LLM Mart Basic

Use this skill when a Salesforce matter must be assigned a standardized matter type, risk tier, or escalation gate before routing or handoff. Defines all matter types (org-config, automation, code, integration, security/IAM, data, sales/CPQ, service/SLA, experience-cloud, marketi

Use this skill when a Salesforce matter must be classified and routed to the right specialist agent, when a matter crosses multiple Salesforce domains and needs parallel review, or when specialist agents disagree and the conflict must be resolved. It defines routing rules per mat

Use this skill to statically review AI/BI Genie agent and dashboard design: agent scoping (30-table limit), instructions and trusted assets, metric-view correctness, dashboard limits and rendering, benchmark design and honest accuracy reading, and the critical 'Individual data' v

Use this skill to review Databricks data protection and privacy design for regulatory alignment and least-privilege enforcement: row filters and column masks, ABAC policies, data classification, deletion and GDPR erasure mechanics, Delta Sharing egress, residency and Geo constrai

Use this skill to design and verify data quality expectations, table constraints, Lakehouse Monitoring, freshness detection, event-log interrogation, quality SLAs, and downstream quality signaling for Lakeflow pipelines. Reads pipeline source, table schema, expectations, monitor

Use this skill to review a Declarative Automation Bundle configuration, authentication setup, and deployment flow against production readiness criteria: bundle structure, deployment modes, run-as identity boundaries, variable resolution timing, OAuth and environment-variable auth

Use this skill to statically review Databricks cost and cost-attribution: system.billing.usage and system.billing.list_prices for correct joins, custom-tag-based attribution with coverage-confidence reporting, DBU uptime charging semantics, serverless versus classic cost comparis

Use this skill to review generative-AI agent design on Databricks: Mosaic AI Agent Framework and ResponsesAgent interface, Databricks AI Search index variant and sync-mode choice, retrieval and context engineering, MCP server category and trust boundaries, external model-provider

Use this skill to review generative-AI evaluation, tracing, and observability design on Databricks: MLflow Tracing instrumentation and span design, trace storage and governance, `mlflow.genai.evaluate()` harness design, the judge-versus-scorer distinction, built-in judge selectio

Use this skill to review Databricks identity and network security design for proper admin separation, SCIM/federation configuration, credential hygiene, and network boundary enforcement: admin roles, service-principal posture, OAuth vs PAT, token lifecycle, IP access lists, serve

Use this skill to design Lakeflow Spark Declarative Pipelines: medallion layering, Lakeflow Jobs orchestration and task dependencies, Delta table layout (liquid clustering, deletion vectors, Predictive Optimization), Auto Loader ingestion, schema evolution and `_rescued_data`, ma

Review and guide Databricks Lakehouse engineering on Azure: medallion architecture (bronze/silver/gold), Delta Lake pipelines, ADLS Gen2 access via Unity Catalog external locations and storage credentials, Access Connector managed identity, cluster access mode enforcement, AKV-ba

Mutating-runtime live guard for Unity Catalog privilege management on Azure Databricks. Executes exactly ONE GRANT or REVOKE of a single privilege on a single Unity Catalog securable (schema, table, or volume) to a single principal — with explicit written human approval, dry-run

Use this skill to classify an incoming Databricks task and route it to the narrowest owning specialist on the Databricks board. Classifies on intent, business context, artifact type, blast radius, required evidence, implied runtime authority, and specialist ownership; emits a sin

Use this skill to review machine-learning model lifecycle on Databricks: MLflow 3 with Unity Catalog as default registry, alias-based promotion and champion/challenger patterns, feature-store design with point-in-time correctness, Model Serving endpoint configuration and traffic

Use this skill to review Databricks account and workspace topology for scalability and Well-Architected alignment: metastore-per-region constraint, workspace segmentation ratios, serverless vs classic placement, catalog organisation, cross-region and cross-organisation access pat

Use this skill to diagnose and design platform reliability using system-table evidence, job and pipeline execution review, cluster policies, instance pools, quota headroom, and disaster-recovery posture: job timeouts and retries, run-history retention, managed DR design, incident

Use this skill to statically review SQL warehouse and query performance: warehouse type and sizing for concurrency, Photon and Predictive I/O applicability, three-tier caching semantics and when a cached result is misleading, query-profile reading for skew and spill, data layout

Use this skill to verify Structured Streaming query correctness and recovery: state-schema immutability, checkpoint compatibility across restarts, watermark semantics, trigger selection (AvailableNow, Once, ProcessingTime), exactly-once vs at-least-once sinks, foreachBatch idempo

Use this skill to review Unity Catalog governance design for privilege correctness, ownership clarity, and least-privilege enforcement: three-level namespace design, GRANT inheritance, ownership, workspace-catalog binding, governed tags, storage credentials, and audit completenes

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.

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

Your car as a chat-room agent: Raspberry Pi 5 + dashcam + local AI. CodeWatch's sibling for the garage.

The enterprise gateway for autonomous agents. Identity management, per-channel isolation, credential vault, per-session tamper-evident audit log.

AI agent for Home Assistant — talk to your home, create automations in plain language, analyze cameras with face recognition, and get proactive anomaly alerts.…

A desktop app to prototype agent ideas, inspect every harness step, replay failures, and evaluate performance, all in one place. Local-first, cloud-ready for ma…

Local-first AI memory you can see, edit, and override — portable across Claude Code, Codex, Cursor, Windsurf, and other MCP coding tools.

Web & desktop (Electron) client for the OCTO open workplace — one React + TypeScript codebase shipping browser and PC surfaces, with first-class AI agent UX.

同花顺官方 A股金融数据服务，提供股票实时行情、历史行情、财务报表、指数、板块、涨停等数据，适用于 AI Agent、量化研究和应用开发，支持 API、MCP、CLI 和 Python。Official Tonghuashun (HiThink) A-share financial data service provi…

Open-source desktop SQL workspace for PostgreSQL, MySQL/MariaDB, SQLite and 15+ more databases like DuckDB, ClickHouse, Redis and Firestore. Built-in MCP server…

🤖 Local-assist BOSS Zhipin CLI for AI agents — search, welfare filtering, shortlist, JSON-envelope output; low-risk & compliant by default.

The local-first sidebar AI agent for ComfyUI — runs on your own Claude OR ChatGPT subscription (no API keys, no extra LLM costs). Drives your live graph: edits,…

Persistent memory for Claude Code — identity, context, and continuity across sessions

🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI…

A curated list of plugins, skills, MCP servers, patch/profile layers, orchestrators & UIs for DeepSeek Harness (DSH). Visualization · PPT · Coding · Agents · Lo…

World Memory Protocol.

Run your own organization of agents.

The most RAM efficient harness

AI-native, free, open-source alternative to Jira, Trello, ClickUp & Monday. Built for Scrum teams where humans and AI agents collaborate as equals — on the same…

Free open-source desktop app that scrapes JAV metadata and generates NFO + cover art for Jellyfin, Emby & Kodi. No Docker, no CLI — one-click install on Windows…

A cross-platform desktop app to manage Agent Skills in one place and sync them to multiple AI coding tools’ global skills directories — “Install once, sync ever…

天枢 (Tianshu) 是一个基于harness工程的终端编程智能体运行时（Tui X Gui），针对DeepSeek V4 做了前缀缓存工程优化（长会话实测稳态命中率 97–99%）和深度适配。它跳出了传统 AI 编程助手把大模型仅当成“工具”的局限，基于认知虚拟机 (CVM)、自感知层和信息素（Stigmergy…