Write effective LLM prompts, commands, and agent instructions. Goal-oriented over step-prescriptive. Role + Objective + Latitude pattern. Use when writing prompts, designing agents, building Claude Code commands, or reviewing LLM instructions. Keywords: prompt engineering, agent design, command writing.

Orchestrate multiple LLMs as a council, generating collective intelligence through peer review and chairman synthesis

Optimize documentation for AI coding assistants and LLMs. Improves docs for Claude, Copilot, and other AI tools through c7score optimization, llms.txt generation, question-driven restructuring, and automated quality scoring. Use when asked to improve, optimize, or enhance documentation for AI assistants, LLMs, c7score, Context7, or when creating llms.txt files. Also use for documentation quality analysis, README optimization, or ensuring docs follow best practices for LLM retrieval systems.

Implementing function calling (tool use) with LLMs for structured outputs and external integrations.

Finding and accessing AI/LLM model brand icons from lobe-icons library. Use when users need icon URLs, want to download brand logos for AI models/providers/applications (Claude, GPT, Gemini, etc.), or request icons in SVG/PNG/WEBP formats.

Guidance for implementing batching schedulers for LLM inference systems with compilation-based accelerators. This skill applies when optimizing request batching to minimize cost while meeting latency thresholds, particularly when dealing with shape compilation costs, padding overhead, and multi-bucket request distributions. Use this skill for tasks involving batch planning, shape selection, generation-length bucketing, and cost-model-driven optimization for neural network inference.

Use when integrating LLM APIs into applications. Covers API patterns, prompt templates, streaming, error handling, cost optimization, and provider abstraction. Apply when building chat interfaces, completion endpoints, or AI-powered features.

Advanced LLM jailbreaking techniques, safety mechanism bypass strategies, and constraint circumvention methods

Optimize prompts for better LLM outputs through systematic analysis and refinement

Comprehensive system for managing llms.txt and my-blog.json files across the project. Handles creation, updating, and synchronization of AI-readable content catalogs. Automatically detects when working with llms.txt files, my-blog.json files, or updating blog content. Includes utilities for fetching latest content from query-index.json and organizing by last modified dates. Keywords include llms.txt, my-blog.json, blog catalog, content index, query-index.json, AI content discovery, update llms, update blog, content synchronization.

LMS feature implementations including Browse Gradebook, Course Kanban, Communications, Statistics Module, MAP Analytics, Teacher Schedule, and Attendance System. Use this skill when implementing or modifying these features, understanding their data structures, or debugging feature-specific issues.

Research standards/patterns via MCP Ref. Generates Standards Research for Story Technical Notes subsection. Reusable worker.

Top orchestrator for complete doc system. Delegates to ln-110 coordinator (project docs via 5 L3 workers) + ln-120-150 workers. Phase 4: global cleanup. Idempotent.

Creates test documentation (testing-strategy.md + tests/README.md). Establishes testing philosophy and Story-Level Test Task Pattern. L2 Worker in ln-100-documents-pipeline workflow.

Builds interactive HTML presentation with 6 tabs (Overview, Requirements, Architecture/C4, Tech Spec, Roadmap, Guides). Creates presentation/README.md hub. L2 Worker under ln-100-documents-pipeline.

Validates Stories/Tasks with GO/NO-GO verdict, Readiness Score (1-10), Penalty Points, and Anti-Hallucination verification. Auto-fixes to reach 0 points, delegates to ln-002 for docs. Use when reviewing Stories before execution or when user requests validation.

L3 Worker. Reviews task implementation for quality, code standards, test coverage. Creates [BUG] tasks for side-effect issues found outside task scope. Sets task Done or To Rework. Usually invoked by ln-400 with isolated context, can also review a specific task on user request.

Fixes tasks in To Rework and returns them to To Review. Applies reviewer feedback only for the selected task.

Runs a single Story final test task (label "tests") through implementation/execution to To Review.

Story-level quality orchestrator with 4-level Gate (PASS/CONCERNS/FAIL/WAIVED) and Quality Score. Pass 1: code quality -> regression -> manual testing. Pass 2: verify tests/coverage -> calculate NFR scores -> mark Story Done. Use when user requests quality gate for Story or when ln-400 delegates quality check.

Worker that checks DRY/KISS/YAGNI/architecture compliance with quantitative Code Quality Score. Validates architectural decisions via MCP Ref: (1) Optimality - is chosen approach the best? (2) Compliance - does it follow best practices? (3) Performance - algorithms, configs, bottlenecks. Reports issues with SEC-, PERF-, MNT-, ARCH-, BP-, OPT- prefixes.

