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Research Reprompter

Transform rough research questions into executable USACF research prompts.

Rating: 4.3 (134 reviews)
Downloads: 4,658 downloads
Version: 1.0.0

Overview

Transform rough research questions into executable USACF research prompts.

✨Key Features

Receive raw input

Complexity detection

Smart interview (gather user input)

Generate USACF super-prompt + score

Complete Documentation

View Source →

Researcher v2.0 (USACF Research Generator)

Voice-to-research engineering for Claude Code. Transform rough questions into executable USACF swarm configurations.

Changelog

Version	Changes
v2.0	Full USACF integration: algorithm selection, claude-flow commands, adversarial review, fact-checking, memory namespaces
v1.0	Initial version based on Reprompter v4.1

Purpose

Turn your rough research questions into complete, executable multi-agent research prompts using the USACF framework.

The Problem:

Research questions are often vague and unstructured
Manual setup of research swarms is tedious
Missing adversarial review leads to blind spots
No systematic algorithm selection

The Solution: Smart interview → USACF super-prompt with all phases, agents, and commands.

Process (4 steps)

Step 1: Receive raw input

Accept the user's rough research question (dictated, typed messily, or incomplete).

Trigger words: research, investigate, deep dive, analyze, research this

Step 2: Complexity detection

Auto-detect complexity to select algorithm:

Simple (< 20 words, single topic) → CoT, 1-3 agents
Medium (branching, comparison) → ToT, 4-8 agents
Complex (comprehensive, multi-domain) → GoT, 9-15 agents

Step 3: Smart interview (gather user input)

Gather:

Research Title - Name for this research
Subject - What we're researching
Subject Type - Product / Software / Business / Process / Organization
Research Type - Competitive / Gap / Technical / Due Diligence / Market
Objectives - What to find out (1-20, one per line)
Constraints - Focus areas, limitations (optional)
Depth - CoT / ToT / GoT
Output - Brief / Full Report / Action Plan / Raw

Step 4: Generate USACF super-prompt + score

Generate complete executable configuration with:

Initialization commands
All phase agents (Discovery, Analysis, Adversarial, Synthesis)
Memory operations
Final report generator
Quality score comparison

CRITICAL: MUST GENERATE COMPLETE SUPER-PROMPT

After interview completes, you MUST immediately:

Select algorithm (CoT/ToT/GoT) based on complexity
Generate full USACF super-prompt with ALL phases
Include claude-flow commands for every operation
Add adversarial review agents (red-team, fact-checker)
Show quality score (before/after comparison)
Offer to execute or copy

text

WRONG: Generate simple prompt without agents
RIGHT: Generate full USACF config with all phases, agents, memory ops

Algorithm selection matrix

Complexity	Algorithm	Topology	Agents	When to Use
Simple	Chain-of-Thought (CoT)	Star	1-3	"What is X?" Single topic
Medium	Tree-of-Thought (ToT)	Hierarchical	4-8	"Compare X vs Y" Branching
Complex	Graph-of-Thought (GoT)	Mesh/Hive	9-15+	"Comprehensive analysis"

Complexity Indicators:

Simple: Single topic, factual question, < 20 words
Medium: "compare", "vs", "evaluate", "options"
Complex: "comprehensive", "gaps and opportunities", multiple domains

USACF phases (all required for complex research)

Phase 0: Initialization

bash

npx claude-flow@alpha init --force
npx claude-flow@alpha swarm init --topology {topology} --max-agents {N}
npx claude-flow@alpha memory store "session/config" '{...}' --namespace search

Phase 0.5: Meta-analysis

Step-back prompting (principles, criteria)
Self-ask decomposition (15-20 questions)
Research planning (ReWOO)

Phase 1: Discovery (Parallel)

component-identifier
hierarchy-analyzer
interface-mapper
flow-tracer

Phase 2: Analysis (Parallel)

6 gap hunters (quality, performance, security, structural, capability, UX)
4 risk analysts (FMEA, edge cases, vulnerabilities, reliability)

Phase 2.5: Adversarial review (critical)

red-team-reviewer: Challenge ALL findings
fact-checker: RAG verification with web_search
coordinator: Integrate feedback, update confidence

