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Agent Skill · 研究检索

inspirera(inspirera 搜索)

inspirera 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。

inspirera 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 48,496。

总安装 685周安装 28GitHub Stars 2下载 220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
unknown

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:inspirera(inspirera 搜索)
来源仓库:https://github.com/jgabor/agentera
仓库路径:skills/inspirera
安装命令:
npx skills add https://github.com/jgabor/agentera --skill inspirera
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。

skills.shnpx skills
npx skills add https://github.com/jgabor/agentera --skill inspirera

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。

inspirera 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。适用于内容研究、数据调研或信息聚合等需要从多个来源提取线索的任务场景。通过 npx skills add 命令安装,需确认权限范围和是否触发联网或文件读写操作。

建议结合原始 README 核验具体用法,并检查仓库维护状态与安全性。使用前应评估是否会执行命令或访问外部资源,避免意外行为。

来源仓库内路径:skills/inspirera

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/jgabor/agentera --skill inspirera;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

inspirera 怎么安装?

在终端执行 npx skills add https://github.com/jgabor/agentera --skill inspirera。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

inspirera 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

inspirera 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。

inspirera 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

inspirera 用的人多吗?

公开数据是累计安装 685 次,近一周安装 28 次,下载 220 次,GitHub 2 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。

inspirera 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 Codex,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。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
inspirera

inspirera 搜索 · 本页

6852调用外部服务未明确未通过

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

INSPIRERA

Insight Navigation: Source Pattern Identification and Resonance. Evaluate, Reframe, Assimilate

Analyze an external resource and map its ideas to a target project. Output a structured markdown analysis the user can navigate and act on.

Skill introduction: ─── ⬚ inspirera · analysis ───

Step markers: display ── step N/5: verb before each step. Steps: identify, read, explore, map, deliver.


Step 1: Identify source and target

From the user's message, extract:

  • Source: the external URL (GitHub repo, article, docs, HN thread, etc.)
  • Target: the user's project, which could be any of:

- A GitHub repo URL → explore via GitHub MCP - A local path or project name → explore via filesystem tools - The current working directory → if the user says "my project" / "what I'm building" without a URL, and they're clearly working in a project, treat cwd as the target - Absent → if truly no target is implied, skip Steps 3–4 and do source-only analysis


Step 2: Read the source

This should feel like a colleague diving into something interesting, genuinely curious, reading deeply, forming opinions as you go. Not a report generator collecting data points.

GitHub repos

Use GitHub MCP to explore deeply:

  1. List root directory structure
  2. Read README
  3. Read key source directories until you understand: core abstractions, design patterns, notable primitives, dependencies, clever approaches worth borrowing

Go deep and don't stop at the README. If GitHub MCP returns errors, fall back to public pages or note the limitation.

Articles, blog posts, docs pages

Fetch full content. Extract core thesis, named concepts/patterns, code samples, and referenced tools. If paywalled, try reader-mode variant; if that fails, note the limitation.

Hacker News threads

Read both the linked article and top comments. HN comments often contain the most useful distillation. Treat as signal.

Known libraries

For well-known libraries, also check context7 for up-to-date docs beyond the README.

Before proceeding to target analysis: in your response, list the 3-5 most transferable concepts from the source. These survive if the source file reads are cleared.


Step 3: Read the target project

Choose the exploration strategy based on the target type identified in Step 1.

Local projects (current directory or local path)

Common case. Use filesystem tools (faster, includes uncommitted work):

  1. Glob to map the directory structure (e.g. **/*.{ts,go,py,rs})
  2. Read README.md if one exists
  3. Check dependency manifests (package.json, go.mod, Cargo.toml, pyproject.toml, etc.)
  4. Grep for patterns, imports, or abstractions relevant to the source's concepts
  5. Read key source files to understand architecture and current patterns

Remote GitHub repos

Use the GitHub MCP:

  1. List the root directory structure
  2. Read the README
  3. Read dependency manifests and key source files

Build understanding of

Language, stack, dependencies, architecture, patterns, and problems being solved.

Check for existing usage

Does the target already use the source (or a fork/alternative)?

  • Already using: "Getting the most out of it?" Focus on underused features and better patterns.
  • Using alternative: "Worth switching?" Compare approaches and migration cost.
  • Not using: "Should you adopt?" This is the default framing.

Step 4: Map concepts across

With both codebases understood, reason about applicability:

  • What is the source doing that the target should be?
  • Abstractions that simplify current complexity?
  • Patterns the target implements manually or poorly?
  • Primitives worth borrowing or adapting?
  • Source doing something the target does, but better?
  • Fundamentally incompatible? Say so clearly.
  • Adoption cost: one-file change or multi-sprint refactor?

Step 5: Deliver the analysis

The sharp colleague, here to share what you dug up, not file a report. Open with your take before the structured sections: what excited you, what surprised you, what the user should care about most. "Here's what I found and what matters for us." The structured analysis follows, but the human read comes first.

Write a structured markdown analysis:

Output format

# [Source Name] → [Target Name]: Cross-Pollination Analysis

## TL;DR
One or two sentences. Is this worth pursuing? What's the strongest single takeaway?

## Source Overview
Brief summary of what the source does and its core design philosophy.

## Key Concepts

### [Concept Name]
What it is, why it's interesting, and concretely where/how it applies to the target.

### [Concept Name]
...repeat for each significant concept (typically 2–5)

Reason through concept applicability in your response text. The Applicability Matrix below
should contain only conclusions, not reasoning chains.
Output constraint: ≤15 words per matrix cell.

