296건 · 관련도순
This skill provides guidance for implementing Go Embedded SPA architecture - embedding React/Vue/TSX frontend static resources into Go binary using go:embed directive. Use this skill when building self-contained single-binary applications, implementing SPA with Go backend, setting up cross-platform deployable full-stack projects, or configuring static file serving with Go standard library net/http.
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Guide for contributing new skills to the skills-x collection. This skill should be used when users want to add new open-source skills from external sources (like agentskills.io or anthropics/skills) to the skills-x repository. It covers the complete workflow from discovery to publishing.
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Go testing patterns including table-driven tests, subtests, benchmarks, fuzzing, and test coverage. Follows TDD methodology with idiomatic Go practices.
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Use when you have a spec or requirements for a multi-step task, before touching code
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Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
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Building production-quality CLI tools with Cobra command framework and Viper configuration management
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Use when completing tasks, implementing major features, or before merging to verify work meets requirements
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Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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Use when implementing any feature or bugfix, before writing implementation code
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Use when executing implementation plans with independent tasks in the current session
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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
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Use when creating new skills, editing existing skills, or verifying skills work before deployment
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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
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Use when you have a written implementation plan to execute in a separate session with review checkpoints
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Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
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Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.
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Framework-agnostic agent-to-agent payment system with SHIB on Polygon. Provides trustless escrow, price negotiation, and reputation system. 9,416x cheaper than traditional escrow (~$0.003 gas).
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ERC-8004 Trustless Agents - Register and manage AI agent identities on TRON and BSC blockchains with on-chain reputation tracking
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Deterministic AI development workflow orchestrator. Manages task boards (.jonggrang/jonggrang-tasks.json), runs 16-phase pipelines (BUGFIX/SMALL/MEDIUM/LARGE), installs lifecycle hooks for Claude Code and OpenCode, and coordinates five specialist roles (Lead/Developer/Reviewer/TestLead/Tester). Use when planning features, executing multi-step development workflows, or orchestrating AI coding agents. Run via npx jonggrang.
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16-phase feature orchestration workflow. Runs in the main thread as Kernel Mode. Coordinates the Lead→Developer→Reviewer→TestLead→Tester assembly line.
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Prove — or honestly fail to prove — a specific technical claim: "the fix works", "it's deployed", "the service is running", "the site is done", "there are no vulnerabilities". Atomizes the claim, picks the shortest decisive signal on the evidence ladder, and returns a verdict from a fixed vocabulary (pass / partial / fail / skip) with an explicit coverage boundary. NOT code review, NOT a fix, NOT orchestration — only "what is proven, and what we merely hope". Triggers — "prove it works", "is it really done?", "are you sure?", "we deployed — is it live?", "verify that", "did we actually check?".
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Preserve output from expensive one-shot commands for monitoring and later analysis. Use when rerunning a build, API query, CI inspection, or diagnostic would be costly and a managed background process is not the better interface.
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