Search

PythonReactSEOPDF
OpenClaw

research-reprompter

Transform rough research questions into executable USACF research prompts. Use when user says "research", "research this", "investigate", "deep dive", "researcher", or pastes a research topic. Generates complete multi-agent swarm configuration with algorithm selection, claude-flow commands, and adversarial review.

dorukardahan0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install dorukardahan/research-reprompter

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

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:

  1. Research Title - Name for this research
  2. Subject - What we're researching
  3. Subject Type - Product / Software / Business / Process / Organization
  4. Research Type - Competitive / Gap / Technical / Due Diligence / Market
  5. Objectives - What to find out (1-20, one per line)
  6. Constraints - Focus areas, limitations (optional)
  7. Depth - CoT / ToT / GoT
  8. 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, Syn...

Source information

Source
github
Repository
openclaw/skills
Path
skills/dorukardahan/research-reprompter
Version
—
Commit
746a87e712bdedc80c71ebd36c01997020298ccd
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# 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:
1. **Research Title** - Name for this research
2. **Subject** - What we're researching
3. **Subject Type** - Product / Software / Business / Process / Organization
4. **Research Type** - Competitive / Gap / Technical / Due Diligence / Market
5. **Objectives** - What to find out (1-20, one per line)
6. **Constraints** - Focus areas, limitations (optional)
7. **Depth** - CoT / ToT / GoT
8. **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, Syn...

Related skills

OpenClaw

bambu-studio-ai

heyixuan2

Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...

OpenClaw

geo-optimization

capt-marbles

Generative Engine Optimization (GEO) for AI search visibility. Optimize content to appear in ChatGPT, Perplexity, Claude, and Google AI Overviews. Use when optimizing websites, pages, or content for LLM discove...

OpenClaw

0protocol

0isone

Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.

OpenClaw

0x0-messenger

eijiac24

Send and receive P2P messages using disposable numbers and PINs. No servers, no accounts. Use for human notifications, approval flows, and agent-to-agent communication.