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Agent Skill · 研究检索

protocol(protocol 搜索)

protocol 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。

protocol 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 66,424。

总安装 264周安装 11GitHub Stars 37下载 88核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:protocol(protocol 搜索)
来源仓库:https://github.com/simhacker/moollm
仓库路径:skills/protocol
安装命令:
npx skills add https://github.com/simhacker/moollm --skill protocol
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 protocol

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

protocol 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。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、维护状态,以及是否会触发联网、命令执行或文件读写操作。建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/protocol

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/simhacker/moollm --skill protocol;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

protocol 怎么安装?

在终端执行 npx skills add https://github.com/simhacker/moollm --skill protocol。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

protocol 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

protocol 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

protocol 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

protocol 用的人多吗?

公开数据是累计安装 264 次,近一周安装 11 次,下载 88 次,GitHub 37 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

protocol 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
protocol

protocol 搜索 · 本页

26437接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

PROTOCOL

"Protocol names ARE K-lines."

MOOLLM protocol symbols are Minsky's K-lines made concrete and greppable.


Protocol Names = K-Lines

A K-line (Minsky, *Society of Mind*) is a mental structure that, when activated, reactivates an entire constellation of knowledge, skills, and associations.

Protocol names are exactly this:

YAML-JAZZ         ← Activates: semantic comments, jazz interpretation, LLM-as-musician
POSTEL            ← Activates: charitable interpretation, robustness, collaboration
PLAY-LEARN-LIFT   ← Activates: exploration, pattern-finding, crystallization, sharing
COHERENCE-ENGINE  ← Activates: consistency, constraints, orchestration, referee, simulation, DM

Type the name → activate the tradition → invoke the behavior.

The UPPER-CASE-HYPHENATED format makes these K-lines findable.


Activation: scene and stance, not only a pointer

A K-line is not only a compact reference to a concept. In Minsky’s account, activating a K-line tends to re-instantiate a configuration that worked before: which sub-skills, tone, and moves to bring online. Henry Minsky put it in terms of a whole state of mind (or “scene”) bound to a name—closer to a re-enterable closure than to a bare pointer.

That matters in MOOLLM because:

  • Shared index — PROTOCOLS.yml, skills/INDEX.yml, and prior turns give the same symbol the same load-bearing role. Without that, a name is noise.
  • Embodied triggers — A photo, a warning sign, a vocal cue can act like a K-line if the culture around it is shared: they snap attention to a lived situation, not a map coordinate.

Linguistic analogy (not Minsky’s wording, but useful): In some languages, *am Bahnhof Zoo* vs bare *Bahnhof Zoo* shifts from bare reference toward situated, insider scene—the way *the 405* in Los Angeles is not “a highway” but that corridor of life. Same mechanism in spirit: name → thick situation, not only name → fact.

Optional cultural example: Nina Hagen’s *Berlin Zoo* (1982) is often read as performative activation—noise, animal theatre, West Berlin grit—not as zoology. The track works as a charged name for a subculture-tinted “zoo” of people and noise. A zoo sign about a lion spraying urine through bars is thematically on that wavelength: blunt, bodily, public—scene-first, not taxonomy-first.

Contrast: Frames (Minsky) describe the shape of a situation (slots, defaults). Drescher schemas (see schema-mechanism) describe learned causality (Context → Action → Result). Schemapedia (see schema, schemas/registry.yml) indexes interchange, causal, frames, activation, relational SQL/SQLite, and SoM—each family with its own delegate skills. Protocol symbols here describe which competence bundle to load. See knowledge-frames for the full map.


Naming Convention

RuleExampleWhy
ALL CAPSYAML-JAZZ not yaml-jazzStands out in prose
Hyphen-separatedPLAY-LEARN-LIFT not PLAY_LEARN_LIFTShell-friendly, readable
No colonsPOSTEL not WP:POSTELCleaner than Wikipedia
SemanticROBUST-FIRST not RFSelf-documenting

Why This Convention?

