AI Agent Skills Marketplace
Showing 24 of 24 results
token-guard
Stop wasting tokens and money on oversized context. Token-Guard measures what you are about to send, flags the biggest offenders, and tells you what to trim, summarize, or drop before the call. Includes budget thresholds, per-file cost estimates, hard-limit gates, and a report you can act on. Pairs with lean-context and context-offload-hint.
sop-architect
Turn a messy process into an agent-ready SOP. Interviews you about a workflow, then produces a structured spec with roles, inputs, steps, decision points, failure paths, and acceptance checks that an AI agent can execute or automate. Includes templates, a gap analysis, and a validation checklist.
lean-context
Cut token spend in long coding sessions. lean-context detects oversized context before you paste it (logs, JSON, big diffs, long docs) and gives concrete ways to split or offload it, then scopes code review to changed files with line counts and token estimates. Pairs with Token-Guard and context-offload-hint.
SKILL.md Snippet Generator
Generate a minimal, valid SKILL.md from a one-liner: frontmatter plus Triggers, Inputs, Procedure, Report, and Example. Designed to feed straight into skl-validator.
Context Offload Hint
Detect oversized content about to be pasted into a model or agent — logs, JSON, big diffs, long docs — and get concrete ways to split or offload it. Feeds into Token-Guard and lean-context.
YAML Frontmatter Fixer
Fix broken SKILL.md YAML frontmatter — quotes, commas, colons, list syntax, invalid characters — and show a before/after for each fix. Helps your skills pass skl-validator faster.
git changed scope lister
List changed/staged files with line counts and a rough token-size estimate, flagging the biggest diffs first. Helps you scope a code review or trim what you feed an agent. Feeds into lean-context/Token-Guard.
Markdown Link Checker
Sanity-check links in Markdown files — relative paths, anchors, and obviously broken targets — and get a fix list keyed by file and line. Handy for docs, READMEs, and SKILL.md files before you ship.
local-jev
Make Laya trustworthy. Fit its confidence to your own labeled data, keep the decisions it gets right local and free on Apple Silicon, and send only the ones it does not know to hosted Jev (TypeSafe System One) or any OpenAI-compatible LLM. Includes a Jev-compatible local server (laya-mlx), per-type temperature calibration with before/after ECE and accuracy, confidence thresholds for a target accuracy, an escalation-rate and cost report, a fail-open TypeScript decide() client, and a playbook to find LLM calls that are really classifications. Use for ticket routing, yes/no gates, rubric scores, replacing prompt-and-parse LLM classification, cutting Jev or LLM spend, and private offline typed decisions.
Project Sentinel - Multi-Project Development Progress Tracker
ENHE Project Sentinel is a local-first, read-only Skill for tracking multiple development projects. It reads authorized Git repositories, project documents, reports, quality signals, and available Codex usage data to generate bilingual project portfolio reports. Reports include progress, elapsed development time, ETA ranges, risks, quality signals, Codex context, token usage, next actions, and agent-ready prompts. It is designed for indie developers, AI product builders, product owners, and small R&D teams. Supports Windows 10/11. By default, it does not modify project code or upload source code, and it clearly marks insufficient evidence instead of inventing precise ETAs.
mesh flow
What it does Mesh Flow replaces fragile, implicit prompt-chaining with a robust, artifact-driven DAG (Directed Acyclic Graph) orchestration system. By defining your agent workflows in a structured project.yaml, you move logic out of the prompt and into a compile-time validated system. It enforces hard gates—such as human approval or dependency verification—that the AI cannot bypass or hallucinate past. Why use this skill Standard agentic workflows often fail because the LLM decides to skip steps or "forgets" requirements. Mesh Flow treats your agentic workflow like a CI/CD pipeline. It features a Compile-then-Run architecture that validates your topology for cycles and missing artifacts before single token is generated. This ensures 100% predictable execution paths, explicit failure states (failed, blocked, rejected), and absolute control over recovery paths. Supported tools YAML-based workflow configurations Standardized Adapter Interfaces for cross-skill communication Mermaid DAG visualization for debugging Zod-backed schema validation CLI tools for compilation and execution (mesh compile, mesh run) Output structure The skill produces a normalized execution plan and a detailed execution trace. Every node execution returns a standardized status, a list of produced artifacts, and comprehensive metadata including tool calls and verification reasoning. Use Cases Build multi-step agent pipelines with hard verification gates Enforce human-in-the-loop approval before sensitive code deployments Visualize complex agent task dependencies using Mermaid DAGs Standardize artifact sharing between disparate AI skills and agents
harness-engineering
The Harness Engineering skill implements a structured methodology for agent orchestration. It allows you to build sophisticated control loops using a multi-role architecture: Planner: Defines contracts and stop rules. Executor: Performs bounded actions. Verifier: Validates results against evidence. Critic/Recovery: Identifies regressions and manages error state.
