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llm-boostLLM boost 搜索

Agent Skill

llm-boost 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

490

周安装

20

GitHub Stars

1

下载量

158
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:llm-boost(LLM boost 搜索)
来源仓库:https://github.com/ven0m0/claude-config
仓库路径:skills/llm-boost
安装命令:
npx skills add https://github.com/ven0m0/claude-config --skill llm-boost
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/ven0m0/claude-config --skill llm-boost

简介

llm-boost 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 它能辅助梳理任务线索、来源信息和候选方案,帮助 Agent 快速缩小范围。
  • 通过 npx skills add 命令从指定仓库安装,具体用法请参考原始 README。
  • 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。
  • llm-boost 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

LLM Boost Skill

Optimize all LLM-facing content: documentation, skills, prompts, and parameters.

Quick Reference

AreaKey MetricTarget
c7scoreQuestion-Snippet Match80% weight
SkillsSKILL.md size<=500 lines
LLM TuningTask-appropriate settingsSee tuning table

Documentation Optimization (c7score)

  1. Analyze: Read README.md, docs/*.md
  2. Generate questions: Create 15-20 "How do I..." questions covering setup, auth, basic usage, errors, advanced features, integrations
  3. Map questions to snippets: Mark complete, partial, or missing (prioritize missing)
  4. Optimize by priority:
PriorityWeightAction
P1: Question coverage80%Add complete code for unanswered questions
P2: Remove duplicates5%Consolidate similar snippets
P3: Fix formatting5%Proper language tags, TITLE/DESCRIPTION/CODE
P4: Remove metadata5%Strip licensing, directory trees, citations
P5: Enhance init5%Combine import-only with usage examples
  1. Validate each snippet: runs standalone, answers specific question, proper format, includes imports
  2. Score before vs after across all 5 metrics

Snippet Transformation Patterns

  • API ref to usage example: Replace method signatures with complete working code including imports, setup, and expected output
  • Import-only to complete setup: Combine from lib import X with actual usage showing real output
  • Multiple fragments to one comprehensive: Merge related 1-2 line snippets into one complete workflow
  • Remove metadata: Strip directory trees, license text, BibTeX citations entirely

For detailed patterns: references/optimization_patterns.md


llms.txt Generation

<format_rules>

  • H1 title required, H2 sections only (no H3+)
  • Full URLs with protocol, prefer.md files
  • - [Title](url): description link format
  • "Optional" section = skippable for shorter context
  • No code blocks, images, or complex formatting
  • Place at repo root as /llms.txt </format_rules>
Project TypeMust HaveShould Have
LibraryDocumentation, API Reference, ExamplesGetting Started, Development
CLI ToolGetting Started, Commands, ExamplesConfiguration, Development
FrameworkDocumentation, Guides, API Reference, ExamplesIntegrations

For templates: examples/sample_llmstxt.md


Skill Optimization

500-Line Rule

Keep in SKILL.md: purpose, quick start, critical practices, brief examples (5-10 lines), cross-references. Move to reference files: API docs, extensive examples (>20 lines), troubleshooting, pattern libraries, schemas.

Optimization Modes

ModeSizeAction
Light<3K tokensTighten wording, add YAML if missing
Standard3K-6KConsolidate, tables over prose, one example
Aggressive6K-10KTable everything, strip filler
Split>=10KPropose 3-4 files + index

YAML Frontmatter

Description field (max 1024 chars) must include: what the skill does, when to use it, key technologies, action verbs. Write in third person.

Progressive Disclosure Pattern

## Topic Overview
Brief explanation (2-3 sentences).

**Quick Example:**
(5-10 line code block)

**For detailed docs**: [REFERENCE.md](REFERENCE.md#topic)

XML Tag Structuring

<design_principles>

PrincipleGuideline
Semantic namingTag names describe content: <contract>, <rubric>
ConsistencySame tag names throughout; reference by name in instructions
Nesting<outer><inner></inner></outer> for hierarchy
No canonical tagsNo "best" tags - name for your use case
Combine techniquesPair with CoT (<thinking>/<answer>) and multishot (<examples>)

</design_principles>

Core Patterns

Multi-document: <documents><document index="1"><source>...</source><content>...</content></document></documents>

Structured evaluation: <rubric> + <submission> -> <evaluation><score> + <feedback>

CoT separation: <thinking> for reasoning, <answer> for final output

Multishot examples: <examples><example><input>...</input><output>...</output></example></examples>

Guard rails: <instructions><task>...</task><formatting>...</formatting><constraints>...</constraints></instructions>

Output Extraction

import re

def extract_tag(text, tag):
    match = re.search(f'<{tag}>(.*?)</{tag}>', text, re.DOTALL)
    return match.group(1).strip() if match else None

For comprehensive tag catalog: references/xml_tags.md


LLM Parameter Tuning

Taskmax_tokenstemperaturetop_pRationale
Theorem proving40960.60.95CoT needs space; higher temp explores tactics
Code generation20480.2-0.4-Deterministic preferred
Creative/exploration40960.8-1.0-Maximum diversity
Classification2560.0-0.1-Consistency over creativity
Summarization10240.3-Faithful to source

CLAUDE.md Audit Checklist

CheckHow
Tech stack claims`Read("package.json\Cargo.toml")`
File path referencesGlob("claimed/path")
Command referencesGrep("script", glob="package.json")
Testing frameworkGlob("**/*.test.*")
Linting config`Glob("**/biome.json\**/.eslintrc*")`
Line countwc -l CLAUDE.md - target <300
No code duplicationUses file:line pointers
WHAT/WHY/HOW structureManual review

Reference Materials

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

37.74%
按下载量换算60

Claude

28.88%
按下载量换算46

Cursor

18.49%
按下载量换算29

Gemini CLI

9.23%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。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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