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strangers-eye陌生人的眼睛

Agent Skill

strangers-eye 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

353

周安装

15

GitHub Stars

公开资料未说明

下载量

124
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

MIT

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:strangers-eye(陌生人的眼睛)
来源仓库:https://github.com/pentaxis93/aiandi
仓库路径:skills/strangers-eye
安装命令:
npx skills add pentaxis93/aiandi --skill "strangers-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 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。

AgentSkills.tonpx skills
npx skills add pentaxis93/aiandi --skill "strangers-eye"

简介

strangers-eye 用于查找、检索和筛选相关信息,适合在多平台环境中快速定位内容。

  • 支持基于关键词或任务场景进行信息组织与筛选。
  • 通过 npx 命令从 GitHub 安装,建议查阅原始文档了解细节。
  • 使用前需确认权限、维护状态及是否触发联网或命令执行。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
strangers-eye
description
Multi-persona documentation review that catches quality failures AND signal mismatches. Produces prioritized edits, not assessment. Quality before signals.

Strangers-Eye: Multi-Persona Documentation Review

See what strangers see. Catch what makes them leave.

When to Use This Skill

  • Before publishing documentation (README, guides, landing pages)
  • When documentation "feels off" but you can't articulate why
  • After major rewrites to verify clarity
  • When documentation serves multiple audiences with different intended relationships

The Core Insight

Documentation fails in two distinct ways:

  1. Quality failures - Readers leave because the writing is incoherent, confusing, or trust-breaking. They never receive any signal at all.
  1. Signal mismatches - Readers receive the wrong relationship signal (welcomed when they should be warned, redirected when they should be welcomed).

Quality failures are MORE urgent. Signal calibration on a broken foundation is meaningless.

The Three Relationships

Every documentation has an intended relationship with each audience segment:

RelationshipIntentSuccess Looks Like
WELCOME"This is for you, come in"Reader feels invited, sees value, wants to engage
WARN"This might not be for you, proceed with awareness"Reader understands the tradeoff, can make informed choice
REDIRECT"This isn't for you, here's where you should go"Reader quickly knows this isn't their path

Critical: A negative reaction from a REDIRECT persona isn't a problem. It's success - the signal worked. Only signal MISMATCHES require edits.


The Five-Phase Process

Phase 1: Intent Mapping

Before any personas exist, establish intended relationships.

Agent action: Infer from the documentation what relationship is intended with each audience segment. Present mapping for user approval.

Output format:

Intent Mapping (Proposed)

| Audience Segment | Intended Relationship | Rationale |
|------------------|----------------------|-----------|
| [Segment 1]      | WELCOME / WARN / REDIRECT | [Why] |
| [Segment 2]      | WELCOME / WARN / REDIRECT | [Why] |
| ...              | ...                  | ...       |

Does this capture your intent? (User can adjust before proceeding)

User gate: Do not proceed until user approves or adjusts the mapping.


Phase 2: Dimensional Analysis

Determine which dimensions are relevant for THIS documentation.

Possible dimensions (not exhaustive - emerge from material):

  • Technical literacy (beginner → expert)
  • Philosophical openness (resistant → seeking)
  • Role (evaluator → end-user → contributor)
  • Arrival context (stumbled upon → intentionally sought)
  • Domain expertise (specific to the field)
  • Time investment (quick scan → deep read)

Agent action: Define the dimensional space that meaningfully differentiates how readers will experience this documentation.

Output format:

Dimensional Space

1. [Dimension name]: [Low end] → [High end]
   Why relevant: [Brief rationale]

2. [Dimension name]: [Low end] → [High end]
   Why relevant: [Brief rationale]

...

No user gate. Agent proceeds after defining dimensions.


Phase 3: Persona Construction

Construct exactly the personas needed to cover the dimensional space and test all intended signal paths.

Each persona includes:

  • Name (memorable, descriptive)
  • Dimensional coordinates (position on each axis)
  • Intended relationship (from Phase 1 mapping)
  • Quality lens (what would make them leave due to confusion?)
  • Signal lens (what relationship would they perceive?)

Output format:

Constructed Personas

1. [Name] - [Brief archetype]
   Coordinates: [Dim1: value], [Dim2: value], ...
   Intended signal: [WELCOME/WARN/REDIRECT]
   Quality vulnerabilities: [What confuses this persona?]
   Signal sensitivities: [What signals resonate or repel?]

2. ...

No user gate. Agent proceeds after constructing personas.


Phase 4: Independent Reviews

Each persona reviews the documentation. No cross-contamination between reviews.

Each review assesses TWO dimensions:

A. Quality Assessment (Always first)

Questions the persona asks:

  • Does the opening make sense? Would I keep reading?
  • Are sentences clear and logical?
  • Do I trust this author's competence?
  • Is the value proposition communicated?
  • Are there passages that simply don't work?