1. Grep-Friendly

# Find all uses of a protocol
grep -r "YAML-JAZZ" .

# Find all protocol definitions
grep -r "^[A-Z-]*:" PROTOCOLS.yml

# Find all mentions in docs
grep -rn "POSTEL\|PLAY-LEARN-LIFT" skills/

2. Vector-Search Friendly

The UPPER-CASE format creates distinct embeddings. When you search for "YAML-JAZZ", you get:

  • The protocol definition
  • Files that reference it
  • Examples that demonstrate it

Not general YAML documentation.

3. Human-Scannable

In prose, protocols pop:

When parsing user input, apply POSTEL (charitable interpretation). If the command is ambiguous, use YAML-JAZZ to infer intent.

You can't miss them.


Protocol Layers

Kernel Protocols (Low-Level)

Defined in kernel/, fundamental to operation:

ProtocolPurposeLocation
FILES-AS-STATEEverything is fileskernel/constitution-core.md
WHY-REQUIREDTool calls need reasonskernel/tool-calling-protocol.md
APPEND-ONLYNever modify logskernel/constitution-template.md
MINIMAL-DIFFChange only what's neededkernel/constitution-core.md

Skill Protocols (Mid-Level)

Defined in skills/, behavioral patterns:

ProtocolPurposeLocation
PLAY-LEARN-LIFTThe methodologyskills/play-learn-lift/
SOUL-CHATEverything speaksskills/soul-chat/
POSTELCharitable interpretationskills/postel/
YAML-JAZZComments carry meaningskills/yaml-jazz/

Ad-Hoc Protocols (Emergent)

Mentioned in conversations, examples, documents:

# In a soul-chat document
We should apply KITCHEN-RULES here — no running,
always clean up, respect the fridge contents.

# In an adventure room
This room follows GRUE-SAFE protocol — magically lit.

These don't need formal definitions. Naming them makes them real.


Where Protocols Live

Central Registry

PROTOCOLS.yml — The master list:

YAML-JAZZ:
  meaning: "Comments carry meaning. Data is interpreted."
  location: kernel/constitution-core.md §3
  invoke_when: "Writing or reading YAML with semantic intent"

Embedded in Files

Protocols can be defined inline where they're used:

# In a room's ROOM.yml
protocols:
  DARK-ROOM:
    meaning: "No light source = grue danger"
    applies_when: "player enters without lit lamp"

Referenced in Prose

Just mention them:

This adventure follows REINCARNATION protocol — death returns you to start, inventory preserved.

The name IS the definition until someone formalizes it.


Protocol Lifecycle

Mentioned → Documented → Formalized → Skill
    ↓            ↓            ↓          ↓
  "Let's       Entry in     Directory   Full
   try          PROTOCOLS    in          PROTOTYPE
   FOOD-CHAIN"  .yml         skills/     + templates

Most protocols stay informal. Only crystallize when needed.


K-REFs: File Pointers as K-Lines

A K-REF is a K-line that points to a specific location in a file with optional metadata:

/path/to/file.md:42-58 # section_type | preview text

K-REF Format

PATH[:LINE[-END]][#ANCHOR][?SEARCH] # TYPE [LABEL] SEVERITY - DESCRIPTION
  EXCERPT or MASKED_VALUE

Components:

  • PATH — Absolute file path (if no line number, refers to whole file)
  • LINE — Line number (1-based), optionally with column :LINE:COL. Omit for whole file.
  • END — Optional end line or column for ranges
  • #ANCHOR — Jump to heading/section (like URL fragments)
  • ?SEARCH — Find first match of pattern (like URL query)
  • TYPE — What was found (uuid, secret, user_prompt, tool_call, etc.)
  • LABEL — Optional redact label like [SSH_KEY]
  • SEVERITY — Icon: 🔴 critical, 🟠 high, 🟡 medium, 🔵 low, ℹ️ info
  • DESCRIPTION — Human-readable explanation
  • EXCERPT — Surrounding context or masked value

Location Specifiers

FormatMeaningExample
/path/fileWhole file/etc/config.yml
/path/file:42Line 42/src/main.py:42
/path/file:42-58Lines 42-58/src/main.py:42-58
/path/file:42:10-46Line 42, cols 10-46/src/main.py:42:10-46
/path/file#sectionAnchor/heading/docs/API.md#authentication
/path/file?patternSearch for pattern/src/main.py?def process_data
/path/file#section?patternSection + search/docs/API.md#errors?401

