← 返回 Skills 市场
leegitw

Essence Distiller

作者 Lee Brown · GitHub ↗ · v1.0.3 · MIT-0
cross-platform ✓ 安全检测通过
2650
总下载
6
收藏
10
当前安装
4
版本数
在 OpenClaw 中安装
/install essence-distiller
功能描述
Find what actually matters in your content — the ideas that survive any rephrasing.
使用说明 (SKILL.md)

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

Level What It Means
High The source stated this clearly — I'm confident in the extraction
Medium I inferred this from context — reasonable but check my work
Low This 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

Situation What 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:

Field pbe-extractor essence-distiller
source_type Included Omitted
word_count_original Included Omitted
word_count_compressed Included Omitted
summary (confidence counts) Included Omitted

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.

安全使用建议
This skill appears internally consistent and low-risk because it is instruction-only and asks for no credentials or installs. Before using it, be mindful that any content you submit will be processed by whichever model your agent is configured to use — if that model runs in the cloud (GPT, Claude, etc.), your data will be transmitted to that provider under their terms. Avoid sending highly sensitive or regulated data unless you confirm the model and agent's privacy settings. Also review outputs (normalizations and inferred principles) before acting on them; the skill explicitly finds patterns and does not verify factual correctness.
功能分析
Type: OpenClaw Skill Name: essence-distiller Version: 1.0.3 The 'Essence Distiller' skill is a text analysis tool designed to extract core principles and patterns from user-provided content. The SKILL.md file contains instructions for the AI agent to perform summarization and normalization without any evidence of malicious intent, data exfiltration, or unauthorized execution. It explicitly states that it operates within the agent's trust boundary and does not use external APIs or write to disk.
能力评估
Purpose & Capability
Name/description align with the SKILL.md: the skill describes extracting core ideas and normalization. It requests no binaries, env vars, or config paths, which is proportionate to a text-analysis/summarization helper.
Instruction Scope
SKILL.md instructs the agent to analyze provided content using the agent's configured model and explicitly states it does not call external third-party services itself or write files to disk. This is consistent, but the skill relies on whatever model the agent is configured to use — if that model is cloud-hosted, user data will be processed by that external provider as part of normal agent operation. Also the skill refers to a separate 'pattern-finder' skill for cross-source validation; that external interaction is optional but worth noting.
Install Mechanism
No install spec and no code files (instruction-only). This minimizes disk writes and reduces supply-chain risk.
Credentials
No required environment variables, credentials, or config paths are declared. The SKILL.md doesn't instruct access to unrelated secrets or files.
Persistence & Privilege
always is false and the skill is user-invocable. It does not request persistent presence or modify other skills/configurations.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install essence-distiller
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /essence-distiller 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.3
No user-facing changes in this version. - Version incremented from 1.0.2 to 1.0.3 with no changes to files or documentation. - No new features, fixes, or documentation updates detected.
v1.0.2
Essence Distiller 1.0.2 changelog - Added version metadata to SKILL.md. - Updated `homepage` link to new GitHub repository location. - Expanded and updated tags for broader discoverability (e.g., summarization, distillation, tldr, analysis). - Added a section clarifying data handling and privacy—specifies no content is sent to third parties and how agent models are used. - No code or core logic changes; documentation improvements and metadata updates only.
v1.0.1
Migrated to public GitHub repo, updated homepage URLs
v1.0.0
- Initial release of Essence Distiller skill. - Extracts core principles from user-provided content, focusing on ideas that survive any rephrasing. - Simplified output compared to related skills, omitting detailed metrics for a more streamlined experience. - Provides confidence levels, supporting evidence, and compression achieved for each extracted principle. - Includes error handling with clear, user-friendly guidance. - Designed for clarity, not authority—principles require validation and user judgment.
元数据
Slug essence-distiller
版本 1.0.3
许可证 MIT-0
累计安装 10
当前安装数 10
历史版本数 4
常见问题

Essence Distiller 是什么?

Find what actually matters in your content — the ideas that survive any rephrasing. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 2650 次。

如何安装 Essence Distiller?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install essence-distiller」即可一键安装,无需额外配置。

Essence Distiller 是免费的吗?

是的,Essence Distiller 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Essence Distiller 支持哪些平台?

Essence Distiller 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Essence Distiller?

由 Lee Brown(@leegitw)开发并维护,当前版本 v1.0.3。

💬 留言讨论