LLM Mart Basic
@llm-mart · Joined Jun 2026
Use this skill when you need to compare performance evidence across versions and assess regression risk; triggers include performance regression analysis.
Use this skill when you need to interpret performance results, evidence quality, and risk without inventing conclusions; triggers include performance result analysis.
Use this skill when you need Gatling performance scope, simulations, or runnable entry points; triggers include Gatling, Gatling simulations, and Gatling performance testing.
Use this skill when you need to design JMeter test plans with Thread Groups, samplers, data sets, assertions, timers, CLI runs, and HTML reports; triggers include JMeter performance testing, performance testing, and performance-test-jmeter.
Use this skill when you need to model realistic performance workload, traffic, and acceptance assumptions; triggers include performance workload modeling.
Use this skill when you need to determine test impact from a pull request or code diff; triggers include PR test impact analysis.
Use this skill when you need to analyze production-incident evidence, impact, and follow-up actions; triggers include production incident analysis.
Use this skill when you need to plan or assess evidence-based production verification after a release; triggers include production verification.
Use this skill when you need to design safe prompt-injection tests for AI systems and tool boundaries; triggers include prompt injection testing.
Use this skill when you need to test prompt behavior, regression risk, and output boundaries across versions; triggers include prompt testing and prompt-regression.
Use this skill when you need to turn invariants, generation domains, and shrinking strategies into reviewable property-test candidates; triggers include 基于属性的测试 and property-based test design.
Use this skill when you need evidence-bounded quality dashboard audiences, decision questions, panels, drill-downs, freshness, and alert boundaries; triggers include 质量仪表盘 and quality dashboard.
Use this skill when you need evidence-bounded quality-debt items, origins, impact, age, priority, ownership, and paydown tradeoffs; triggers include 质量债务 and quality debt.
Use this skill when you need evidence-bounded entry criteria, evidence requirements, owners, and exception paths for a delivery or release gate; triggers include 质量门禁 and quality gate.
Use this skill when you need evidence-bounded quality-practice maturity dimensions, rubric anchors, evidence sufficiency, and improvement gaps; triggers include 质量成熟度 and quality maturity.
Use this skill when you need evidence-bounded quality metric definitions, calculation rules, data sources, freshness, and anti-gaming boundaries; triggers include 质量指标 and quality metric.
Use this skill when you need evidence-bounded quality and delivery metrics, denominators, attribution limits, gaming risk, and the Human-use boundary; triggers include 质量生产力 and quality productivity.
Use this skill when you need to identify and prioritize quality risks from product, change, and evidence inputs; triggers include quality risk analysis.
Use this skill when you need evidence-bounded grounding, relevance, completeness, citation support, abstention, and answer-level evidence in RAG outputs; triggers include RAG 质量 and RAG quality.
Use this skill when you need evidence-bounded query variants, chunking, filters, recall/precision proxies, ranking, freshness, and retrieval evidence; triggers include 检索结果 and retrieval result.
Fourteen posts of being wrong in production, compressed to checkboxes
Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds
Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.
Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.
A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.
A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.
None of the safety came from the model. It came from six boring habits.
Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.
Six hours in, one step left, everything green, and the incident that didn't happen
CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.
Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it
An API request routing a model's tool call through an approval gate to a remote MCP server
31 config keys, two audits, and why the first one was wrong in both directions
The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.
Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.
Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.
"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks
stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.
The most important rule wasn't about what I could change. It was about what I was allowed to display.
Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.
/create-database-migrations
Create database migrations
Create and manage database migrations
/create-docs
Create docs
Analyze GitHub issue and create technical specification with implementation plan
/create-feature
Create feature
Scaffold new feature with boilerplate code
/create-jtbd
Create jtbd
Create a Jobs to be Done (JTBD) document for a product feature focusing on user needs
/create-onboarding-guide
Create onboarding guide
Create developer onboarding guide
/create-pr
Create pr
Create a new branch, commit changes, and submit a pull request with automatic commit splitting
/create-prd
Create prd
Create a Product Requirements Document (PRD) for a product feature
/create-prp
Create prp
Create a comprehensive Product Requirement Prompt (PRP) with research and context gathering
/create-pull-request
Create pull request
Guide for creating pull requests using GitHub CLI with proper templates and conventions
/create-worktrees
Create worktrees
Manage git worktrees for open PRs and create new branch worktrees
/cross-reference-manager
Cross reference manager
Manage cross-platform reference links
/debug-error
Debug error
Systematically debug and fix errors
/decision-quality-analyzer
Decision quality analyzer
Analyze decision quality with scenario testing, bias detection, and team decision-making process optimization.
/decision-tree-explorer
Decision tree explorer
Explore decision branches with probability weighting, expected value analysis, and scenario-based optimization.
/dependency-audit
Dependency audit
Audit dependencies for security vulnerabilities
/dependency-mapper
Dependency mapper
Map and analyze project dependencies
/design-database-schema
Design database schema
Design optimized database schemas
/design-rest-api
Design rest api
Design RESTful API architecture
/digital-twin-creator
Digital twin creator
Create systematic digital twins with data quality validation and real-world calibration loops.
/directory-deep-dive
Directory deep dive
Analyze directory structure and purpose
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