LLM Mart Basic
@llm-mart · Joined Jun 2026
Runs Ansible diagnostic playbooks using ansible-runner and the Ansible Collections ecosystem (ansible.builtin, community.general). Captures system health, service status, and log analysis across inventory hosts.
Executes ansible-playbook --check --diff mode and parses the JSON callback output using the ansible.posix.json callback plugin. Identifies tasks that would change, predicts idempotency issues, and generates change impact reports.
Validates Ansible playbooks using ansible-lint and the Ansible Galaxy API. Performs check-mode dry runs, validates role dependencies, and detects deprecated module usage across collections.
Validates Ansible playbooks in check mode using ansible-playbook --check --diff and the Ansible Python API. Detects idempotency issues, undefined variables, and unreachable hosts before production runs.
Validates Ansible playbooks and roles using ansible-lint and yamllint APIs. Enforces best practices for idempotency, variable naming, and handler usage with custom rule profiles.
Validates Ansible playbooks using ansible-lint with custom rule plugins and the Ansible Collections API. Checks for deprecated modules, missing handlers, insecure variable practices, and role dependency conflicts.
Imported from agentskillexchange/skills/skills/ansible-playbook-runner.
Generates Ansible playbook YAML with proper module usage, handler chains, and role structures using ansible-core built-in modules. Supports Jinja2 template rendering and vault-encrypted variable files.
Generates and validates Ansible playbooks from infrastructure requirements. Uses ansible-lint for validation and queries Ansible Galaxy API for discovering certified roles and collections.
Executes Ansible playbooks against dynamic inventories from AWS EC2 or Azure, decrypting Ansible Vault secrets via HashiCorp Vault KV v2 API. Streams task output in real time and posts a per-host pass/fail summary to Slack. Supports --check mode for dry-run validation before live
Executes Ansible playbooks for server diagnostics and remediation using ansible-runner Python SDK. Supports inventory parsing, vault-encrypted credentials, and real-time task output streaming.
Instruments Anthropic API calls to log token usage, latency, and cost per request using the Anthropic TypeScript SDK. Wraps the anthropic.messages.create method to capture usage.input_tokens, usage.output_tokens, and timing from the API response. Writes structured logs to CloudWa
Anyquery is a SQL query engine that lets you run SQL against 40+ apps, files, and databases including GitHub, Notion, Chrome, and Apple Notes. Built on SQLite with MCP server support for connecting AI agents to structured data across services.
Imported from agentskillexchange/skills/skills/apache-airflow-mcp.
Manages Apache Avro schema evolution with compatibility checking via Confluent Schema Registry API. Validates forward, backward, and full compatibility across schema versions automatically.
Maps and transforms data between systems using Apache Camel route definitions and the Camel Component API. Supports XSLT, JSONPath, and DataFormat transformations via camel-core SDK.
Diagnoses Kafka consumer group lag using the Kafka AdminClient API and JMX metrics exposed via the Confluent Metrics API. Identifies slow consumers, topic partition hotspots, and broker rebalance storms that contribute to lag growth. Provides a step-by-step runbook to tune fetch.
Extracts and transforms Avro, Protobuf, and JSON Schema definitions from Confluent Schema Registry. Generates typed data models and validates schema compatibility using the Schema Registry REST API.
Imported from agentskillexchange/skills/skills/apache-kafka-stream-processor.
Processes real-time event streams using KafkaJS consumer groups and transforms messages with configurable schemas. Handles partition rebalancing, offset commits, and dead-letter queue routing for failed transformations.
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.
Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.
/lineage-discovery
Lineage discovery
Discover testnet↔mainnet subnet lineage from repo configs and open a PR for review (pass --dry-run to report only)
/capture
capture
Triage raw inbox notes into reviewed repository destinations without deleting their sources.
/clean-ai-writing
clean-ai-writing
Audit and rewrite content to remove AI writing patterns
/content-shipped
content-shipped
Log a completed piece of content to content/log.md after the user confirms it was published.
/dream-apply
dream-apply
Validate a dream artifact, review each proposal, and apply only individually accepted changes.
/dream
dream
Run a curator pass against the validated memory directory and produce a proposal artifact.
/end
end
End a session — log what happened, update state and the decision log, propose memory updates, and check for uncommitted or unpushed work
/find-context
find-context
Find relevant context files by topic. Use when you need to load files for a topic without a slash command, or when a task spans multiple domains.
/migrate-gemini
migrate-gemini
Inventory and migrate selected Gemini CLI workflows with dry-run review and parity checks.
/mine-gemini-workflows
mine-gemini-workflows
Find repeated workflows in selected Gemini CLI sessions and draft portable skills after review.
/reconcile
reconcile
Scan multi-session drift and offer individually reviewed fixes only after explicit approval.
/recover
recover
Scan orphaned worktrees and stale branches, then offer explicit approval-gated cleanup.
/setup
setup
Guided onboarding or import for durable workspace context
/start
start
Start a session — load state files, flag staleness, and give a briefing on current priorities, deadlines, and blockers
/today
today
Create a morning heartbeat from repository state and update the local heartbeat log.
/update
update
Mid-session checkpoint — append progress to today's session log and update state files if a priority shifted, without ending the session
/distribution-audit
distribution-audit
Maintainer-only. Find every file that would newly ship to adopters and decide, one file at a time, whether to ship it or withhold it. Drives the release CLI, which refuses to produce a manifest until every shipping file has an answer.
/gaia-audit
gaia-audit
Audit memory, wiki, and auto-loaded files for duplication, conflicting instructions, and stale content. The default path researches, then asks you a single Apply / Discuss / Decline question; on Apply it applies the report, files any out-of-scope problem as a tech-debt issue, then commits, opens a PR, and merges it on a main-branch run like /update-deps. Pass --apply to re-run the apply-and-publish stage against the most recent report.
/gaia-debt
gaia-debt
Fix the tech-debt backlog, a single issue or a recommended related batch, highest severity then oldest first, on a fresh isolated branch through the audit gate, closing the issue(s) on merge. Pass `list` to see the ordered backlog, `why <issue-number>` to explain the recommendation, or a bare `<issue-number>` to fix that issue directly.
/gaia-fitness
gaia-fitness
Health-check and auto-heal this project's Claude integration, triage, heal, verify, and report an F-to-A+ grade.
Your car as a chat-room agent: Raspberry Pi 5 + dashcam + local AI. CodeWatch's sibling for the garage.
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