prompt-engineer-pro
Prompt Engineer Pro is a production-grade system for teams shipping AI workflows into business-critical environments. Unlike basic prompting assistants, this skill treats prompts as software assets that require governance, audit trails, and regression testing. It helps you build, audit, and harden prompt stacks for complex tasks involving multiple models, tool-use agents, and structured data extraction.
SEO & AEO Auditor
Deep SEO + AEO auditor that transforms any codebase into a search and AI visibility powerhouse. Runs 102 checks across 8 categories: technical SEO, content SEO, structured data, AEO readiness, AI crawler strategy, llms.txt, performance (Lighthouse + PageSpeed Insights), and content gap analysis. Generates schema markup, llms.txt, robots.txt AI directives, and FAQ patterns. Scores your site 0-100, then fixes what you approve. Built for developers using Claude Code.
Git Commit Assistant
Writes structured, conventional commit messages from git diff output. Supports Conventional Commits, semantic versioning, and project-specific commit styles.
Code Review Agent
Comprehensive code review with security, performance, and style analysis for Python, TypeScript, and Go. Detects OWASP Top 10 vulnerabilities, N+1 queries, race conditions, and logic errors before they reach production.
prompt-engineer-lite
Building high-performance LLM applications requires more than just basic instructions. This skill equips your AI agent with a sophisticated framework for designing, debugging, and optimizing prompts across any major model provider. It solves the common problems of model drift, parsing failures, and hallucination by implementing industry-standard engineering patterns.
PR Description Generator
Generates comprehensive, structured PR descriptions from git diff or branch comparison. Includes summary, changelog, testing notes, and deployment considerations.
SKILL.md Validator / Linter
A free, instruction-only SKILL.md validator and linter for agent skills. Checks structure, frontmatter, required sections, and common pitfalls, then returns a clean PASS / REVISE / FAIL verdict with fixes.
flutter-add-integration-test
# Implementing Flutter Integration Tests ## Contents - [Project Setup and Dependencies](#project-setup-and-dependencies) - [Interactive Exploration via MCP](#interactive-exploration-via-mcp) - [Test Authoring Guidelines](#test-authoring-guidelines) - [Execution and Profiling](#execution-and-profiling) - [Workflow: End-to-End Integration Testing](#workflow-end-to-end-integration-testing) - [Examples](#examples) ## Project Setup and Dependencies Configure the project to support integration testing and Flutter
code-review-excellence
# Code Review Excellence Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement. ## When to Use This Skill - Reviewing pull requests and code changes - Establishing code review standards for teams - Mentoring junior developers through reviews - Conducting architecture reviews - Creating review checklists and guidelines - Improving team collaboration - Reducing code review cycle time - Maintaining code quality standards #
expo-data-fetching
# Expo Networking **You MUST use this skill for ANY networking work including API requests, data fetching, caching, or network debugging.** ## References Consult these resources as needed: ``` references/ expo-router-loaders.md Route-level data loading with Expo Router loaders (web, SDK 55+) offline-and-cancellation.md NetInfo network status, offline-first React Query, AbortController ``` ## When to Use Use this skill when: - Implementing API requests - Setting up data fetching (React Query, SWR) - Using Ex
data-quality-frameworks
# Data Quality Frameworks Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines. ## When to Use This Skill - Implementing data quality checks in pipelines - Setting up Great Expectations validation - Building comprehensive dbt test suites - Establishing data contracts between teams - Monitoring data quality metrics - Automating data validation in CI/CD ## Core Concepts ### 1. Data Quality Dimensions | Dimension | Descriptio
scoutqa-test
# ScoutQA Testing Skill Perform AI-powered exploratory testing on web applications using the `scoutqa` CLI. **Think of ScoutQA as an intelligent testing partner** that can autonomously explore, discover issues, and verify features. Delegate testing to multiple parallel ScoutQA executions to maximize coverage while saving time. ## When to Use This Skill Use this skill in two scenarios: 1. **User requests testing** - When the user explicitly asks to test a website or verify functionality 2. **Proactive verifi