Claude Skills

aka. Agent Skills

Discover skills for AI coding agents. Works with Claude Code, OpenAI Codex, Gemini CLI, Cursor, and more.

Claude Code
OpenAI Codex
Gemini CLI
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9409 verified skills
#1921

project-brainstorming

verified

Socratic questioning and ideation methodology for project conception using structured brainstorming frameworks

athola/claude-night-market
158
#1922

skills-eval

verified

Evaluate and improve Claude skill quality through auditing. Triggers: quality-assurance, skills, optimization, tool-use, performance-metrics, skill audit, quality review, compliance check, improvement suggestions, token usage analysis, skill evaluation, skill assessment, skill optimization, skill standards, skill metrics, skill performance. Use when reviewing skill quality, preparing skills for production, or auditing existing skills. Do not use when creating new skills (use modular-skills) or writing prose (use writing-clearly-and-concisely). Use this skill before shipping any skill to production.

athola/claude-night-market
158
#1923

optimizing-large-skills

verified

Systematic methodology to reduce skill file size through externalization, consolidation, and progressive loading patterns. Triggers: large skill, skill optimization, skill size, 300 lines, inline code, skill refactoring, skill context reduction, skill modularization Use when: skills exceed 300 lines, multiple code blocks (10+) with similar functionality, heavy Python inline with markdown, functions >20 lines embedded DO NOT use when: skill is under 300 lines and well-organized. DO NOT use when: creating new skills - use modular-skills instead. Consult this skill when skills-eval shows "Large skill file" warnings.

athola/claude-night-market
158
#1924

project-execution

verified

Systematic task execution with checkpoint validation, progress tracking, and quality gates

athola/claude-night-market
158
#1925

qwen-delegation

verified

Qwen CLI delegation workflow implementing delegation-core for Alibaba's Qwen models.

athola/claude-night-market
158
#1926

makefile-generation

verified

Generate language-specific Makefile with common development targets

athola/claude-night-market
158
#1927

precommit-setup

verified

Configure comprehensive three-layer pre-commit quality system with linting, type checking, and testing enforcement

athola/claude-night-market
158
#1928

project-specification

verified

Transform project brief into detailed, testable specifications using spec-driven development methodology

athola/claude-night-market
158
#1929

gemini-delegation

verified

Gemini CLI delegation workflow implementing delegation-core for Google's Gemini models.

athola/claude-night-market
158
#1930

war-room

verified

Multi-LLM deliberation framework for strategic decisions through pressure-based expert consultation

athola/claude-night-market
158
#1931

subagent-testing

verified

TDD-style testing methodology for skills using fresh subagent instances to prevent priming bias and validate skill effectiveness. Triggers: test skill, validate skill, skill testing, subagent testing, fresh instance testing, TDD for skills, skill validation Use when: validating skill improvements, testing skill effectiveness, preventing priming bias, measuring skill impact on behavior DO NOT use when: implementing skills (use skill-authoring instead), creating hooks (use hook-authoring instead)

athola/claude-night-market
158
#1932

delegation-core

verified

Delegate tasks to external LLM services (Gemini, Qwen) with quota, logging, and error handling.

athola/claude-night-market
158
#1933

shared

verified

Shared utilities and constants for scribe plugin skills. This module provides common patterns, word lists, and utilities used across slop-detector, style-learner, and doc-generator.

athola/claude-night-market
158
#1934

style-learner

verified

Triggers: writing style, voice, tone, style guide, exemplar, style learning Learn and extract writing style patterns from exemplar text for consistent application. Triggers: learn style, extract style, style profile, writing voice, tone analysis, style guide generation, exemplar analysis Use when: creating a style guide from existing content, ensuring consistency across documents, learning a specific author's voice, customizing AI output style DO NOT use when: detecting AI slop - use slop-detector instead. DO NOT use when: just need to clean up existing content - use doc-generator with --remediate. Use this skill to build style profiles from exemplar text.

athola/claude-night-market
158
#1935

doc-generator

verified

Generate or remediate documentation with human-quality writing and style adherence. Triggers: documentation, generate docs, write docs, technical writing, generate documentation, write readme, create guide, doc generation, remediate docs, polish content, clean up docs. Use when creating new documentation, rewriting AI-generated content, or applying style profiles. Do not use for slop detection only (use slop-detector) or learning styles (use style-learner).

athola/claude-night-market
158
#1936

shell-review

verified

Audit shell scripts for correctness, portability, and common pitfalls. Triggers: shell script, bash, sh, script review, pipeline, exit code Use when: reviewing shell scripts, CI scripts, hook scripts, wrapper scripts DO NOT use when: creating new scripts - use attune:workflow-setup

athola/claude-night-market
158
#1937

session-management

verified

Triggers: session, resume, rename, checkpoint Manage Claude Code sessions with naming, checkpointing, and resume strategies. Use when: organizing long-running work, creating debug checkpoints, managing PR reviews

athola/claude-night-market
158
#1938

aqe-v2-v3-migration

verified

Migrate Agentic QE projects from v2 to v3 with zero data loss

proffesor-for-testing/agentic-qe
157
#1939

svelte-code-writer

verified

CLI tools for Svelte 5 documentation lookup and code analysis. MUST be used whenever creating or editing any Svelte component (.svelte) or Svelte module (.svelte.ts/.svelte.js). If possible, this skill should be executed within the svelte-file-editor agent for optimal results.

sveltejs/mcp
155
#1940

testing-strategies

verified

Strategic guidance for choosing and implementing testing approaches across the test pyramid. Use when building comprehensive test suites that balance unit, integration, E2E, and contract testing for optimal speed and confidence. Covers multi-language patterns (TypeScript, Python, Go, Rust) and modern best practices including property-based testing, test data management, and CI/CD integration.

ancoleman/ai-design-components
152
#1941

security-hardening

verified

Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.

ancoleman/ai-design-components
152
#1942

using-vector-databases

verified

Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.

ancoleman/ai-design-components
152
#1943

displaying-timelines

verified

Displays chronological events and activity through timelines, activity feeds, Gantt charts, and calendar interfaces. Use when showing historical events, project schedules, social feeds, notifications, audit logs, or time-based data. Provides implementation patterns for vertical/horizontal timelines, interactive visualizations, real-time updates, and responsive designs with accessibility (WCAG/ARIA).

ancoleman/ai-design-components
152
#1944

writing-infrastructure-code

verified

Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.

ancoleman/ai-design-components
152
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