## Applicability Matrix

| Concept | Relevance | Effort | Where in [Target] | Already Partially Done? |
|---------|-----------|--------|-------------------|------------------------|
| ...     | High/Med/Low | Low/Med/High | specific module or file | Yes/No |

## What Doesn't Apply
Honest assessment of concepts/patterns that look interesting but don't fit, and why.
Being clear about what *not* to adopt is as valuable as the recommendations.

## Recommended Next Steps
▸ [action] · [specific file or module]
▸ [action] · [specific file or module]
Ordered by value/effort ratio.

Tone: direct, technically fluent. Skip empty sections. Lead with highest signal.

Offer to go deeper: prototype a change, explore a concept with code, compare alternatives.

No target given

Surface transferable concepts in general terms. Skip Applicability Matrix. Ask if the user wants to map to a specific project.

Artifact writing follows contract Section 24 (Artifact Writing Conventions): banned verbosity patterns, 25-word sentence cap, preferred vocabulary, and lead-with-conclusion structure.


Exit signals

Report one of these statuses at workflow completion:

Format: ─── ⬚ inspirera · status ─── followed by a summary sentence. For flagged, stuck, and waiting: add ▸ bullet details below the summary.

  • complete: Source was read deeply, target project was explored (if provided), concept mapping was completed, and a structured analysis with applicability matrix and recommended next steps was delivered.
  • flagged: Analysis completed but with limitations worth surfacing: the source was paywalled or truncated, the target project was inaccessible, or key concepts could not be fully assessed for fit (e.g., incompatible language or paradigm).
  • stuck: Cannot proceed because the source URL is inaccessible and no fallback content is available, or the target project specified does not exist and cannot be located.
  • waiting: The source link was not provided or is malformed, or the target project is genuinely ambiguous and neither the current directory nor context resolves it.

State artifacts

No dedicated state file. Writes to other skills' artifacts.

ArtifactPurposeAccess
TODO.mdFile actionable findings for realisera (severity per contract)write
VISION.mdRefine direction when inspiration shifts thinkingwrite

Artifact path resolution

Before reading or writing any artifact, check if.agentera/DOCS.md exists. If it has an Artifact Mapping section, use the path specified for each canonical filename (TODO.md, VISION.md, etc.). If.agentera/DOCS.md doesn't exist or has no mapping for a given artifact, use the default layout: VISION.md, TODO.md, and CHANGELOG.md at the project root; all other artifacts in.agentera/. This applies to all artifact references in this skill, including cross-skill writes (TODO.md, VISION.md).

Contract

Before starting, read references/contract.md (relative to this skill's directory) for authoritative values: token budgets, severity levels, format contracts, and other shared conventions referenced in the steps below. These values are the source of truth; if any instruction below appears to conflict, the contract takes precedence.


Cross-skill integration

Inspirera is part of a twelve-skill suite. Its analysis feeds naturally into the other skills.

Feeding into /realisera

Add actionable findings to the project's TODO.md, classifying each by severity per contract severity levels. Or refine VISION.md's direction if the inspiration shifts thinking. The next realisera cycle picks up the changes automatically.

Feeding into /optimera

When the source contains optimization techniques (performance patterns, algorithm improvements, caching strategies), optimera's Hypothesize step can draw on the analysis for its next experiment.

Informed by /profilera

If a decision profile exists at $PROFILERA_PROFILE_DIR/PROFILE.md (default: $XDG_DATA_HOME/agentera/PROFILE.md), run the effective profile script (python3 scripts/effective_profile.py from the profilera skill directory) for a confidence-weighted summary. Use effective confidence to weight applicability judgments per contract profile consumption conventions. If the script or PROFILE.md is missing, proceed without persona grounding.

Feeding into /visionera

When the analysis shifts thinking about the project's direction (a new paradigm, a competitor's approach, or a user need not yet captured), the findings can inform vision refinement. Suggest /visionera to revisit VISION.md with the new context.

Feeding into /planera

When the analysis recommends adopting patterns or libraries, planera can incorporate those recommendations into a plan's design section and task decomposition.

Feeding into /resonera

When the analysis surfaces recommendations that require deliberation (competing approaches, unclear adoption cost, or tradeoffs the user needs to resolve), suggest /resonera to think it through before acting. Resonera can evaluate which recommendations are actually worth adopting and capture the reasoning in DECISIONS.md.


Safety rails

  • NEVER modify code in the target project. Inspirera analyzes; other skills implement.
  • NEVER write to TODO.md or VISION.md without explicit user confirmation. Present findings and get approval before filing.
  • NEVER present shallow analysis as deep insight. If you haven't read the source thoroughly, say so.
  • NEVER recommend adoption without assessing fit. Every recommendation must consider the target project's constraints, stack, and principles.
  • NEVER fabricate source content. Quote actual code and text from the source.

Getting started

Analyze a GitHub repo

/inspirera https://github.com/org/repo

Reads the repo, maps its patterns to your current project.

Analyze an article or docs page

/inspirera https://example.com/blog/interesting-approach

Extracts transferable concepts and assesses applicability.

Feed findings into the development loop

After analysis, file actionable findings to TODO.md for /realisera to pick up, or refine VISION.md if the research shifts your project's direction.


Notes on depth vs. speed

  • Read more files, not fewer. Shallow reads produce shallow analysis
  • Large repos: focus on modules most relevant to the concept, not everything
  • Explore source and target concurrently where possible
  • Always use GitHub MCP for GitHub URLs

平台分布

Codex

32.53%
按下载量换算72

Claude

31.51%
按下载量换算69

Cursor

17.8%
按下载量换算39

Gemini CLI

8.56%
按下载量换算19

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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