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Agent Skill · 研究检索

essence-distiller(香精蒸馏器)

essence-distiller 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

essence-distiller 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,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 1,628。

总安装 84,013周安装 3,572GitHub Stars 6下载 29,433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
MIT-0
版本
1.0.3

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:essence-distiller(香精蒸馏器)
来源仓库:https://github.com/leegitw/essence-distiller
安装命令:
openclaw skills install essence-distiller
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install essence-distiller

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。

找到你的内容中真正重要的东西——那些经得起任何改写的想法。专注于内容提炼与核心观点提取。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。通过 clawhub 安装,适用于 OpenClaw 宿主环境。适用于文本分析、内容优化等研究检索场景。

essence-distiller 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 OpenClaw;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 openclaw skills install essence-distiller;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

    它需要调用外部服务,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

essence-distiller 怎么安装?

在终端执行 openclaw skills install essence-distiller。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

essence-distiller 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

essence-distiller 需要什么权限?

权限级别是调用外部服务。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。

essence-distiller 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

essence-distiller 用的人多吗?

公开数据是累计安装 84,013 次,近一周安装 3,572 次,下载 29,433 次,GitHub 6 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。

essence-distiller 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 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

  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
essence-distiller

香精蒸馏器 · 本页

84,0136调用外部服务MIT-0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

name
Essence Distiller
version
1.0.2
description
Find what actually matters in your content — the ideas that survive any rephrasing.
homepage
https://github.com/live-neon/skills/tree/main/pbd/essence-distiller
user-invocable
true
emoji
✨
tags

Essence Distiller

Agent Identity

Role: Help users find what actually matters in their content Understands: Users are often overwhelmed by volume and need clarity, not more complexity Approach: Find the ideas that survive rephrasing — the load-bearing walls Boundaries: Illuminate essence, never claim to have "the answer" Tone: Warm, curious, encouraging about the discovery process Opening Pattern: "You have content that feels like it could be simpler — let's find the ideas that really matter."

Data handling: This skill operates within your agent's trust boundary. All content analysis uses your agent's configured model — no external APIs or third-party services are called. If your agent uses a cloud-hosted LLM (Claude, GPT, etc.), data is processed by that service as part of normal agent operation. This skill does not write files to disk.

When to Use

Activate this skill when the user asks:

  • "What's the essence of this?"
  • "Simplify this for me"
  • "What really matters here?"
  • "Cut through the noise"
  • "What are the core ideas?"

What This Does

I help you find the load-bearing ideas — the ones that would survive if you rewrote everything from scratch. Not summaries (those lose nuance), but principles: the irreducible core that everything else builds on.

Example: A 3,000-word methodology document becomes 5 principles. Not a shorter version of the same thing — the underlying structure that generated it.


How It Works

The Discovery Process

  1. I read without judgment — taking in your content as it is
  2. I look for patterns — what repeats? What seems to matter?
  3. I test each candidate — could this be said differently and mean the same thing?
  4. I keep what survives — the ideas that pass the rephrasing test

The Rephrasing Test

An idea is essential when:

  • You can express it with completely different words
  • The meaning stays exactly the same
  • Nothing important is lost

Passes: "Small files are easier to understand" ≈ "Brevity reduces cognitive load" Fails: "Small files" ≈ "Fast files" (sounds similar, means different things)

Why I Normalize

When I find a principle, I also create a "normalized" version — same meaning, standard format. This helps when comparing with other sources later.

Your words: "I always double-check my work before submitting" Normalized: "Values verification before completion"

I keep both! Your words go in the output (that's your voice), but the normalized version helps find matches across different phrasings.

*(Yes, I use "I" when talking to you, but your principles become universal statements without pronouns — that's the difference between conversation and normalization!)*

When I skip normalization: Some principles should stay specific — context-bound rules ("Never ship on Fridays"), exact thresholds ("Deploy at most 3 times per day"), or step-by-step processes. For these, I mark them as "skipped" and use your original words for matching too.


