skill▌

Comprehensive guide for creating effective Agent Skills for Apollo GraphQL and GraphQL development. \n \n Provides complete skill structure requirements including mandatory SKILL.md frontmatter, naming conventions, and optional reference file organization \n Covers best practices for writing skill descriptions with numbered trigger conditions, body content with progressive disclosure, and reference file management \n Includes Apollo Voice style guidelines emphasizing approachable, opinionated, a

Learn from practitioners, not theorists—build skills based on real-world case studies and failure patterns. \n \n Focuses on finding practitioners who excel in their domain, then extracting their concrete methods through case studies, failures, and before/after comparisons rather than abstract theory \n Workflow: narrow the task, collect golden cases (what top performers do) and failure cases (common mistakes), identify patterns through contrast, then use theory only to explain why \n Emphasizes

IRON LAW: Every line in a skill must justify its token cost. If it doesn't make the model's output better, more consistent, or more reliable — cut it.

Comprehensive guide for building modular skills that extend Claude's capabilities with specialized knowledge and workflows. \n \n Skills are self-contained packages combining procedural instructions, tool integrations, domain expertise, and bundled resources (scripts, references, assets) to transform Claude into a specialized agent \n Follows progressive disclosure design: metadata always available, SKILL.md loaded on trigger, bundled resources loaded as needed to minimize context bloat \n Core

Guide for creating well-structured Agent Skills for Claude Code with best practices and validation. \n \n Covers full Skill lifecycle: scoping, file structure, YAML frontmatter validation, content organization, and testing procedures \n Enforces strict naming rules (lowercase, hyphens, max 64 chars) and description requirements (specific triggers, file types, \"what\" and \"when\" clauses) \n Provides templates for common patterns including read-only Skills, script-based Skills, and multi-file S

Verification skill demonstrating the skill system's basic functionality and loading mechanism. \n \n Executes when /day1-test-skill is invoked, confirming the skill system is operational \n Outputs a success message and explanation of how skills are loaded on-demand into context \n Designed as an onboarding exercise to help users understand Progressive Disclosure and skill mechanics \n

Guidelines and validation for writing Agent Skills compliant with agentskills.io specification. \n \n Covers skill structure (SKILL.md, references/, scripts/), frontmatter constraints (name format, description limits), and token budgets (SKILL.md <5000 tokens, references <1000 each) \n Enforces metadata best practices: use WHEN: trigger phrases in descriptions, avoid DO NOT USE FOR: keywords, keep descriptions under 60 words \n Implements progressive disclosure with just-in-time refere

Automated security scanning for agent skills before installation, detecting prompt injection, malicious code, excessive permissions, and supply chain risks. \n \n Runs static analysis via bundled Python scanner that outputs structured JSON findings with severity levels and URLs \n Validates SKILL.md frontmatter (required fields, tool justification, model overrides) and checks for config poisoning or scope creep in instructions \n Analyzes scripts for data exfiltration, reverse shells, credential

Create, test, and iteratively improve AI agent skills with structured evaluation and benchmarking. \n \n Guides you through the full skill development lifecycle: intent capture, draft writing, test case creation, evaluation, and iteration based on user feedback \n Runs quantitative benchmarks comparing skill-enabled vs. baseline performance, with timing and token usage analysis across multiple test cases \n Generates an interactive browser-based review tool for qualitative evaluation — side-by-s

21 production-ready scripts for iOS app testing, building, and automation via accessibility-driven navigation. \n \n Semantic element interaction using accessibility trees instead of pixel coordinates, reducing token cost from 1,600–6,300 per screenshot to 10–50 per query \n Covers five domains: build and testing, navigation and gestures, accessibility auditing, permissions and notifications, and simulator lifecycle management \n All scripts support --json for machine-readable output and auto-de