Phase 3: Synthesis (Parallel)

quick-win-generator (0-3 months)
strategic-generator (3-12 months)
transformational-generator (12-36 months)
pareto-optimizer (multi-objective portfolios)

Phase 4: Final report

Ultra-brief (3 sentences)
Executive summary
Top 10 findings with confidence
Recommended actions by horizon
Limitations & uncertainties

Memory namespace convention

All agents store to namespaced memory:

bash

# Session
session/config          # Research configuration

# Meta
meta/principles         # Core principles
meta/questions          # Decomposed questions
meta/research-plan      # Planned tasks

# Discovery
discovery/components    # Identified components
discovery/hierarchy     # Structural map
discovery/interfaces    # APIs/contracts
discovery/flows         # Data/control flows

# Gaps
gaps/quality            # Quality gaps
gaps/performance        # Performance gaps
gaps/security           # Security gaps

# Risks
risks/fmea              # Failure mode analysis
risks/edge-cases        # Edge cases
risks/vulnerabilities   # Security vulnerabilities

# Adversarial
adversarial/critiques   # Red team challenges
adversarial/fact-check  # Verified claims

# Opportunities
opportunities/quick-wins        # 0-3 month wins
opportunities/strategic         # 3-12 month plays
opportunities/transformational  # 12-36 month bets
opportunities/pareto-recommendation  # Optimal portfolio

# Output
output/final-report     # Comprehensive report

Quality scoring

Always show before/after metrics:

Dimension	Before	After	Change
Clarity	X/10	X/10	+X%
Algorithm Selection	0/10	10/10	+∞
Agent Design	0/10	9/10	+∞
Memory Ops	0/10	10/10	+∞
Adversarial	0/10	9/10	+∞
Fact Checking	0/10	8/10	+∞
Overall	X/10	9+/10	+2000%+

Example

Before (rough input):

"look into what solana is doing with AI and how we compare"

After (USACF super-prompt):

markdown

# USACF Research: Solana AI Competitive Analysis

## Configuration
- Algorithm: ToT (medium complexity - comparison)
- Topology: Hierarchical
- Agents: 8
- Output: Executive Brief

## Phase 0: Initialization
[claude-flow init commands]

## Phase 1: Discovery
[4 parallel agents with memory stores]

## Phase 2: Analysis
[Gap hunters + risk analysts]

## Phase 2.5: Adversarial
[Red team + fact checker]

## Phase 3: Synthesis
[Opportunity generators + pareto optimizer]

## Phase 4: Report
[Final report generator]

Quality: 1.2/10 → 9.3/10 (+675%)

Tips for best results

Be specific about competitors - Name them in the input
Mention constraints early - "focus on Q1", "executive-level"
State objectives - Even rough ones help
Say "expand" - For full interview on simple queries
Say "quick" - To skip interview for simple research

Comparison: Reprompter vs Researcher

Aspect	Reprompter	Researcher
Trigger	"reprompt"	"research" / "researcher"
Purpose	General prompts	Research prompts
Output	Structured prompt	USACF swarm config
Agents	None	8-15 parallel agents
Memory	No	Full namespace system
Adversarial	No	Red team + fact checker
Algorithm	No	CoT/ToT/GoT selection

Installation

Terminal bash


openclaw install research-reprompter

Copied!

💻Code Examples

RIGHT: Generate full USACF config with all phases, agents, memory ops

right-generate-full-usacf-config-with-all-phases-agents-memory-ops.txt

---

## Algorithm selection matrix

| Complexity | Algorithm | Topology | Agents | When to Use |
|------------|-----------|----------|--------|-------------|
| Simple | Chain-of-Thought (CoT) | Star | 1-3 | "What is X?" Single topic |
| Medium | Tree-of-Thought (ToT) | Hierarchical | 4-8 | "Compare X vs Y" Branching |
| Complex | Graph-of-Thought (GoT) | Mesh/Hive | 9-15+ | "Comprehensive analysis" |

**Complexity Indicators:**
- Simple: Single topic, factual question, < 20 words
- Medium: "compare", "vs", "evaluate", "options"
- Complex: "comprehensive", "gaps and opportunities", multiple domains