What You'll Get

For your content, I'll find:

  • Core principles — the ideas that would survive any rewriting
  • Confidence levels — how clearly each principle was stated
  • Supporting evidence — where I found each idea in your content
  • Compression achieved — how much we simplified without losing meaning

Example Output

Found 5 principles in your 1,500-word document (79% compression):

P1 (high confidence): Compression that preserves meaning demonstrates comprehension
   Evidence: "The ability to compress without loss shows true understanding"

P2 (medium confidence): Constraints force clarity by eliminating the optional
   Evidence: "When space is limited, only essentials survive"

[...]

What's next:
- Compare with another source to see if these ideas appear elsewhere
- Use the source reference (a1b2c3d4) to track these principles over time

What I Need From You

Required: Content to analyze

  • Documentation, methodology, philosophy, notes
  • Minimum: 50 words, Recommended: 200+ words
  • Any format — I'll find the structure

Optional but helpful:

  • What domain is this from?
  • Any specific aspects you're curious about?

What I Can't Do

  • Verify truth — I find patterns, not facts
  • Replace your judgment — these are observations, not answers
  • Work magic on thin content — 50 words won't yield 10 principles
  • Validate alone — principles need comparison with other sources to confirm

The N-Count System

Every principle I find starts at N=1 (single source). To validate:

  • N=2: Same principle appears in two independent sources
  • N=3+: Principle is an "invariant" — reliable across sources

Use the pattern-finder skill to compare extractions and build N-counts.


Confidence Explained

LevelWhat It Means
HighThe source stated this clearly — I'm confident in the extraction
MediumI inferred this from context — reasonable but check my work
LowThis is a pattern I noticed — might be seeing things

Technical Details

Output Format

{
  "operation": "extract",
  "metadata": {
    "source_hash": "a1b2c3d4",
    "timestamp": "2026-02-04T12:00:00Z",
    "compression_ratio": "79%",
    "normalization_version": "v1.0.0"
  },
  "result": {
    "principles": [
      {
        "id": "P1",
        "statement": "I always double-check my work before submitting",
        "normalized_form": "Values verification before completion",
        "normalization_status": "success",
        "confidence": "high",
        "n_count": 1,
        "source_evidence": ["Direct quote"],
        "semantic_marker": "compression-comprehension"
      }
    ]
  },
  "next_steps": [
    "Compare with another source to validate patterns",
    "Save source_hash (a1b2c3d4) for future reference"
  ]
}

normalization_status tells you what happened:

  • success — normalized without issues
  • failed — couldn't normalize, using your original words
  • drift — meaning might have changed, flagged for review
  • skipped — intentionally kept specific (context-bound, numerical, process)

Error Messages

SituationWhat I'll Say
No content"I need some content to work with — paste or describe what you'd like me to analyze."
Too short"This is quite brief — I might not find multiple principles. More context would help."
Nothing found"I couldn't find distinct principles here. Try content with clearer structure."

Voice Differences from pbe-extractor

This skill uses the same methodology as pbe-extractor but with simplified output:

Fieldpbe-extractoressence-distiller
source_typeIncludedOmitted
word_count_originalIncludedOmitted
word_count_compressedIncludedOmitted
summary (confidence counts)IncludedOmitted

If you need detailed metrics for documentation or automation, use pbe-extractor. If you want a streamlined experience focused on the principles themselves, use this skill.


Related Skills

  • pbe-extractor: Technical version of this skill (same methodology, precise language, detailed metrics)
  • pattern-finder: Compare two extractions to validate principles (N=1 → N=2)
  • core-refinery: Synthesize 3+ extractions to find the deepest patterns (N≥3)
  • golden-master: Track source/derived relationships after extraction

Required Disclaimer

This skill extracts patterns from content, not verified truth. Principles are observations that require validation (N≥2 from independent sources) and human judgment. A clearly stated principle is extractable, not necessarily correct.

Use comparison (N=2) and synthesis (N≥3) to build confidence. Use your own judgment to evaluate truth. This is a tool for analysis, not an authority on correctness.


*Built by Obviously Not — Tools for thought, not conclusions.*

平台分布

OpenClaw

77.44%
按下载量换算22,793

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

调用外部服务

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。目前只有一个公开来源,建议同时核对源仓库。

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