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media-meta-analysis媒体元分析

Agent Skill

media-meta-analysis 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。

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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:media-meta-analysis(媒体元分析)
来源仓库:https://github.com/jwynia/agent-skills
仓库路径:skills/media-meta-analysis
安装命令:
npx skills add https://github.com/jwynia/agent-skills --skill media-meta-analysis
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。

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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/jwynia/agent-skills --skill media-meta-analysis

简介

media-meta-analysis 用于媒体相关研究的检索与筛选,支持多宿主环境中的信息聚合。

  • 适用于关键词驱动的内容查找、来源线索匹配和研究资料整理。
  • 通过 npx skills add 命令从 agent-skills 仓库安装使用。
  • 安装前应核实权限、维护状态及是否触发联网或文件访问。
  • 建议查阅原始文档了解分析方法与适用范围。

SKILL.md

Media Meta-Analysis

Purpose

Synthesize patterns and connections across multiple individual media analyses to reveal deeper insights, conceptual networks, and emergent themes. Operates on collections of analyzed content, not individual pieces.

Core Principle

The whole reveals what the parts cannot. Patterns invisible in individual sources become visible across collections.


When to Use

Use after analyzing multiple pieces with individual extraction (e.g., media content extraction framework). This framework operates on collections of analyses, not raw media.


Collection Assessment

1. Corpus Composition

Document collection characteristics:

  • Content types: Video, article, podcast, etc.
  • Temporal distribution: Recency, historical coverage
  • Creator diversity: Single source or multiple
  • Topic distribution: Narrow or broad
  • Depth distribution: Quick takes vs. deep dives
  • Audience variations: Expert vs. general

Identify biases or gaps in coverage

2. Concept Frequency Analysis

AnalysisWhat to Track
Most frequent conceptsCore themes
High connection densityHub concepts
Isolated conceptsOrphan ideas
Concept clustersRelated idea groups
Terminology variationsSame idea, different words
Evolution over timeHow ideas develop

3. Argument Pattern Identification

Map the argumentation landscape:

  • Recurring claim types
  • Common evidence patterns
  • Shared assumptions across sources
  • Consistent logical structures
  • Frequent fallacies
  • Areas of consensus vs. contention

Connection Mapping

1. Concept Bridges

Discover connections between disparate sources:

  • Shared conceptual foundations
  • Complementary frameworks
  • Terminological equivalences
  • Parallel reasoning patterns
  • Similar metaphorical structures
  • Common historical/theoretical references

Map connection strength and directionality

2. Contradiction Detection

Identify meaningful tensions:

TypeExample
Direct claim contradictionsSource A says X, Source B says not-X
Competing interpretationsSame evidence, different conclusions
Framework incompatibilitiesFundamental approach differences
Value priority differencesDifferent hierarchies
Definitional inconsistenciesSame term, different meanings
Methodological disagreementsHow to study the question

Note whether contradictions are apparent or fundamental

3. Reinforcement Patterns

Identify mutually supporting elements:

  • Complementary evidence
  • Multi-source claim verification
  • Framework compatibility
  • Methodological triangulation
  • Converging conclusions from different approaches
  • Progressive refinement across sources

Rate reinforcement strength and source independence


Synthesis Elements

1. Emergent Themes

Patterns not prominent in individual pieces:

  • Implicit value structures
  • Recurring unacknowledged assumptions
  • Evolving discourse patterns
  • Shifts in emphasis
  • Boundary conditions of consensus
  • Questions raised but never answered

2. Knowledge Gaps

Map the negative space:

  • Consistently unaddressed questions
  • Missing methodological approaches
  • Excluded stakeholder perspectives
  • Underdeveloped theoretical connections
  • Limited evidential support areas
  • Potential blind spots

Prioritize by significance and addressability

3. Insight Amplification

Elements that gain significance across sources:

  • Ideas recurring in different contexts
  • Concepts serving as connective tissue
  • Formulations clarifying across domains
  • Evidence gaining cumulative strength
  • Questions revealing deeper patterns
  • Frameworks with broad applicability

Integration Protocol

1. Cross-Reference Index

StructurePurpose
Concept-to-source indexFind where ideas appear
Claim verification pathwaysTrace evidence chains
Contradiction mapsSee where sources disagree
Evidence chainsFollow proof patterns
Framework comparisonsCompare approaches
Question-answer networksTrack inquiry paths

