summarization

Condense multi-agent outputs into structured summaries with key points and evidence.

Updated Apr 16, 2026

Search Tags:#multi-agent #summarize #condense #open-questions #data-synthesis #agent-outputs #evidence-preserving

Ready to run

npx skills add https://github.com/seshxn/ai-swarm --skill summarization-seshxn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: summarization
Source: https://github.com/seshxn/ai-swarm/tree/main/skills/summarization
Command: npx skills add https://github.com/seshxn/ai-swarm --skill summarization-seshxn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Condense long multi-agent outputs into an accurate, traceable summary.

Core Features & Use Cases

Evidence-preserving: Retains essential findings and decisions while removing duplication.
Audience-aware: Tailors the summary for stakeholders needing concise handoffs.
Conflict-aware: Highlights disagreements and open questions to preserve context.

Quick Start

Summarize the latest multi-agent outputs into a concise, evidence-preserving summary.

Frequently Asked Questions about summarization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I condense multi-agent outputs without losing key evidence?▼

You can condense multi-agent outputs without losing evidence by applying an evidence-preserving summarization process that removes duplication while retaining essential findings and decisions. This produces a structured artifact containing the summary, key points, and evidence.

What is the best way to summarize verbose agent logs for stakeholder handoffs?▼

Summarizing verbose agent logs for stakeholder handoffs is best achieved through audience-aware condensation that tailors the output for concise handoffs. This highlights disagreements and open questions to preserve context while reducing noise.

How do I extract open questions and conflicts from multi-agent deliberations?▼

Extracting open questions and conflicts from multi-agent deliberations requires conflict-aware summarization that highlights disagreements and open questions. This preserves the context of agent decisions while reducing noise in the final report.

Can I generate structured JSON from multi-agent reports for downstream tooling?▼

Generating structured JSON from multi-agent reports is possible by condensing the outputs into a structured JSON-like artifact. This artifact contains the summary, key points, evidence, and open questions specifically formatted for downstream tooling.

Does evidence-preserving summarization work for multi-agent deliberations and reports?▼

Evidence-preserving summarization works effectively for multi-agent deliberations and reports where decisions and evidence must be preserved. It reduces noise while retaining essential findings and highlighting conflicts.