Skill

research-loop

Orchestrate multi-agent research with recursive self-improvement. USE THIS SKILL whenever the user asks for comprehensive research, deep analysis, multi-source comparison, or investigation across documentation, codebases, and web sources. Trigger for: "research", "deep dive", "investigate", "compare", "survey", "comprehensive analysis", "find out everything about", or any question that needs multiple sources to answer well. This skill spawns parallel subagents, synthesizes findings, evaluates gaps, and recursively improves.

Install

npx claudepluginhub jadecli/claude-knowledge-sdk-typescript

Tool Access

This skill is limited to using the following tools:

ReadGrepGlobWebSearchWebFetchAgent

Preview

```

SKILL.md

Similar Skills

cache-components

Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.

cache-components

139.1k

claude-opus-4-5-migration

2 files

Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.

claude-opus-4-5-migration

83.2k

claude-code-plugin-release

1 file

Automates semantic versioning and release workflow for Claude Code plugins: bumps versions in package.json, marketplace.json, plugin.json; verifies builds; creates git tags, GitHub releases, changelogs.

claude-mem

64.7k

Stats

Stars0

Forks0

Last CommitMar 26, 2026

Actions

View Source View Plugin View on GitHub View README

Multi-Agent Research Loop

Architecture (from anthropic.com/engineering/multi-agent-research-system)

Lead Agent (Opus) ─── classifies query
    │
    ├── Subagent 1 (Sonnet) ── explores subtopic A
    ├── Subagent 2 (Sonnet) ── explores subtopic B
    └── Subagent N (Sonnet) ── explores subtopic N
    │
    └── Lead synthesizes ── evaluates gaps ── spawns follow-ups

Process

Phase 1: Classify

Determine query complexity:

Simple: 0 subagents, direct answer (1-3 tool calls)
Lookup: 1 subagent, single-source (3-10 tool calls)
Comparison: N subagents per entity (10-15 calls each)
Deep dive: N subagents per facet (10-15 calls each)
Survey: N subagents per subtopic (10-15 calls each, up to 10)

Phase 2: Plan

For each subagent, define:

Specific objective (one core question)
Output format (summary, JSON, code, or comparison table)
Tools to use (Read, Grep, WebSearch, WebFetch)
Scope boundaries (what NOT to research)

Phase 3: Execute

Fan out all subagents in parallel using the Agent tool. Each subagent:

Gets a clean context window
Explores with 10-15 tool calls
Returns compressed findings (10K→1K tokens)

Phase 4: Synthesize

The lead agent merges all subagent findings into:

BLUF (Bottom Line Up Front) — direct answer in first paragraph
Evidence sections — 3-5 focused sections with bold key facts
Confidence — explicit confidence level and limitations

Phase 5: Evaluate & Improve (Recursive)

Use Haiku to evaluate the synthesis for gaps:

If gaps found → spawn targeted follow-up agents
Merge follow-up findings → re-synthesize
Repeat up to 3 rounds maximum

Scaling Rules

Query Type	Agents	Tool Calls/Agent	Model
Simple	0	0	haiku
Lookup	1	3-10	sonnet
Comparison	2-5	10-15	sonnet
Deep Dive	2-4	10-15	sonnet
Survey	5-10	10-15	sonnet

Output Format

Always use BLUF (Smart Brevity):

First paragraph answers the question directly
Bold key facts and figures
Narrative prose, not bullet lists
Source attribution inline
Final paragraph: confidence level and known limitations