Orchestrates test planning pipeline (research → manual → auto tests). Coordinates ln-511, ln-512, ln-513. Invoked by ln-500-story-quality-gate.

Performs manual testing of Story AC via executable bash scripts saved to tests/manual/. Creates reusable test suites per Story. Worker for ln-510.

Plans automated tests (E2E/Integration/Unit) using Risk-Based Testing after manual testing. Calculates priorities, delegates to ln-301-task-creator. Worker for ln-510.

Audit project documentation quality across 8 categories (Hierarchy, SSOT, Compactness, Requirements, Actuality, Legacy, Stack Adaptation, Semantic Content). Delegates to ln-601 for deep semantic verification of project documents. Use when documentation needs quality review, after major doc updates, or as part of ln-100-documents-pipeline. Outputs Compliance Score X/10 per category + Findings + Recommended Actions.

Semantic content auditor (L3 Worker). Verifies document content matches stated SCOPE, aligns with project goals, and reflects actual codebase state. Called by ln-600 for each project document. Returns scope_alignment and fact_accuracy scores with findings.

Audit code comments and docstrings quality across 6 categories (WHY-not-WHAT, Density, Forbidden Content, Docstrings, Actuality, Legacy). Use when code needs comment review, after major refactoring, or as part of ln-100-documents-pipeline. Outputs Compliance Score X/10 per category + Findings + Recommended Actions.

Coordinates 9 specialized audit workers (security, build, architecture, code quality, dependencies, dead code, observability, concurrency, lifecycle). Researches best practices, delegates parallel audits, aggregates results into single Linear task in Epic 0.

Architecture audit worker (L3). Checks DRY (7 types), KISS/YAGNI, layer breaks, error handling, DI patterns. Returns findings with severity, location, effort, recommendations.

Code principles audit worker (L3). Checks DRY (7 types), KISS/YAGNI, TODOs, error handling, DI patterns. Returns findings with severity, location, effort, recommendations.

Code quality audit worker (L3). Checks cyclomatic complexity, deep nesting, long methods, god classes, O(n²) algorithms, N+1 queries, magic numbers, decentralized constants, duplicate constants. Returns findings with severity, location, effort, recommendations.

Dependencies and reuse audit worker (L3). Checks outdated packages, unused dependencies, reinvented wheels, custom implementations of standard library features. Returns findings with severity, location, effort, recommendations.

Observability audit worker (L3). Checks structured logging, health check endpoints, metrics collection, request tracing, log levels. Returns findings with severity, location, effort, recommendations.

Risk-Based Value audit worker (L3). Calculates Usefulness Score = Impact (1-5) × Probability (1-5) for each test. Returns KEEP/REVIEW/REMOVE decisions based on thresholds (≥15 KEEP, 10-14 REVIEW, <10 REMOVE).

Test Isolation + Anti-Patterns audit worker (L3). Checks isolation (APIs/DB/FS/Time/Random/Network), determinism (flaky, order-dependent), and 6 anti-patterns.

L3 Worker. Analyzes single pattern implementation, calculates 4 scores (compliance, completeness, quality, implementation), identifies gaps and issues. Usually invoked by ln-640, can also analyze a specific pattern on user request.

L3 Worker. Audits architectural layer boundaries, detects violations (code in wrong layers), checks pattern coverage. Invoked by ln-640 once per audit.

Coordinates dependency upgrades across all detected package managers

Coordinates project structure migration to Clean Architecture

Coordinates Docker, CI/CD, and environment configuration setup via auto-detection

Coordinates linters, pre-commit hooks, and test infrastructure setup

Creates test infrastructure with Vitest, xUnit, and pytest

Configures structured logging (Serilog/.NET, structlog/Python)

Executes all test suites and reports results with coverage

Run and interact with lnd Lightning Network daemon in Docker. Use for Lightning development, testing payment channels on regtest, managing lnd containers, and calling lnd RPC endpoints (getinfo, connect, open/close channels, pay/receive). Supports bitcoind, btcd, and neutrino backends.

load-context

Validate system capacity with realistic load tests.

Performance and load testing for API endpoints, payment pipeline, and concurrent user scenarios

| Atributo | Valor |

Create and manage K6 load tests for REST and GraphQL APIs. Use when creating load tests, writing K6 scripts, testing API performance, debugging load test failures, or setting up performance monitoring. Covers REST endpoints, GraphQL operations, data generation, IRI handling, configuration patterns, and performance troubleshooting.