runbook validity, or cleaning stale/duplicate markdown after LLM edits.

vasilyu1983
vasilyu1983
4911

software-ux-research

Covers user research methods and research ops. Use when running interviews, usability tests, surveys, or A/B tests to de-risk product decisions.

vasilyu1983
vasilyu1983
4911

ai-rag

RAG and search engineering — chunking, hybrid retrieval, reranking, and nDCG evaluation. Use when building retrieval-augmented generation pipelines.

vasilyu1983
vasilyu1983
4911

mini-wiki

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trsoliu
trsoliu
491

payload

Use when working with Payload projects (payload.config.ts, collections, fields, hooks, access control, Payload API). Use when debugging validation errors, security issues, relationship queries, transactions, or hook behavior.

payloadcms
payloadcms
491

quiz-generator

This skill generates interactive multiple-choice quizzes for each chapter of an intelligent textbook, with questions aligned to specific concepts from the learning graph and distributed across Bloom's Taxonomy cognitive levels to assess student understanding effectively. Use this skill after chapter content has been written and the learning graph exists.

dmccreary
dmccreary
494

story-generator

This skill generates graphic novel narratives about physicists and scientists for intelligent textbooks. It creates compelling, historically-accurate stories with image prompts designed for high school students. Use this skill when the user wants to add a new scientist story to the Physics History Graphic Novels section of an MkDocs Material textbook, or when creating educational graphic novel content about historical scientists.

dmccreary
dmccreary
494

reference-generator

This skill generates curated, high-quality reference lists for textbooks with 10 references per chapter. References prioritize Wikipedia for reliability, include detailed relevance descriptions, and are stored in separate references.md files for token efficiency. Use this skill when working with intelligent textbooks that need academic references.

dmccreary
dmccreary
494

book-installer

Installs and configures project infrastructure including MkDocs Material intelligent textbook templates, learning graph viewers, and skill tracking systems. Routes to the appropriate installation guide based on what the user needs to set up.

dmccreary
dmccreary
494

chapter-content-generator

This skill generates comprehensive chapter content for intelligent textbooks after the book-chapter-generator skill has created the chapter structure. Use this skill when a chapter index.md file exists with title, summary, and concept list, and detailed educational content needs to be generated at the appropriate reading level with rich non-text elements including diagrams, infographics, and MicroSims. (project, gitignored)

dmccreary
dmccreary
494

readme-generator

This skill creates or updates a README.md file in the GitHub home directory of the current project. The README.md file it generates will conform to GitHub best practices, including badges, project overview, site metrics, getting started instructions, and comprehensive documentation.

dmccreary
dmccreary
494

course-description-analyzer

This skill analyzes or creates course descriptions for intelligent textbooks by checking for completeness of required elements (title, audience, prerequisites, topics, Bloom's Taxonomy outcomes) and providing quality scores with improvement suggestions. Use this skill when working with course descriptions in /docs/course-description.md that need validation or creation for learning graph generation.

dmccreary
dmccreary
494

diagram-reports-generator

This skill generates comprehensive diagram and MicroSim reports for the geometry course by analyzing chapter markdown files and creating table and detail reports. Use this skill when working with an intelligent textbook (specifically geometry-course) that needs updated visualization of all diagrams and MicroSims across chapters, including their status, difficulty, Bloom's Taxonomy levels, and UI complexity.

dmccreary
dmccreary
494

glossary-generator

This skill automatically generates a comprehensive glossary of terms from a learning graph's concept list, ensuring each definition follows ISO 11179 metadata registry standards (precise, concise, distinct, non-circular, and free of business rules). Use this skill when creating a glossary for an intelligent textbook after the learning graph concept list has been finalized.

dmccreary
dmccreary
494

linkedin-announcement-generator

This skill generates professional LinkedIn announcement text for intelligent textbooks by analyzing book metrics, chapter content, and learning resources to create engaging posts with key statistics, hashtags, and links to the published site. Use this skill when you need to create social media announcements about textbook completion or major milestones.

dmccreary
dmccreary
494

moving-rainbow

Generate MicroPython programs for the Moving Rainbow LED strip educational project using Raspberry Pi Pico with NeoPixel strips and button controls.

dmccreary
dmccreary
494

microsim-generator

Creates interactive educational MicroSims using the best-matched JavaScript library (p5.js, Chart.js, Plotly, Mermaid, vis-network, vis-timeline, Leaflet, Venn.js). Analyzes user requirements to route to the appropriate visualization type and generates complete MicroSim packages with HTML, JavaScript, CSS, documentation, screen capture, and metadata.

dmccreary
dmccreary
494

book-metrics-generator

This skill generates comprehensive metrics reports for intelligent textbooks built with MkDocs Material, analyzing chapters, concepts, glossary terms, FAQs, quiz questions, diagrams, equations, MicroSims, word counts, and links. Use this skill when working with an intelligent textbook project that needs quantitative analysis of its content, typically after significant content development or for project status reporting. The skill creates two markdown files - book-metrics.md with overall statistics and chapter-metrics.md with per-chapter breakdowns - in the docs/learning-graph/ directory.

dmccreary
dmccreary
494

book-chapter-generator

This skill generates a structured chapter outline for intelligent textbooks by analyzing course descriptions, learning graphs, and concept dependencies. Use this skill after the learning graph has been created and before generating chapter content, to design an optimal chapter structure that respects concept dependencies and distributes content evenly across all of the chapter in a book.

dmccreary
dmccreary
494

microsim-utils

Utility tools for MicroSim management including quality validation, screenshot capture, icon management, and index page generation. Routes to the appropriate utility based on the task needed.

