LLM Mart Basic
@llm-mart · Joined Jun 2026
Use when the user requests diagrams, flowcharts, architecture diagrams, ER diagrams, UML / sequence / class diagrams, SysML / MBSE diagrams (block definition, internal block, requirement, parametric), BPMN business process diagrams, swimlane / cross-functional flowcharts, network
Read the clawock request file, write decision.json, let clawock validate. Use when a clawock run request is present in .clawock/work/.
kcn 每个工作日 08:00 HKT 跑一次的盘前全 swarm 深度分析。harness 化:`clawock brief preflight` 计算并编译 typed decision packet,LLM 只做 Tier 1/2/3/Judge 与受限 judgment overlay,`clawock brief postflight` 验证、生成 Pages projection、commit 并自动投递微信。**输出**:完整 markdown、结构化 plan、受限 judgment JSON 与紧凑微信卡。**只在每日 8 点 cron
Manual, event-driven earnings review for a US or HK holding, backed by first-party filings. Use when a company reports, when a management promise comes due, or when a thesis review needs primary-source numbers. Produces a structured artifact under memory/earnings/<TICKER>/<period
Pre-investment screening gate for a name that is not yet researched or held. Use before spending a deep-research run or opening new exposure, to answer only "is this understandable and researchable enough to proceed?". Grades information richness separately from investment qualit
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
Workspace-aware Hong Kong stock analysis for kcn. Routes through clawock analyze-hk (Tencent primary + Eastmoney full-batch independent cross-check/fallback → stooq → yfinance) for price/技术指标/news, layered with HK-specific concepts — 南向资金, HSTECH 方向, 杠杆 ETF 衰减, 老千股警惕, T+0 无涨跌幅. U
OpenClaw **system-level** maintenance — cleans sessions/.bak rotation, disk bloat under ~/.openclaw/, validates model fallback chain, audits cron health, prunes short-term-recall noise. Run after `openclaw update` or every 1-2 weeks. Different from **workspace document tune-up**
Safe OpenClaw version upgrade with regression checklist. Use when kcn says "升级 openclaw" / "openclaw 有没有更新" / "/openclaw-upgrade", or after noticing a newer npm version. Checks for in-flight crons, uses the built-in updater, reapplies all host-local dist patches, and runs the ful
Single-pass holdings analysis for kcn using workspace files and the local fetch chain. Use for portfolio review, position risk ranking, US-HK cross-market read, leverage ETF risk assessment, and one-shot actionable reports based on portfolio.json plus fresh quotes from `clawock a
Multi-agent swarm review of kcn's current holdings. Inspired by TauricResearch/TradingAgents framework already in workspace — three-tier analysis (analysts → bull/bear debate → risk debate + judge) with confidence scoring. Use for post-close reviews, holiday/next-session planning
Imported from kcnyu/clawock/skills/scrapling.
Turn an investment agent into a supply-chain bottleneck hunter. Use this skill for source-backed investment research, live market/theme scans, AI/semi/technology value-chain mapping, A-share/HK/US stock screening, thesis stress tests, and Serenity-inspired research conversations.
AI-optimized web search via Tavily API. Returns concise, relevant results for AI agents.
Trading analysis and education. Technical analysis, chart patterns, risk management, and position sizing for stocks, forex, and crypto.
Workspace-aware US stock analysis for kcn. Routes through `clawock analyze-us` / `clawock us-quotes` instead of generic web search, then layers fundamental/technical/news analysis on top. Use when user asks to analyze a US ticker (e.g. "analyze AAPL", "look at RKLB", "compare TSL
Produce an evidence-linked investment decision with an explicit bull case, opposing bear case, thesis invalidation conditions, bounded action, and reconciled order/FX amounts. Use when an agent must analyze a security or portfolio action and publish a verifiable decision artifact
Run a clawock investment decision — read the prepared request, research with the host's own tools, write decision.json with evidence and an explicit bull/bear debate, and let Python validate and settle. Use when the user asks for an investment decision or a clawock run request is
Implement a piece of work based on a PRD or set of issues.
Adopt OpenSpec in a repo that lacks it: scaffold the openspec/ layout, write baseline specs describing the system as it exists today (from code, docs, and git history), and deliver the whole thing as a documentation-only PR. One-time per repo. Trigger when the user asks to adopt
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.
/improve-agent
Improve agent
Improve an existing agent through performance baselines, prompt engineering, A/B testing, and staged rollout
/multi-agent-optimize
Multi agent optimize
Optimize multi-agent system performance through profiling, context window tuning, coordination efficiency, and cost and latency tradeoffs
/team-debug
Team debug
Debug issues using competing hypotheses with parallel investigation by multiple agents
/team-delegate
Team delegate
Task delegation dashboard for managing team workload, assignments, and rebalancing
/team-feature
Team feature
Develop features in parallel with multiple agents using file ownership boundaries and dependency management
/team-review
Team review
Launch a multi-reviewer parallel code review with specialized review dimensions
/team-shutdown
Team shutdown
Gracefully shut down an agent team, collect final results, and clean up resources
/team-spawn
Team spawn
Spawn an agent team using presets (review, debug, feature, fullstack, research, security, migration) or custom composition
/team-status
Team status
Display team members, task status, and progress for an active agent team
/api-mock
Api mock
Build realistic API mock servers with request stubbing, dynamic data, test scenarios, and contract testing
/performance-optimization
Performance optimization
Orchestrate end-to-end application performance optimization from profiling to monitoring
/feature-development
Feature development
Orchestrate end-to-end feature development from requirements to deployment
/block-no-verify
Block no verify
Set up PreToolUse hook to block --no-verify and other git bypass flags in Claude Code projects
/c4-architecture
C4 architecture
Generate comprehensive C4 architecture documentation (Context, Container, Component, Code) for a codebase using bottom-up analysis and four coordinated C4 agents.
/workflow-automate
Workflow automate
Automate CI/CD pipelines, releases, and development workflows with GitHub Actions, pre-commit hooks, and infrastructure automation
/code-explain
Code explain
Explain complex code, algorithms, and design patterns with step-by-step breakdowns, visual diagrams, and interactive examples
/doc-generate
Doc generate
Generate API, architecture, code, and user documentation from a codebase and automate keeping it current
/context-restore
Context restore
Restore saved project context and decisions to resume a session
/refactor-clean
Refactor clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
/tech-debt
Tech debt
Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.
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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