LLM Mart Basic
@llm-mart · Joined Jun 2026
AutoGen is Microsoft's open-source framework for building multi-agent systems where AI agents converse with each other and humans to solve tasks, with support for tool use and human-in-the-loop workflows.
Lets agents operate Google NotebookLM/Gemini Notebook through a Python API, CLI, skill, MCP server, or REST server to create notebooks, add sources, ask cited questions, generate artifacts, and download exports.
Operate Lark and Feishu work objects from one agent-ready CLI instead of stitching together separate APIs and browser flows.
Use ARIS to run Markdown-based agent skills for literature review, idea discovery, cross-model critique, experiment planning, and paper-writing support.
Use NotebookLM through MCP or a local REST API to run cited Q&A, generate Studio artifacts, and manage high-volume research batches.
Use GitHub Agentic Workflows to let an agent triage issues, inspect CI failures, or deliver scheduled repository upkeep inside GitHub Actions with explicit workflow definitions and reviewable runs. This is for bounded, repeatable repository operations, not for listing GitHub as a
Add controlled retries to pytest runs so agents can contain flaky tests and report final failures without rerunning whole suites by hand.
Assess a public HTTP(S) page before building browser automation, extraction, or an integration. Use this skill to collect bounded structural evidence, readiness signals, risk flags, acceptance tests, and remediation priorities without sending credentials or accessing private targ
Automattic WordPress Remote MCP connects MCP clients to live WordPress sites using OAuth, JWT, or application passwords. It is aimed at agents that need to read or operate against WordPress content and site features through a maintained remote MCP bridge.
5-phase repeatable structure for autonomous agent sessions: context-load, tiered work-selection, coordination claim, execute, and persist-learning. Prevents duplicate work across concurrent sessions and ensures every run produces durable artifacts. Runtime-agnostic — Claude Code,
Imported from agentskillexchange/skills/skills/aws-cdk-scaffolder.
Monitors AWS CloudFormation stacks for configuration drift using the AWS SDK DetectStackDrift and DescribeStackResourceDrifts APIs. Generates remediation templates and integrates with AWS Config rules for continuous compliance.
Diagnoses failed AWS CloudFormation stack operations using the AWS CLI (aws cloudformation describe-stack-events) and cfn-lint validator. Traces resource creation failures, rollback causes, and nested stack dependency chains.
Normalizes and enriches AWS CloudTrail JSON logs into OCSF (Open Cybersecurity Schema Framework) format. Maps eventSource/eventName pairs to MITRE ATT&CK technique IDs using the MITRE ATT&CK STIX API.
Creates and manages CloudWatch alarms using the AWS SDK for JavaScript v3 (@aws-sdk/client-cloudwatch). Configures metric math expressions, composite alarms, and SNS notification routing via @aws-sdk/client-sns.
Diagnoses firing AWS CloudWatch alarms by querying CloudWatch Metrics, alarm history, and related AWS Config resource snapshots via the AWS SDK. Correlates metric anomalies with recent infrastructure changes to suggest root cause hypotheses. Outputs a structured incident summary
Generates structured incident runbooks from AWS CloudWatch alarm configurations using the CloudWatch DescribeAlarms API and AWS Systems Manager documents. Links alarms to remediation procedures automatically.
Automates incident response for AWS CloudWatch alarms using boto3, the CloudWatch GetMetricData API, and AWS Systems Manager runbook documents. Maps alarm states to diagnostic procedures and remediation actions.
Triages AWS CloudWatch alarms using boto3 CloudWatch.describe_alarms, CloudWatch Logs Insights queries, and AWS X-Ray trace analysis via the xray-sdk. Correlates alarm triggers with deployment events.
Triages AWS CloudWatch alarms using the CloudWatch DescribeAlarms API, GetMetricData for historical analysis, and CloudTrail LookupEvents for root cause correlation. Prioritizes alerts by blast radius and provides remediation playbooks.
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.
/checkpoint
Checkpoint
Periodic multi-reviewer sweep of the whole codebase — surfaces a triaged checkpoint report.
/chore
Chore
Sanctioned lane for non-behavioral work — docs-only edits, dependency bumps, reverts. Type-scaled gates; no TDD demanded of prose.
/cleanup
Cleanup
{{SKILL_ENTRY:post-merge-cleanup}}
/commands
Commands
Show the codeArbiter command catalog — the public command list and what each routes to.
/commit
Commit
{{SKILL_ENTRY:commit-gate}}
/conflict
Conflict
Stop everything and surface a rule conflict — persona vs. docs vs. code. Present both sides and the conflict-hierarchy level; the user resolves. No silent reconciliation.
/context-check
Context check
{{SKILL_ENTRY:context-check}}
/create-context
Create context
{{SKILL_ENTRY:context-creation}}
/debug
Debug
{{SKILL_ENTRY:debug}}
/decompose
Decompose
{{SKILL_ENTRY:decompose}}
/doctor
Doctor
Verify the active host install, package, command ownership, enforcement{{IF:pi}}, wrapper self-test, and active-dispatch coverage gap{{ELSE}}, and harmless live-fire probe{{END}}. Read-only.
/feature
Feature
Start a feature: brainstorm a spec, get it approved, then drive it test-first through the pipeline. The one entry to implementation.
/fix
Fix
Fix a confirmed bug: a failing regression test first, then a minimal fix, then the rest of the tdd gates.
/init
Init
Opt this repo into codeArbiter — scaffold the root-level .codearbiter/ state store.
/metrics
Metrics
Read-only 3-metric governance glance — override rate, small-lane rate, sprint low-confidence ratio — each with a trend arrow vs. the prior 20-commit window.
/override
Override
Sanctioned, logged bypass of a gate or hard rule — one audit line, then proceed.
/pr
Pr
{{SKILL_ENTRY:finishing-a-development-branch}}
/preview
Preview
Zero-onboarding, read-only dry-run of the reviewer fleet against the current uncommitted diff. Predicts reviewers, runs the state-free secret scan, writes nothing.
/prune
Prune
Trim transcript clutter to extend session lifetime — analyze, prune a copy, or toggle the after-each-turn service. Dry-run by default; gains land at resume/compaction, not the current turn.
/reconcile
Reconcile
{{SKILL_ENTRY:decision-variance}}
VCP 部署在 AI 模型 API 与前端应用之间,是面向AGI OS开发和探索的工业级基建示范项目。通过统一指令协议、多层级持久化记忆、分布式插件引擎及多 Agent 协作框架,将原本“无状态、无记忆、无工具调用能力”的大语言模型,彻底改造成拥有永久自我意识、物理世界操作权及群体协作智能的完整智能体系统。
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