LLM Mart Basic
@llm-mart · Joined Jun 2026
Use HarnessKit to inventory, audit, enable, disable, and deploy skills, MCP servers, plugins, hooks, CLIs, configs, memory, and rules across multiple coding agents.
Use Chops to discover, search, edit, and organize local or remote agent skills across Claude Code, Cursor, Codex, Windsurf, Amp, Copilot, and Aider.
Use REUSE when an agent needs file-level licensing clarity instead of guessing from a single top-level LICENSE file. The agent checks compliance, adds or verifies SPDX headers, pulls missing license texts into LICENSES/, and produces a concrete remediation list or SPDX export.
Use Avoid AI Writing when an agent needs a repeatable audit, rewrite, or in-place edit workflow for removing AI writing patterns from draft prose.
Run a repeatable HIG compliance audit over app code before shipping UI changes, then use the findings to guide remediation.
Use Cloudsplaining when an agent needs to flag privilege-escalation paths and overbroad IAM permissions before an AWS policy change reaches production.
Use Retentioneering's Python toolkit, MCP server, and agent skills to run reproducible customer-journey and event-log analytics that agents can validate and cross-check.
Run targeted AWS, Azure, or GCP security and compliance audits when an agent needs actionable cloud findings instead of a generic cloud-security platform overview.
Run CodeBurn locally or as an MCP server so agents can inspect token usage, cost, model mix, project spend, and budget waste across coding tools.
Run a focused security pass on GitHub Actions workflows before merge so token misuse, dangerous permissions, and unpinned actions are caught early.
This ASE skill uses zizmor to audit GitHub Actions workflows and composite actions for security mistakes before they ship. An agent can scan local repos or remote GitHub repositories, flag risky permission scopes and unsafe workflow patterns, and return plain output, GitHub-nativ
Produce a license inventory for Go module dependencies before release, procurement review, or open-source due diligence.
Uses Lynis to run an on-host security audit and turn the findings into a prioritized hardening checklist for an agent or operator. Invoke it when a machine is about to become internet-facing, after base image changes, or whenever you need a quick read on hardening drift instead o
This skill uses OpenClaw's healthcheck workflow to inspect the host running the assistant, surface risky exposure, and turn the findings into a staged hardening plan. It is for operator-style audits with explicit approval gates, not a generic software listing or a replacement for
Use Deptry when an agent needs to verify that a Python project's declared dependencies still match the imports the code actually uses. The agent scans the codebase, flags unused direct dependencies, missing declarations, and transitive imports that only work by accident, then tur
Scan Python requirements and environments for known vulnerable or malicious packages before they move further through delivery or promotion workflows.
Check Python environments and requirements files for published vulnerabilities before shipping, upgrading, or approving dependency changes.
Inspect an SSH endpoint or config for outdated ciphers, key exchange choices, and hardening gaps before exposure or upgrades.
Use GEO SEO Claude when Claude Code should run repeatable AI-search and SEO audits with citability, crawler, schema, platform, and report workflows.
Use Agent QA's CLI and MCP server to author, validate, run, debug, and triage natural-language web and mobile regression tests with persistent test memory and reviewable run evidence.
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.
/install
install
Install a rule from the catalog
/list
list
List all rules with their status
/catchup
catchup
Context recovery after session breaks. Summarize recent git changes, extract key decisions, and identify pending work.
/justify
justify
Audit changes for additive bias, Iron Law compliance, and minimal intervention. Justifies every change against a "simplest safe change" standard.
/karpathy-check
karpathy-check
Pre-flight gate walking four Karpathy principles (think first, simplicity, surgical edits, goal-driven). Use before implementation.
/stewardship-health
stewardship-health
Display stewardship health dimensions for one or all plugins
/structured-review
structured-review
Start a structured review workflow with evidence logging and formatted output
/reinstall-all-plugins
reinstall-all-plugins
Uninstall and reinstall all Claude Code plugins to refresh cache and resolve version mismatches.
/update-all-plugins
update-all-plugins
Update all installed Claude Code plugins from all marketplaces.
/verify-plugin
verify-plugin
Verify plugin behavioral contract history via GitHub Attestations (SLSA)
/garden
garden
Manage and analyze digital gardens for evolving knowledge bases, including health metrics.
/navigate
navigate
Search and navigate across memory palaces to find and retrieve stored information.
/palace
palace
Create and manage memory palaces, the unified storage for knowledge captured from research sessions.
/review-room
review-room
Capture and retrieve PR review knowledge in project memory palaces.
/skill-logs
skill-logs
View and manage skill execution memories stored by memory-palace.
/close-issue
close-issue
Analyze if issues (GitHub/GitLab) can be closed based on commits
/create-issue
create-issue
Create a new GitHub issue with formatted description, labels, and optional reference links
/update-labels
update-labels
Reorganize GitHub issue labels into type, priority, and effort groups.
/oracle-setup
Oracle setup
Provision the oracle ML inference daemon
/analyze-tests
analyze-tests
Analyze Python test suites for quality, coverage, and improvement opportunities.
An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间,让人与智能体在此协作
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