2. Knowledge Graph Construction

Create navigable relationship models:

  • Core concept clusters
  • Evidence-claim networks
  • Source relationship maps
  • Temporal development patterns
  • Framework overlaps
  • Question exploration pathways

3. Narrative Pathways

Map exploration routes:

  • Progressive depth pathways
  • Contrasting perspective sequences
  • Framework comparison journeys
  • Evidence evaluation trails
  • Concept development traces
  • Question-driven routes

Documentation Template

Collection Metadata

## Collection: [Name]

**Sources:** [Number and types]
**Date Range:** [Publication dates]
**Analysis Period:** [When analyzed]
**Primary Domains:** [Subject areas]
**Analysis Purpose:** [Intended use]

Synthesis Element

## [Element Type]: [Theme/Connection/Pattern]

**Sources:** [Contributing sources with locations]
**Evidence:** [Key supporting examples]
**Significance:** [Why this matters]
**Tensions:** [Contradictions or complications]
**Exploration Vectors:** [Further investigation directions]

Application Guidelines

Content Creation Support

For developing new content:

  • Identify strongest evidence chains for claims
  • Map contradictory perspectives for balance
  • Locate terminological consensus for clarity
  • Find conceptual bridges for interdisciplinary work
  • Pinpoint high-value unanswered questions
  • Trace intellectual lineages for attribution

Research Direction Setting

For guiding investigation:

  • Prioritize knowledge gaps by significance
  • Identify promising conceptual connections
  • Map methodological blind spots
  • Locate perspective imbalances
  • Find evidence weaknesses
  • Discover emergent questions

Library Organization

For structuring knowledge:

  • Create concept-based navigation
  • Develop claim verification structures
  • Build perspective comparison frameworks
  • Map evidence quality distributions
  • Organize by question rather than topic
  • Structure around insight clusters

Anti-Patterns

1. Collection Without Curation

Pattern: Including all available sources without assessing their quality, relevance, or redundancy. Why it fails: Bad sources contaminate synthesis. Redundant sources create false consensus. Irrelevant sources distract from patterns that matter. Fix: Assess corpus composition explicitly. Remove low-quality sources. Weight sources by independence. Note when "multiple sources" are actually one source repeated.

2. Pattern Hallucination

Pattern: Finding patterns that exist only in the selection of sources, not in the underlying reality. Why it fails: Confirmation bias shapes what sources you find. If you search for "X causes Y," you'll find sources discussing X and Y. That's not evidence of a pattern. Fix: Actively seek disconfirming sources. Note absence of pattern where expected. Distinguish "all my sources agree" from "I selected sources that agree."

3. Averaging Instead of Mapping

Pattern: Synthesizing contradictory sources into a middle position—"the truth is somewhere between." Why it fails: Contradictions often indicate real disagreement, not measurement error. The middle position may be held by no one and supported by no evidence. Fix: Map contradictions explicitly. Understand why sources disagree. Present the landscape of positions rather than an artificial consensus.

4. Evidence Chain Collapse

Pattern: Citing a synthesis as if it were primary evidence, losing the chain back to original sources. Why it fails: Meta-analysis is only as good as its sources. When the chain collapses, you can't evaluate reliability or identify where disagreement actually lies. Fix: Maintain source-to-claim indices. Always know which original source supports which synthesis claim. Make verification pathways explicit.

5. Gap Neglect

Pattern: Focusing on what sources say without mapping what they don't say—the knowledge gaps and blind spots. Why it fails: What's missing is often more important than what's present. Systematic gaps reveal biases, under-researched areas, and opportunities. Fix: Explicitly map negative space. What questions do no sources address? What methodologies are absent? What perspectives are unrepresented?

Integration

Inbound (feeds into this skill)

SkillWhat it provides
researchIndividual source discovery and query expansion
claim-investigationVerified individual claims for synthesis
fact-checkQuality-checked individual analyses

Outbound (this skill enables)

SkillWhat this provides
researchIdentified gaps for further investigation
(content creation)Synthesized knowledge for original work
(knowledge organization)Structure for information architecture

Complementary

SkillRelationship
researchResearch finds sources; meta-analysis synthesizes them. Use iteratively—synthesis reveals gaps that research fills
claim-investigationClaim-investigation verifies individual claims; meta-analysis traces how claims connect across sources

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