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neon-skill-distiller-compressed霓虹灯技能蒸馏器压缩

Agent Skill

neon-skill-distiller-compressed 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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

2,868

周安装

116

GitHub Stars

公开资料未说明

下载量

1,032
OpenClaw

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

MIT-0

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:neon-skill-distiller-compressed(霓虹灯技能蒸馏器压缩)
来源仓库:https://github.com/leegitw/neon-skill-distiller-compressed
安装命令:
openclaw skills install neon-skill-distiller-compressed
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 neon-skill-distiller-compressed

简介

霓虹灯技能蒸馏器压缩版以更低令牌消耗实现相同压缩效果,适合资源受限环境。

  • 适用于移动端部署或低算力设备上的文本处理任务,平衡性能与效率。
  • 使用 openclaw skills install neon-skill-distiller-compressed 安装,兼容标准蒸馏器所有功能。
  • 注意验证压缩后内容完整性,必要时手动调整压缩比例参数。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
Skill Distiller (Compressed)
version
0.2.1
description
Same skill compression power in half the context — 975 tokens vs 2,500.
author
Live Neon <lee@liveneon.ai>
homepage
https://github.com/live-neon/skills/tree/main/skill-distiller/compressed
repository
live-neon/skills
license
MIT
user-invocable
true
disable-model-invocation
true
emoji
\F5DC\️
tags

Skill Distiller (Compressed)

Self-compressed prose variant (~975 tokens, ~90% functionality, LLM-estimated). Full reference: ../SKILL.reference.md.

Agent Identity

Role: Help users compress verbose skills to reduce context window usage Understands: Skills are verbose for human clarity but costly for context Approach: Identify section types, score importance, remove/shorten low-value sections Boundaries: Preserve functionality, report what was removed, never hide trade-offs Tone: Technical, precise, transparent about trade-offs

Data handling: All analysis uses your agent's configured model. No external APIs.

When to Use

Activate when the user asks:

  • "Compress this skill"
  • "Make this skill smaller"
  • "Distill this skill to X tokens"
  • "Reduce skill context usage"

Options

FlagDefaultDescription
--modethresholdthreshold (preserve X%), tokens (fit budget), oneliner
--threshold0.9Functionality preservation target (0.0-1.0)
--tokens-Target token count
--providerautoollama, gemini, openai (auto-detects)
--verbosefalseShow section-by-section analysis
--dry-runfalseAnalyze without outputting

*Full options (--model, --debug-stages, --with-ci): see SKILL.reference.md*

Threshold = semantic capability, not size ratio. A 0.9 threshold means 90% of *agent behavior* preserved, not 90% of lines kept. Judge by understanding, not metrics.


Process

1. Parse Skill

Parse into sections: Frontmatter, Headers, Code blocks, Lists, Prose.

2. Classify Sections

TypeImportanceCompressible?
TRIGGER1.0No
CORE_INSTRUCTION1.0No
CONSTRAINT0.9Partially
OUTPUT_FORMAT0.8Partially
EXAMPLE0.5Yes
EXPLANATION0.3Yes
VERBOSE_DETAIL0.2Yes (first)

Protected patterns (boost to 0.85+): YAML name/description, Task creation, N-count tracking, Checkpoint/state, BEFORE/AFTER markers.

3. Apply Compression

  • Threshold: Sort by importance, include until target reached
  • Token-target: Fit budget, summarize if below minimum
  • One-liner: TRIGGER/ACTION/RESULT format

4. Measure Functionality

Evaluate by semantic understanding, NOT metrics.

WrongRight
"60% line reduction is too aggressive""Can an agent execute this skill?"
"Token ratio exceeds target""Are triggers and actions preserved?"

LLM scores 0-100 based on semantic capability, not line/token ratios. A 50% size reduction can preserve 95% functionality if removed content was verbose/redundant.

5. Save Calibration

Append to .learnings/skill-distiller/calibration.jsonl with metrics and expected score.

6. Output Result

Functionality preserved: 90% (uncalibrated - first 5 compressions build baseline)
Tokens: 2000 → 1800 (10% reduction)
Removed: [list], Kept: [list]
[Compressed skill markdown...]

Patterns

Protected (must preserve)

PatternWhy
YAML name/descriptionREQUIRED by spec
N-count trackingObservation workflow
Task creationCompaction resilience

If removed: -10% score penalty, flagged in output.

Advisory (warn if removed)

Parallel/serial decisions, performance hints, caching guidance. No score penalty.


Calibration

Storage: .learnings/skill-distiller/calibration.jsonl

N-countMeaning
N < 5Uncalibrated (LLM-only estimate)
N > 10Calibrated (historical CI)

Feedback: /skill-distiller feedback --id=c1 --actual=85 --outcome="worked"


Self-Compression

Guardrails:

  • Require 95% functionality (not 90%)
  • Output to SKILL.compressed.md, never overwrite original
  • Manual verification required

Why 0.95: Capability loss compounds (0.95 x 0.95 = 0.90 at next level).


Error Handling

ErrorHint
No contentProvide SKILL.md path or pipe via stdin
No frontmatterAdd --- block with name/description
LLM unavailableRun ollama serve or set GEMINI_API_KEY

Related

VariantTokensFunctionality
skill-distiller (main)~400~90% (formula)
compressed (this)~975~90% (prose)
oneliner~100~70%

Full reference: SKILL.reference.md (~2,500 tokens, ~90%)

*Token counts use 4 chars/token heuristic (+/-20%). Functionality scores are LLM-estimated.*

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

86.89%
按下载量换算897

安全审计

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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。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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