LLM Mart Basic

Autotask billing item retrieval, approval-level workflows, and invoice search — covering billing item types, approval status filtering, and reconciliation of billable work against invoices for MSP finance teams.

Autotask Configuration Item (CI) asset management: CI types and categories, lifecycle status codes, the CI field schema, related-item relationships, DNS records, notes, and contract/billing associations for MSP infrastructure tracking.

Autotask contract and service agreement management - contract types (recurring services, block hours, time & materials, fixed price, retainer), service/service bundle associations, SLAs, and how contracts drive billing for MSP account managers.

Autotask CRM entities - companies (accounts), contacts, and sites/locations - including field references, company type classifications, and how these records underpin tickets, contracts, and projects for MSP account management.

Autotask expense report and expense item structure - the report/item parent-child relationship, approval status workflow, expense categories, payment types, and the billable vs reimbursable distinction for MSP operational expenses.

Autotask picklist and reference-data lookups — queues, ticket statuses, ticket priorities, and project phases — the instance-specific configured values required before creating or filtering tickets and other entities.

Autotask product catalog structure - Products, Services, and Service Bundles - and how Price Lists override default unit pricing. Covers product/service fields, inventory tracking, and cost-vs-billing margin analysis for MSP quoting and procurement.

Autotask project structure - projects, phases, tasks, and milestones - including project and task fields, status values, resource assignment, and how project work links to contract billing for MSP project managers.

Autotask quote structure and line items - quote item types (product, service, service bundle, labor, expense, shipping), the mutually-exclusive catalog reference rules, and the three discount mechanisms (unit, line, percentage) used to build customer proposals.

Autotask Service Call data model - the ServiceCall / ServiceCallTicket / ServiceCallTicketResource three-layer structure - covering fields, status codes, and how tickets and technicians (resources) are linked to scheduled work.

Autotask ticket notes, attachments, and charges — the secondary entities attached to tickets: retrieving/searching notes and attachments, and creating, updating, or searching ticket charges for labor and expenses billed directly to a ticket.

Autotask ticket lifecycle: status/priority codes and transition rules, the ticket field schema, SLA calculation and clock behavior, escalation rules, ticket metrics, and the MCP tool surface (create, update, search, history, notes) for MSP service desk operations.

Autotask time entry structure: approval status codes and workflow, the time entry field schema, the billing rate hierarchy, budget and contract-limit validation, utilization analytics, and the MSP business rules for rounding and minimum billing increments.

The Autotask MCP lazy-loading pattern - four meta-tools (list_categories, list_category_tools, execute_tool, router) that expose the full 39+ tool catalog progressively instead of loading every tool schema upfront, plus the natural-language router for intent-based tool lookup.

Kaseya Quote Manager (Datto Commerce) API fundamentals: API-key auth and the gateway's header translation, the read-only `kqm_<entity>_list`/`_get` tool surface across the sales, procurement, catalog, CRM, and org domains, page/pageSize/modifiedAfter pagination, rate limits, and

Kaseya Quote Manager procurement data: purchase orders with their lines and costs, the suppliers they are placed with, and product-supplier records mapping catalog products to supplier SKUs and pricing. Read-only tool surface.

Kaseya Quote Manager quoting data: the quote → section → line item hierarchy, and the sales orders, order lines, and payments a quote becomes once accepted. Read-only tool surface.

Better Stack MCP and API surface across Uptime, Telemetry (Logtail), and Error Tracking: available tools, Bearer token authentication, API structure, cursor-based pagination, rate limiting, and error handling.

Better Stack incidents: incident records raised by uptime monitors or reported manually, and the triage, acknowledgment, and resolution lifecycle.

Better Stack log management (Logtail): log sources, structured log search and query syntax, log-based alerting, and log analysis workflows.

A green PR, a controller reporting success, and not one line of the new code running

Run autonomous hunt loop on a target — scope check → recon → rank surface → hunt → validate → report with configurable checkpoints. Usage: /autopilot target.com [--paranoid|--normal|--yolo]

Build an exploit chain — given bug A, finds B and C to combine for higher severity and payout. Knows common chain patterns: IDOR→ATO, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth. Usage: /chain

Active vulnerability hunting. Two-track dispatcher — asks Red Team vs WAPT, hands off to hunt-dispatch skill and sibling commands. Usage: /hunt target.com | /hunt *.target.com | /hunt targets.txt [--vuln-class X] [--source-code P] [--chrome]

On-demand intelligence fetch for a target — CVEs, disclosed reports, new features. Pulls NVD/GitHub-Advisory CVEs + bundled disclosed reports + hunt memory context. Usage: /intel target.com

Inspect or rotate the autopilot ledger JSONL files (findings.jsonl, negatives.jsonl). Caps file size and keeps N rotated backups so memory does not grow unbounded.

Pick up a previous hunt on a target — shows hunt history and untested surface from the autopilot ledger. Usage: /pickup target.com

Run full recon pipeline on a target — subdomain enum (Chaos API + subfinder), live host discovery (dnsx + httpx), URL crawl (katana + waybackurls + gau), gf pattern classification, nuclei scan. Outputs to recon/<target>/ directory. Usage: /recon target.com

Optional manual note on a target or the last confirmed finding. Capture is automatic during autopilot; this is for extra context. Usage: /remember

Write a submission-ready bug bounty report. Generates H1/Bugcrowd/Intigriti/Immunefi format with CVSS 3.1 score, proof of concept, impact statement, and remediation. Run /validate first. Usage: /report

Mandatory pre-flight scope check — verify an asset is in scope BEFORE any HTTP touch. Deterministic (deny-wins, default-deny) via engine/scope.py against the engagement's scope.md. Blocks out-of-scope testing. Usage: /scope <asset> [<asset> ...]

Show ranked attack surface for a target from its recon manifest + hunt memory. Deterministic backing is `cbh surface <target>` (reads recon/<target>/manifest.json); LLM layer adds ledger signal. Usage: /surface target.com

Meme coin and token security scan — checks for rug pull vectors (hidden mint, honeypot, fee manipulation, LP lock bypass, authority retention, bonding curve exploits, fake renounce, sandwich amplification). Manual 8-class grep audit (with an optional automated scanner if present). Usage: /token-scan <contract_path_or_dir> [--chain solana]

Quick 7-Question Gate triage on a finding before writing a report. Kills N/A submissions before they happen. Faster than /validate — for quick go/no-go decisions. Usage: /triage

Validate a finding — runs 7-Question Gate + 4-gate checklist. Kills weak findings before report writing. Prevents N/A submissions that hurt validity ratio. Usage: /validate

Smart contract security audit — runs through 10 bug class checklist (accounting desync, access control, incomplete path, off-by-one, oracle errors, ERC4626, reentrancy, flash loan, signature replay, proxy/upgrade). Applies pre-dive kill signals first. Generates Foundry PoC template for confirmed findings. Usage: /web3-audit <contract.sol>

Crabbox is a single CLI (`crabbox`). Commands are top-level, not nested under a

`crabbox actions` prepares a leased box from your repository's own GitHub

See [Runtime adapter stack](../features/runtime-adapter-stack.md) for the

`crabbox admin` groups trusted operator controls for coordinator-backed leases and the cloud resources behind them. Use it to inspect every lease the broker tracks, reconcile expired leases against live cloud state, force-release or delete a backing server, print provider IAM pol

`crabbox artifacts` turns a desktop lease into durable QA evidence: it collects

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.