LLM Mart Basic
@llm-mart · Joined Jun 2026
为现有前端项目幂等接入 Lovinsp 并验证点击定位源码能力。支持明确输入与结果回读。Use to integrate Lovinsp
Create a @deepseek-ai/dsh-* plugin package end-to-end — choose the extension point or capability seam, scaffold the package, implement the tool/hook/service, and run the repo gates. 触发:新增插件 / 加工具 / 开发 capability。
Publish a validated DSH plugin package (`@deepseek-ai/dsh-*` or `@lovstudio/dsh-*`) to npm, git, or tarball channels and verify it loads in the DeepSeek Harness. Use when the user asks to publish, release, or ship a plugin. 触发:发布插件 / 上架插件 / release dsh 插件。
清理冗余 CSS 并在现有 Tailwind 项目中重构样式。支持明确输入与结果回读。Use to refactor CSS and Tailwind
依据 README 更新 GitHub 仓库描述、主题标签及已核实的官网链接。支持明确输入与结果回读。Use to update a GitHub
分析任意语言项目的目录职责并渐进调整文件结构和引用。支持明确输入与结果回读。Use to improve a project directory
依据项目代码完善 README、安装说明、使用示例与真实品牌资产。支持明确输入与结果回读。Use to create or improve
检查 Next.js 页面元数据、索引规则、站点地图与分享展示。支持明确输入与结果回读。Use to review and improve
配置并备份 Agent 状态栏,支持列出历史版本和恢复。支持明确输入与结果回读。Use to update an agent status
汇总 Git 与项目日志中的检查点、时间范围和演进记录。支持明确输入与结果回读。Use to list a project checkpoint
根据当前 Git 差异和历史记录保存项目里程碑及后续事项。支持明确输入与结果回读。Use to create a project checkpoint.
Write, review, and maintain architecture decision records with clear context, alternatives, consequences, confirmation links, and lifecycle governance. Use when a consequential technical decision or its enforceable architectural constraint must remain understandable. Do not use f
Run a structured multi-agent debate by spawning a panel of expert agents on any question, with convergence-aware iteration and typed synthesis output via the `agent-council` CLI. Use when a decision has genuine tradeoffs, high stakes, or hidden assumptions worth adversarial colla
Design, run, review, or release framework- and vendor-neutral evaluations and observability for AI agents. Use when defining agent evals, datasets, graders, trajectory review, regression analysis, release gates, production traces, or privacy-aware telemetry. Covers task and traje
Operate an evaluated agent with tools and authority in production through a runtime control plane covering versioning, staged rollout, fallback, cost and latency budgets, tool health, human escalation, disablement, and trace-to-eval feedback. Do not use for building agents, desig
Use this skill when creating, reviewing, or editing Agent Skills-format skills, or when implementing skill discovery and loading in an agent client. It covers directory structure, SKILL.md metadata, progressive disclosure, evals, and repository conventions. Do not use this skill
Design and operate an organization's AI governance system: define governance principles, operating models and decision rights, risk frameworks, lifecycle gates, and fairness, transparency, privacy, security, regulatory, and board-oversight controls. Use when standing up a governa
Use when deciding whether an AI-enabled workflow should be adopted, scaled, constrained, redesigned, or retired, and the decision must connect business outcomes, worker or user effects, quality guardrails, full operating cost, telemetry, uncertainty, and accountable governance. D
Use this skill to create subject-neutral images and videos in an analog occultism / industrial CRT noir aesthetic: near-monochrome archival technical atmosphere, severe low-key lighting, tactile signal degradation, industrial geometry, and quiet unresolved tension. Use for portra
Convert Word (.doc/.docx/.docm), PowerPoint (.ppt/.pps/.pot/.pptx/.pptm/.ppsx/.ppsm), Excel (.xls/.xlsx/.xlsm/.xlsb), OpenDocument (.odt/.ods/.odp), RTF, EPUB, CSV, and PDF documents to clean GitHub-Flavored Markdown locally with the Any Doc CLI (npx -y @firecrawl/anydoc@0.2.4):
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.
/status
Status
hydra - Show detailed status of one or all worktrees
/watch
Watch
hydra - Live monitoring of background agents with status table
/list
List
import - List all cached documentation
/search
Search
import - Search within cached documentation
/update
Update
import - Update cached documentation
/url-or-path
Url or path
import - Fetch URL (via Playwright if blocked) or copy local path to docs/
/highscore
limit:highscore
limit - Display all highscores for all plans
/local-cleanup
marketplace:local-cleanup
marketplace - Restore original plugin version from backup
/local-copy
marketplace:local-copy
marketplace - Install local plugin version for testing (backup original)
/CLAUDE
CLAUDE
<claude-mem-context>
/skmtc-retro-review
Skmtc retro review
Aggregate SKMTC friction log entries into a review — cluster patterns, classify interventions, calculate convergence metrics, produce an action plan
/CLAUDE
CLAUDE
<claude-mem-context>
/skmtc-retro
Skmtc retro
Run a SKMTC retrospective on the current session — capture friction and wins to the friction log
/stats
Stats
CoalMine measurement dashboard — canary activity this session + rule-freshness status across the project's rules home
/update
Update
CoalMine self-update — check for a newer CoalMine version and offer to apply it, or set how updates are handled
/stats
Stats
CoalMine measurement dashboard — canary activity this session + rule-freshness status across the project's rules home
/update
Update
CoalMine self-update — check for a newer CoalMine version and offer to apply it, or set how updates are handled
/stats
Stats
CoalWash stats — current band, certain-fat reading, both break-evens, Memory-BMI (informational), pending-ask state, and the last run's trace for this project
/update
Update
CoalWash self-update — check for a newer version and offer to apply it, or set how updates are handled.
/stats
Stats
CoalWash stats — current band, certain-fat reading, both break-evens, Memory-BMI (informational), pending-ask state, and the last run's trace for this project
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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