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moocomooco 搜索

Agent Skill

mooco 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

269

周安装

11

GitHub Stars

37

下载量

86
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mooco(mooco 搜索)
来源仓库:https://github.com/simhacker/moollm
仓库路径:skills/mooco
安装命令:
npx skills add https://github.com/simhacker/moollm --skill mooco
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/simhacker/moollm --skill mooco

简介

mooco 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于根据关键词、任务场景或来源线索进行信息检索的场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • 建议结合原始 README 和仓库内容进一步核验具体功能和使用方法。

SKILL.md

MOOCO

*"Deterministic orchestration around creative computation."*

This skill documents the interface surface MOOCO should provide so MOOLLM skills can run safely and predictably. It focuses on ideas, features, and interoperability, not code details.

Core Expectations

  • Context control: explicit working-set paging and hot/cold management
  • Safe tool boundaries: sister scripts run in constrained envelopes
  • Event emission: structured events for deterministic steps
  • Skill containment: clear input/output channels and limits
  • Streaming-first: async, SSE-native sessions with clean reconnection
  • Shared-core mindset: open components align with private extensions

Tooling Model (Sister Scripts as Components)

MOOCO should treat skills + sister scripts as dynamic tool components:

  • Each skill can declare multiple sister-script APIs
  • Each API exposes typed parameters and safe defaults
  • Invocation is mediated by a component model (COM/OLE/IDispatch analogy)
  • MCP is a supported transport layer, but not the only surface
  • Skills are finer‑grained and more composable; they remain the primary focus
  • The orchestrator can choose between synchronous calls and async lanes
  • Skill calls can be encapsulated in one bubble/LLM iteration, or composed across many skill calls and character turns in a single epoch, or anywhere in between

Execution Modes

  • Fire‑and‑forget: emit a task and continue
  • Wait‑for‑response: block on a specific result or a set (fork/join)
  • Parallel lanes: multiple skill calls in flight
  • Dependency graphs: schedule based on explicit edges

Skill Safety and Mutability

  • Skills may propose edits to other skills, but mutation is gated
  • Skills can be locked read‑only by policy
  • Deterministic steps should run before creative steps
  • Untrusted skills (including imported Anthropic skills) require review and hardening
  • Monitoring and audit trails are mandatory for elevated privileges
  • Upgrades should be reversible and traceable

Untrusted Skill Intake

  • Review: inspect source, inputs, outputs, and failure modes
  • Analyze: run skill‑snitch scans and adversarial checks
  • Bullet‑proof: tighten permissions, constrain inputs, add guardrails
  • Monitor: log usage, trace context, and watch for drift

Per‑Skill Storage

Each skill gets isolated scratch and persistent storage:

  • .moollm/skills/<skill-name>/scratch/ — ephemeral, local
  • .moollm/skills/<skill-name>/store/ — durable, user‑owned

This keeps experiments safe while preserving user‑authored state.

Integration and Uplift

  • Promote stable skills into CARD/README/K‑lines for discoverability
  • Cross‑link related skills in both directions
  • Keep portability: skills should remain usable outside MOOCO

Skill Compatibility

Skills should be able to declare:

  • deterministic steps (handled by orchestration)
  • creative steps (handled by LLM)
  • context needed to run safely
  • required event outputs for traceability

Design Direction

  • Prefer single-epoch simulation when possible
  • Use deterministic evaluation before LLM synthesis
  • Keep all state in files and event logs
  • Emit traceable provenance for every step
  • Favor portability across models and orchestrators

Inheritance Notes

  • MOOCO should treat working-set files as directives
  • Cursor treats them as advisory; the skill surface should adapt

Synergies

  • Speed of Light: single-epoch simulation, multi-agent multi-turn deliberation
  • Sister Scripts: safe, deterministic execution envelopes
  • Cursor-mirror: introspection and traceability
  • YAML Jazz: semantic schemas and human-readable state
  • Edgebox: probe → analyze → call as a practical PLL precedent

Architecture Snapshot

MOOCO favors a shared core: conversation schemas, streaming engine, provider abstraction, and tool execution that can be reused across orchestrators. This keeps skills portable and reduces lock-in to any one model or runtime.

Kilroy Dataflow Networks

MOOCO should support Kilroy‑style dataflow:

  • Events are messages, tools are nodes, skills are pipelines
  • Deterministic transforms compose before LLM synthesis
  • Small local models act as focused nodes in the graph
  • Visual programming is a first‑class view of the same network

GitHub as Microworld

GitHub can be treated as a composable microworld:

  • repositories are rooms
  • issues and pull requests are objects in play
  • labels and milestones are tags and states
  • cross‑repo links are portals
  • commit messages, PR descriptions, and issue discussions are high‑value narrative objects that preserve intent, prompts, context, and audit trails (thoughtful‑commitment catnip)

This makes multi‑repo composition a first‑class navigation problem.

Storage Layering

MOOCO favors layered storage:

  • PostgreSQL for canonical state
  • TimescaleDB for time‑series events
  • pgvector for semantic recall
  • SQLite for lightweight local mirrors
  • Raw text corpora for evidence: Cursor chat history, historic docs, PDFs, citations, Wikipedia pages, web pages, discussion threads, and referenced papers

Layering keeps state durable, analyzable, and portable.

Future Work

  • Define a standard event payload for skill execution
  • Specify a context budget contract for skills
  • Document containment tiers for sister scripts

See Also

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.67%
按下载量换算32

Claude

29.1%
按下载量换算25

Cursor

20.52%
按下载量换算18

Gemini CLI

9.19%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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