LLM Mart Basic
@llm-mart · Joined Jun 2026
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.
Use this skill when you need evidence-bounded recovery-testing analysis and validation preparation; triggers include 恢复测试 and recovery-testing.
End-to-end ML toolkit with 26 CLI commands. Use when training models, tuning hyperparameters, detecting data drift, generating HTML reports with charts, profiling datasets, detecting anomalies, forecasting time series, checking fairness, or serving models as REST APIs. Prefer ove
Wrap a public HTTP API (Open-Meteo weather as the demo) with credential handling, error normalisation, and a single retry on transient network failures. Demonstrates the production-shaped baseline for any "skill that calls an external service" — env-based secrets, structured erro
Read a CSV file from disk, compute per-column min/mean/max for every numeric column, emit the result as JSON. Stdlib-only Python; no pandas, no numpy. Demonstrates the simplest possible "give me a file path, get back structured analysis" skill — a deliberate baseline for any skil
Summarise a chunk of text down to roughly `length` words using the agent's configured LLM provider. Input shape `{ text: string, length?: number }` on stdin, JSON; output shape `{ summary: string }` on stdout, JSON. Minimal: ~50 lines, no streaming, no retries — a deliberate base
Unified operational manual for AI agents driving the Chrono AI service stack — NyxID (identity, services, orgs, OAuth clients, proxy) AND Ornn (skill lifecycle — search, pull, install, execute, build, upload, share). One skill, two halves, one identity bootstrap, one set of failu
The manual an AI agent loads to operate Ornn — the model-agnostic skill-lifecycle API (an npm-style registry + CLI for agent skills) — via the NyxID CLI (`nyxid proxy request ornn-api …`). Load and follow this skill WHENEVER the user asks to do anything with Ornn skills or skills
Operational manual for AI agents using the Ornn skill-lifecycle API via direct HTTPS with a NyxID bearer token (`curl -H "Authorization: Bearer $TOKEN" …`). Once loaded, the host agent can search / pull / execute / build / upload / share skills end-to-end. Authoritative contract
Use this whenever you need to know what is actually in a database, warehouse, or DuckDB file before you trust it: ranked inventory of what exists, column profiles, PII detection, grain and data-quality problems, verified join inference, Mermaid ER diagrams, guarded ad-hoc SQL pro
Use this to keep a dbt project and its semantic layer correct as the warehouse and the business change, including a semantic layer that is native Apache Ossie documents rather than dbt. It detects drift on four axes and proposes the fix: schema drift (source columns and tables ad
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.
Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.
Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.
An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.
/btw
Btw
The one exception to codeArbiter's slash-command pipeline: a lightweight question-and-answer
/checkpoint
Checkpoint
A periodic sweep of the entire codebase with the same reviewer fleet `/ca:review` uses per-diff,
/chore
Chore
This is the lane for changes with no behavior to test-drive — prose edits, a version bump on an
/cleanup
Cleanup
Use this after a pull request has merged but your local checkout is still on the topic branch.
/commands
Commands
Prints the public command catalog straight from `COMMANDS.md` — the plugin's own single source
/commit
Commit
This is the single entry point for turning staged work into a commit — nothing in codeArbiter
/conflict
Conflict
The protocol for a rule conflict — not a skill route, an orchestrator-level halt. When two sources
/context-check
Context check
An optional, on-demand drift audit for the bypass case: a merge, a direct push, or a manual edit
/create-context
Create context
This is the populator for a project that already has code to read. Instead of interviewing you about
/debug
Debug
This is where an unexplained defect goes before anyone touches code. The investigation is
/decompose
Decompose
This is the populator for a project that has no code yet to read. Rather than guessing at
/doctor
Doctor
Proves the install is actually enforcing, rather than just present. codeArbiter's worst failure
/feature
Feature
This is the standard entry point for new work with a human in the loop at every step. A short
/fix
Fix
This is the entry point for a defect that already has a known cause, or one you can describe
/init
Init
This is how a repository opts into codeArbiter for the first time. It writes the root-level state
/metrics
Metrics
A bare-numbers governance glance — three metrics, each with a trend arrow against the prior
/new-skill
New skill
The only permitted entry to creating a new codeArbiter skill. It hands off to the `skill-author`
/override
Override
The sanctioned, logged escape hatch. A routine gate — a lint rule, a style check, a non-security
/pr
Pr
This is the only path to opening a pull request — there's no direct push or force-push to the
/preview
Preview
A zero-onboarding, read-only dry-run of the reviewer fleet against whatever is currently
Persistent session memory for AI coding agents — local-first, with on-device inference, associative recall, and drift detection. Works with Claude Code, Cursor,…
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