Anchor Types

# Markdown headings (slugified)
/README.md#installation
/SKILL.md#k-ref-format

# YAML keys (dot-path)
/config.yml#server.port
/CARD.yml#methods.K-REF

# Code symbols (function/class)
/main.py#def:process_data
/main.py#class:AuditRunner

# HTML-style id
/page.html#footer

Search Patterns

# Literal string (default)
/file.py?def main

# Regex (prefix with ~)
/file.py?~def\s+\w+\(

# First match of pattern
/transcript.txt?user:

# Combine with line context
/file.py?def main # function - Jump to main()

Examples

/path/to/config.yml # config - Whole file reference
/path/to/SKILL.md#k-refs # section - Jump to K-REFs section
/path/to/main.py?def audit # search - Find audit function
/path/transcript.txt:91:12-48 # uuid ℹ️
  fe18ce96-5200-4e15-acd3-190695de6295

/path/transcript.txt:7528:18-45 # private_key ([PRIVATE_KEY]) 🔴 - Private key header
  ********** ******* ********

/path/config.json:15 # password ([PASSWORD]) 🟠 - Database password
  ************

Images Work Too!

Cursor (and other orchestrators) can read absolute file paths directly — INCLUDING IMAGES!

/Users/me/Screenshots/error.png # screenshot - Analyze this error
/tmp/architecture-diagram.jpg # diagram - Explain this system
/path/to/chart.png # data - What trends do you see?

No line numbers for images (obviously). Just the absolute path.

This is powerful:

  • Point to any image on disk
  • Cursor reads and analyzes it
  • Works with screenshots, diagrams, charts, photos
  • Supports: jpeg, png, gif, webp

Security note: Cursor can read ANYWHERE on your disk, not just the workspace. Powerful, but be aware.

Sister Script Methodology

Problem: LLMs have limited context windows. Dumping entire files wastes tokens.

Solution: Sister scripts emit K-REFs (pointers) instead of full content:

# Sister script → K-REF → LLM flow
kref_flow:
  source: "cursor-mirror (sister script)"
  produces: "K-REFs (pointers)"
  consumer: "LLM reads only what it needs"
  1. Scan — Script searches large data (transcripts, databases)
  2. Emit — Output K-REFs pointing to relevant sections
  3. Read — LLM selectively reads only the referenced ranges

This is reference by pointer, not by value — parsimonious context.

K-REF Output Modes

ModeCommandUse Case
K-REF--emit-krefHuman/LLM navigation
REDACT--emit-redactPipe to masking tool
JSON--jsonProgrammatic processing

Example: cursor-mirror as Sister Script

# Emit K-REFs for secrets (LLM reads just the pointers)
cursor-mirror audit --patterns secrets --emit-kref

# Output:
# /path/file.txt:42:10-46 # openai_key ([OPENAI_KEY]) 🔴 - OpenAI API key
#   sk-proj-****...(48 chars)...****

# LLM can then selectively read context around line 42

Commands

CommandAction
PROTOCOL [name]Describe or invoke a protocol
PROTOCOLSList defined protocols
DEFINE-PROTOCOL [name]Add to PROTOCOLS.yml
GREP-PROTOCOL [name]Find all references
K-REF [path:line]Navigate to a K-REF location

Protocol Symbols

SymbolMeaning
PROTOCOLThis meta-protocol
K-LINEMinsky's concept — name activates tradition
K-REFK-line as file pointer: path:line + metadata
SISTER-SCRIPTTool emits K-REFs, LLM reads selectively
GREPPABLEEasy to find with text search
UPPER-CASEThe naming convention itself

Dovetails With


The Power of Naming

*"In the beginning was the Word."*

When you name something, you can:

  • Find it — grep, vector search, memory
  • Invoke it — type the name, activate the tradition
  • Compose it — combine named things into new things
  • Teach it — names are handles for concepts

MOOLLM protocols are handles on ideas. The UPPER-CASE convention makes them impossible to miss, trivial to find.


*"Name it, grep it, use it."*

平台分布

Codex

35.77%
按下载量换算31

Claude

29%
按下载量换算26

Cursor

15.78%
按下载量换算14

Gemini CLI

9.67%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。目前只有一个公开来源,建议同时核对源仓库。

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