---

## USACF phases (all required for complex research)

### Phase 0: Initialization

npx claude-flow@alpha memory store "session/config" '{...}' --namespace search

npx-claude-flowalpha-memory-store-sessionconfig----namespace-search.txt

### Phase 0.5: Meta-analysis
- Step-back prompting (principles, criteria)
- Self-ask decomposition (15-20 questions)
- Research planning (ReWOO)

### Phase 1: Discovery (Parallel)
- component-identifier
- hierarchy-analyzer
- interface-mapper
- flow-tracer

### Phase 2: Analysis (Parallel)
- 6 gap hunters (quality, performance, security, structural, capability, UX)
- 4 risk analysts (FMEA, edge cases, vulnerabilities, reliability)

### Phase 2.5: Adversarial review (critical)
- **red-team-reviewer**: Challenge ALL findings
- **fact-checker**: RAG verification with web_search
- **coordinator**: Integrate feedback, update confidence

### Phase 3: Synthesis (Parallel)
- quick-win-generator (0-3 months)
- strategic-generator (3-12 months)
- transformational-generator (12-36 months)
- pareto-optimizer (multi-objective portfolios)

### Phase 4: Final report
- Ultra-brief (3 sentences)
- Executive summary
- Top 10 findings with confidence
- Recommended actions by horizon
- Limitations & uncertainties

---

## Memory namespace convention

All agents store to namespaced memory:

output/final-report # Comprehensive report

outputfinal-report--comprehensive-report.txt

---

## Quality scoring

**Always show before/after metrics:**

| Dimension | Before | After | Change |
|-----------|--------|-------|--------|
| Clarity | X/10 | X/10 | +X% |
| Algorithm Selection | 0/10 | 10/10 | +∞ |
| Agent Design | 0/10 | 9/10 | +∞ |
| Memory Ops | 0/10 | 10/10 | +∞ |
| Adversarial | 0/10 | 9/10 | +∞ |
| Fact Checking | 0/10 | 8/10 | +∞ |
| **Overall** | **X/10** | **9+/10** | **+2000%+** |

---

## Example

**Before (rough input):**
> "look into what solana is doing with AI and how we compare"

**After (USACF super-prompt):**

example.sh

npx claude-flow@alpha init --force
npx claude-flow@alpha swarm init --topology {topology} --max-agents {N}
npx claude-flow@alpha memory store "session/config" '{...}' --namespace search

example.sh

# Session
session/config          # Research configuration

# Meta
meta/principles         # Core principles
meta/questions          # Decomposed questions
meta/research-plan      # Planned tasks

# Discovery
discovery/components    # Identified components
discovery/hierarchy     # Structural map
discovery/interfaces    # APIs/contracts
discovery/flows         # Data/control flows

# Gaps
gaps/quality            # Quality gaps
gaps/performance        # Performance gaps
gaps/security           # Security gaps

# Risks
risks/fmea              # Failure mode analysis
risks/edge-cases        # Edge cases
risks/vulnerabilities   # Security vulnerabilities

# Adversarial
adversarial/critiques   # Red team challenges
adversarial/fact-check  # Verified claims

# Opportunities
opportunities/quick-wins        # 0-3 month wins
opportunities/strategic         # 3-12 month plays
opportunities/transformational  # 12-36 month bets
opportunities/pareto-recommendation  # Optimal portfolio

# Output
output/final-report     # Comprehensive report

example.md

# USACF Research: Solana AI Competitive Analysis

## Configuration
- Algorithm: ToT (medium complexity - comparison)
- Topology: Hierarchical
- Agents: 8
- Output: Executive Brief

## Phase 0: Initialization
[claude-flow init commands]

## Phase 1: Discovery
[4 parallel agents with memory stores]

## Phase 2: Analysis
[Gap hunters + risk analysts]

## Phase 2.5: Adversarial
[Red team + fact checker]

## Phase 3: Synthesis
[Opportunity generators + pareto optimizer]

## Phase 4: Report
[Final report generator]

Quality: 1.2/10 → 9.3/10 (+675%)