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inspirerainspirera 搜索

Agent Skill

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。

总安装

685

周安装

28

GitHub Stars

2

下载量

220
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

unknown

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: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

简介

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。

SKILL.md

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

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

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

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

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

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。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

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