dmccreary
dmccreary
494

faq-generator

This skill generates a comprehensive set of Frequently Asked Questions (FAQs) from the course description, course content, learning graphs, concept lists, MicroSims, and glossary terms to help students understand common questions and prepare content for chatbot integration. Use this skill after course description, learning graph, glossary, and at least 30% of chapter content exist.

dmccreary
dmccreary
494

learning-graph-generator

Generates a comprehensive learning graph from a course description, including 200 concepts with dependencies, taxonomy categorization, and quality validation reports. Use this when the user wants to create a structured knowledge graph for educational content.

dmccreary
dmccreary
494

Linear

Managing Linear issues, projects, and teams. Use when working with Linear tasks, creating issues, updating status, querying projects, or managing team workflows.

wrsmith108
wrsmith108
494

zimage-skill

Generate images using ModelScope Z-Image-Turbo API. Use when user asks to generate, create, or make images, pictures, or illustrations.

yizhiyanhua-ai
yizhiyanhua-ai
482

domain-assessment

Domain reconnaissance coordinator that orchestrates subdomain discovery and port scanning to build comprehensive domain attack surface inventory

transilienceai
transilienceai
488

common-appsec-patterns

Application security testing coordinator for common vulnerability patterns including XSS, injection flaws, and client-side security issues. Orchestrates specialized testing agents to identify and validate common application security weaknesses.

transilienceai
transilienceai
488

authenticating

Authentication testing skill - automates signup, login, 2FA bypass, CAPTCHA solving, and bot detection evasion using Playwright MCP. Tests authentication security controls. Includes behavioral biometrics simulation, OTP handling, and automated account creation for security assessments.

transilienceai
transilienceai
488

ai-threat-testing

Offensive AI security testing and exploitation framework. Systematically tests LLM applications for OWASP Top 10 vulnerabilities including prompt injection, model extraction, data poisoning, and supply chain attacks. Integrates with pentest workflows to discover and exploit AI-specific threats.

transilienceai
transilienceai
488

pentest

Penetration testing orchestrator that coordinates specialized attack agents. Provides attack indexes, methodology frameworks, and documentation. Execution delegated to specialized agents (SQL Injection, XSS, SSRF, etc.). Use for engagement planning and attack coordination.

transilienceai
transilienceai
488

web-application-mapping

Comprehensive web application reconnaissance and mapping coordinator that orchestrates passive browsing, active endpoint discovery, attack surface analysis, and headless browser automation for complete application coverage.

transilienceai
transilienceai
488

hackerone

HackerOne bug bounty automation - parses scope CSVs, deploys parallel pentesting agents for each asset, validates PoCs, and generates platform-ready submission reports. Use when testing HackerOne programs or preparing professional vulnerability submissions.

transilienceai
transilienceai
488

cve-testing

CVE vulnerability testing coordinator that identifies technology stacks, researches known vulnerabilities, and tests applications for exploitable CVEs using public exploits and proof-of-concept code.

transilienceai
transilienceai
488

ip-attribution

Maps IP addresses to cloud providers, ASNs, and organizations via WHOIS

transilienceai
transilienceai
488

javascript-dom-analysis

Detects frontend frameworks via global variables, DOM attributes, and bundle patterns

transilienceai
transilienceai
488

job-posting-analysis

Extracts technology requirements from job postings and career pages

transilienceai
transilienceai
488

api-portal-discovery

Discovers public API portals, developer docs, and OpenAPI/Swagger endpoints

transilienceai
transilienceai
488

backend-inferencer

Infers backend technologies including servers, languages, frameworks, databases, and CMS

transilienceai
transilienceai
488

cdn-waf-fingerprinter

Identifies CDNs (Cloudflare, Akamai, Fastly) and WAFs

transilienceai
transilienceai
488

certificate-transparency

Queries CT logs for certificates and extracts SANs for subdomain discovery

transilienceai
transilienceai
488

cloud-infra-detector

Detects cloud providers (AWS, Azure, GCP) and PaaS platforms

transilienceai
transilienceai
488

code-repository-intel

Scans GitHub/GitLab for public repos, dependencies, and CI configurations

transilienceai
transilienceai
488

devops-detector

Detects CI/CD tools, containerization, and orchestration from public signals

transilienceai
transilienceai
488

dns-intelligence

Extracts technology signals from DNS records (MX, TXT, NS, CNAME, SRV)

transilienceai
transilienceai
488

domain-discovery

Discovers official company domain via web search, WHOIS, and common TLD patterns

transilienceai
transilienceai
488

frontend-inferencer

Infers frontend technologies including React, Angular, Vue, jQuery, Bootstrap, etc.

transilienceai
transilienceai
488

tls-certificate-analysis

Analyzes TLS certificates for issuer, SAN, and JARM fingerprints

transilienceai
transilienceai
488

web-archive-analysis

Uses Wayback Machine to detect technology migrations over time

transilienceai
transilienceai
488

Page 586 of 1486 · 74265 results

Adoption

Agent Skills are supported by leading AI development tools.

FAQ

Frequently asked questions about Agent Skills.

01

What are Agent Skills?

Agent Skills are reusable, production-ready capability packs for AI agents. Each skill lives in its own folder and is described by a SKILL.md file with metadata and instructions.

02

What does this agent-skills.md site do?

Agent Skills is a curated directory that indexes skill repositories and lets you browse, preview, and download skills in a consistent format.

03

Where are skills stored in a repo?

By default, the site scans the skills/ folder. You can also submit a URL that points directly to a specific skills folder.

04

What is required inside SKILL.md?

SKILL.md must include YAML frontmatter with at least name and description. The body contains the actual guidance and steps for the agent.

05

How can I submit a repo?

Click Submit in the header and paste a GitHub URL that points to a skills folder. We’ll parse it and add any valid skills to the directory.