Quality failures are flagged regardless of intended signal. Incoherent writing fails for ALL audiences.

A persona can report: "I would leave at line X because [quality reason]" - this terminates their review. They never got far enough for signal assessment.

B. Signal Assessment (Only if quality permits)

Questions the persona asks:

  • What signal did I receive? (WELCOMED / WARNED / REDIRECTED / CONFUSED)
  • Does this match the intended signal for my segment?
  • If mismatch: what specific content caused the wrong signal?

Working signals generate no edits. A REDIRECT persona who feels redirected is success, not a problem.

Output format (per persona):

Review: [Persona Name]

QUALITY ASSESSMENT:
- Opening: [Clear/Confusing] - [Specifics if problematic]
- Trust: [Intact/Damaged] - [Specifics if problematic]  
- Clarity: [Passages that don't work, if any]
- Would continue reading: [Yes/No at line X]

[If quality permits continuing:]
SIGNAL ASSESSMENT:
- Signal received: [WELCOMED/WARNED/REDIRECTED/CONFUSED]
- Signal intended: [From Phase 1]
- Match: [Yes/No]
- [If mismatch: specific content that sent wrong signal]

No user gate. Agent completes all persona reviews.


Phase 5: Synthesis into Edits

Consolidate findings into prioritized edits.

Priority ordering:

  1. QUALITY FAILURES (P1) - Incoherent, confusing, trust-breaking content

- These block everything. Fix first. - Flagged regardless of intended signal category.

  1. SIGNAL MISMATCHES (P2) - Wrong relationship communicated

- Only relevant once quality is sound. - Working redirects generate NO edits.

Output format:

Suggested Edits (Prioritized)

## Priority 1: Quality Failures

### Edit 1.1: [Location/Line]
Problem: [What fails]
Personas affected: [Who caught this]
Suggested fix: [Concrete recommendation]

### Edit 1.2: ...

## Priority 2: Signal Mismatches

### Edit 2.1: [Location/Line]
Problem: [Signal sent vs intended]
Personas affected: [Who experienced mismatch]
Suggested fix: [Concrete recommendation]

### Edit 2.2: ...

---

Summary: [X] quality edits, [Y] signal edits
Document assessment: [Poor/Decent/Excellent]

Output volume scales to document quality:

  • Poor document: many edits (quality + signal)
  • Decent document: few edits (signal only)
  • Excellent document: "No changes recommended"

Example Invocation

/strangers-eye README.md

Phase 1: Agent presents intent mapping, user approves Phase 2-4: Agent executes independently Phase 5: User receives prioritized edits


Critical Distinctions

Quality vs Signal

AspectQuality FailureSignal Mismatch
What happensReader leaves confusedReader receives wrong relationship
PriorityP1 (fix first)P2 (fix after quality)
AffectsAll audiencesSpecific audience segments
ExampleGrammatically ambiguous taglineTechnical jargon alienating beginners

Working Redirect vs Failed Redirect

OutcomeIs This a Problem?
REDIRECT persona feels unwelcomeNo - signal worked
REDIRECT persona feels confusedYes - quality failure
WELCOME persona feels redirectedYes - signal mismatch

Anti-Patterns

Quality-Blind Signal Assessment

Assessing whether audiences feel welcomed/warned/redirected without first checking if they'd even finish reading. Fix quality first.

Treating All Negative Reactions as Problems

A REDIRECT persona having a negative reaction is SUCCESS. The signal worked. Only mismatches need fixing.

Assessment Narration in Output

Output is EDITS, not assessment tables. The synthesis phase produces actionable recommendations, not persona monologues.

Predefined Persona Libraries

Personas are bespoke to each documentation. The dimensional space emerges from the material.


Integration Notes

Complements aiandi-docs skill: That skill provides writing guidelines. This skill provides review methodology. Use strangers-eye to verify documentation meets aiandi-docs standards.

Minimal orchestration required: Only Phase 1 (intent mapping) requires user input. Phases 2-4 execute independently. Phase 5 delivers results.


The Compassion Frame (Invisible in Output)

The goal isn't documentation that pleases everyone. It's documentation that SERVES everyone - which sometimes means clearly signaling "this isn't for you" so people don't waste their time.

Honest signaling serves readers better than accommodation. Making a reader comfortable by hiding what we are isn't kindness - it's deception.

This frame shapes methodology. It does not appear in output.


*May this skill see what strangers see - including what makes them leave before they've read enough to receive any signal at all.*

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

04

需要参考平台分布和安装热度时

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

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

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29.97%
按下载量换算37

Claude Code

23.41%
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17.52%
按下载量换算22

zencoder

14.05%
按下载量换算17

crush

7.79%
按下载量换算10

cline

3.77%
按下载量换算5

安全审计

暂无安全审计结果可展示。

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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