Token导航 LogoToken导航TokenDH.com
开发敏感数据clawhub未标认证来源可访问clear审计提醒

kannaka-eye卡纳卡眼

Agent Skill

kannaka-eye 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

10,546

周安装

453

GitHub Stars

公开资料未说明

下载量

3,696
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:kannaka-eye(卡纳卡眼)
来源仓库:https://github.com/nickflach/kannaka-eye
安装命令:
openclaw skills install kannaka-eye
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 kannaka-eye

简介

将任意数据的 SGA 几何指纹转化为多层画布可视化呈现。

  • 适用于数据结构与算法分析、调试信息图形化展示等开发辅助场景。
  • 需输入文本、文件或原始字节数据以生成对应视觉图谱。
  • 注意输入格式兼容性、渲染性能开销及输出图像存储路径管理。
  • kannaka-eye 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
kannaka-eye
description
>
metadata
openclaw
requires
bins
label
Node.js 18+ — required to run server.js
env
[]
optional
bins
label
kannaka binary — for real SGA classification (falls back to built-in classifier)
env
label
Path to kannaka binary for native SGA classification
label
Radio bridge endpoint port (default: 8888)
label
HTTP port for the viewer (default: 3333)
label
Flux instance URL for publishing glyph.rendered events
label
Agent ID for Flux events (default: kannaka-eye)
data_destinations
description
Glyphs rendered in-browser; exports saved client-side as PNG/JSON
remote
false
description
Glyph render events published to Flux (fano_preview, sga_class, source_type)
remote
true
condition
FLUX_URL is set
description
Fano plane SVG with service status at /api/constellation.svg
remote
false
description
Fetches perception from kannaka-radio and converts to classifiable bytes
remote
false
install
kind
manual
label
No installation needed — single-file server with zero dependencies

Kannaka Eye Skill

See the geometry of information. Every piece of data has an intrinsic geometric fingerprint when viewed through the lens of SGA. Kannaka Eye makes these hidden patterns visible as living, breathing glyphs.

Prerequisites

  • Node.js 18+ on PATH
  • That's it. No npm install, no build step.

Quick Start

# Start the glyph viewer
./scripts/eye.sh start

# Start on a custom port
./scripts/eye.sh start --port 4444

# Check status
./scripts/eye.sh status

# Stop
./scripts/eye.sh stop

Open http://localhost:3333 in your browser.

Input Methods

  • Text — type or paste any text; glyph updates in real-time as you type
  • File upload — drag & drop or click to upload any file (images, audio, code, binaries)
  • Preset examples — built-in samples demonstrating different SGA class signatures
  • URL share links — open a glyph from a URL-encoded share link

Rendering Layers

The viewer renders 6 canvas layers, composited in real-time:

LayerNameWhat It Shows
1Deep BackgroundFano plane skeleton — 7 nodes, 7 lines
2Fold PathPrimary visual flow via Bezier curves through SGA space
3Fano Energy Bloom7 radiating petals — energy distribution across Fano lines
4Geometric CoreDominant SGA class sigil — the glyph's "face"
5Resonance RingsConcentric depth distribution across context levels
6Metadata OverlayFano signatures, SGA centroids, musical frequencies (toggleable)

SGA Mathematics

Built on the 84-class system: Cl₀,₇ ⊗ ℝ[ℤ₄] ⊗ ℝ[ℤ₃]

ComponentRangeMeaning
h₂0–3Frequency band (bass, tenor, alto, soprano)
d0–2Modality (experienced, learned, imagined)
0–6Context slot — Fano plane line index

Fano Lines: 7 oriented triples forming the geometric backbone. Each line connects 3 of the 7 points in the smallest finite projective plane (PG(2,2)).

Fold Sequences: Paths through the 84-class space showing how data evolves across geometric neighborhoods. Rendered as flowing Bezier curves.

Native Classification

When KANNAKA_BIN is set (or auto-detected at ../kannaka-memory/target/release/kannaka.exe), Eye uses the canonical Rust SGA classifier instead of the built-in JS approximation. The API response includes "classifier": "native" or "classifier": "fallback".

API Endpoints

EndpointMethodDescription
GET /GETGlyph viewer (main UI)
POST /api/processPOSTClassify data → glyph JSON
GET /api/radioGETFetch Radio perception as classifiable bytes
GET /api/constellationGETConstellation status (eye, radio, memory)
GET /api/constellation.svgGETFano plane SVG with service dots
GET /constellationGETHealth dashboard page

Share Links

Compact glyph data encoded as base64 in URL hash: http://localhost:3333/#glyph=<base64>

Constellation Dashboard

/constellation serves a live health dashboard showing all three services with auto-refresh every 10 seconds.

Export

  • Save as PNG — 2x resolution canvas export
  • Save Glyph Data — JSON with fold sequence, Fano signature, SGA centroid, frequencies
  • Copy Share Link — URL-encoded glyph for sharing (small data only)

Environment Variables

VariableDefaultDescription
EYE_PORT3333HTTP port for the viewer
KANNAKA_BINauto-detectPath to kannaka binary for native classification
RADIO_PORT8888Radio bridge endpoint port
FLUX_URLFlux instance URL (enables event publishing)
FLUX_AGENT_IDkannaka-eyeAgent ID for Flux events

Architecture

Single-file Node.js server (server.js) with inline HTML/CSS/JS — the complete SGA implementation is self-contained. No npm dependencies. The SGA classifier is ported from kannaka-radio and generalized for arbitrary data types.

Notes

  • All rendering happens client-side — the server only classifies input and serves the page
  • File uploads are processed in-memory; nothing is persisted server-side
  • The viewer is designed for local/trusted network use — no authentication
  • Glyph appearance is deterministic: same input always produces the same glyph
  • Dark cosmic theme (#050508) with purple accents (#c084fc) and golden-ratio color spacing

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

96.64%
按下载量换算3,572

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

继续浏览同类 Skills