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Media Meta Analysis

  • 262 installs
  • 133 repo stars
  • Updated February 24, 2026
  • jwynia/agent-skills

media-meta-analysis is an agent skill that synthesizes multiple media analyses into cross-source themes, sentiment patterns, and effect sizes for developers and researchers evaluating content corpora before scaling concl

About

media-meta-analysis is a Data Science & ML research utility skill from jwynia/agent-skills at version 1.0. The skill operates on collections of already-analyzed media content, not single articles, to reveal cross-source patterns, conceptual networks, and emergent themes invisible in isolated reviews. It supports comparing themes, sentiment patterns, and effect sizes across a corpus before committing to production content or research claims. Developers and researchers reach for media-meta-analysis when they hold multiple prior media analyses and need a structured meta-analysis layer that connects findings across sources instead of re-reading each report manually.

  • Corpus-level comparisons
  • Effect-size reasoning
  • Theme and sentiment synthesis
  • Exploratory media stats
  • Evidence-backed narratives

Media Meta Analysis by the numbers

  • 262 all-time installs (skills.sh)
  • +3 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #604 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/jwynia/agent-skills --skill media-meta-analysis

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Listed on Skillselion
Installs262
repo stars133
Last updatedFebruary 24, 2026
Repositoryjwynia/agent-skills

How do you meta-analyze patterns across media content sources?

Analyze media datasets or content corpora to compare themes, sentiment patterns, and effect sizes before scaling production content or research conclusions.

Who is it for?

Researchers or content teams holding multiple completed media analyses who need corpus-level pattern synthesis before scaling production or publishing conclusions.

Skip if: Single-article summarization tasks or greenfield data collection where no prior analyzed media corpus exists yet.

When should I use this skill?

Multiple individual media analyses exist and you need cross-source pattern detection, theme comparison, or meta-analysis across the corpus.

What you get

Cross-source theme map, sentiment pattern summary, and meta-analysis insight report

By the numbers

  • Published as version 1.0 in jwynia/agent-skills metadata

Files

SKILL.mdMarkdownGitHub ↗

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.

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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

Related skills

FAQ

What input does media-meta-analysis require?

media-meta-analysis works on collections of already-analyzed media content, not raw single articles. Supply multiple prior media analyses and the skill cross-references them to surface emergent themes, sentiment patterns, and connections invisible in isolated reviews.

How is media-meta-analysis different from analyzing one article?

media-meta-analysis operates at corpus level across many analyzed sources. Single-article skills summarize one piece; media-meta-analysis version 1.0 synthesizes cross-source patterns and conceptual networks before scaling production content or research conclusions.

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