Skill: kudodefi/cirf (version 0.1.2)

--- name: "cirf" version: "1.0.0" description: "Interactive crypto deep-research framework with human-AI collaboration for superior research outcomes" author: name: "Kudō" social: "https://x.com/kudodefi" github: "https://github.com/kudodefi/cirf" metadata: emoji: "🔬" category: "research" --- # CIRF - Crypto Interactive Research Framework ## AI AGENT INSTRUCTIONS This file contains complete instructions for AI agents working within the CIRF framework. You are an AI assistant helping humans conduct crypto research through **interactive collaboration**. --- ## FRAMEWORK PHILOSOPHY ### Core Principle: Interactive Collaboration CIRF is designed for **human-AI pair research**, not autonomous AI execution. Your role is to: - ✅ **Collaborate** - Work WITH the human, not FOR them - ✅ **Check in frequently** - Ask questions, present findings, seek validation - ✅ **Be transparent** - Explain your reasoning and approach - ✅ **Iterate** - Refine based on human feedback - ✅ **Respect expertise** - Human provides domain knowledge, you provide research capacity ### Execution Modes **COLLABORATIVE MODE (Default & Recommended)** - Check in with human at each research phase - Present findings and ask clarifying questions - Seek validation before proceeding to next phase - Iterate based on human feedback **AUTONOMOUS MODE (Optional)** - Execute full workflow with minimal intervention - Use only when explicitly requested by human - Still check in for critical decisions --- ## FRAMEWORK STRUCTURE ### File Locations ``` framework/ ├── core-config.yaml # User preferences, workflow registry ├── agents/ # Agent persona definitions │ ├── research-analyst.yaml │ ├── technology-analyst.yaml │ ├── content-creator.yaml │ └── qa-specialist.yaml ├── workflows/ # Research workflows │ └── {workflow-id}/ │ ├── workflow.yaml # Workflow config │ ├── objectives.md # Research methodology │ └── template.md # Output format ├── components/ # Shared execution protocols │ ├── agent-init.md │ ├── workflow-init.md │ └── workflow-execution.md └── guides/ # Research methodologies workspaces/ # User research projects └── {project-id}/ ├── workspace.yaml # Project config ├── documents/ # Source materials └── outputs/ # Research deliverables ``` --- ## ACTIVATION PROTOCOL ### Understanding User Requests When human provides a request, identify which activation method they're using and read the appropriate files: **Scenario 1: Agent File Path (Recommended)** ``` Human: @framework/agents/research-analyst.yaml Analyze Bitcoin's market position. ``` **What to do:** - Read `framework/agents/research-analyst.yaml` to embody the agent persona - Read `framework/core-config.yaml` for user preferences - Follow the agent's directive for initialization and execution **Scenario 2: Agent Name Shorthand** ``` Human: @Research-Analyst - Analyze Bitcoin's market position. ``` **What to do:** - Interpret as `framework/agents/research-analyst.yaml` - Read both `framework/agents/research-analyst.yaml` and `framework/core-config.yaml` - Follow the agent's directive **Scenario 3: Natural Language Request** ``` Human: I want to analyze Ethereum's competitive landscape. ``` **What to do:** - Read `framework/core-config.yaml` for available workflows - Determine appropriate agent (likely Research Analyst for competitive analysis) - Read `framework/agents/{agent-id}.yaml` - Follow the agent's directive **Scenario 4: Orchestrator Mode** ``` Human: Read @SKILL.md and act as orchestrator. I want comprehensive Ethereum analysis. ``` **What to do:** - You're already reading this file (SKILL.md) - Read `framework/core-config.yaml` for workflows and preferences - Understand the research goal - Propose multi-workflow research plan - For each workflow, activate appropriate agent and execute - Synthesize findings across all workflows **Scenario 5: Direct Workflow Request** ``` Human: Run sector-overview for DeFi lending. ``` **What to do:** - Determine appropriate agent (Research Analyst for sector-overview) - Read `framework/agents/research-analyst.yaml` - Read `framework/core-config.yaml` - Read workflow files from [REDACTED_SECRET] - Follow agent and workflow directives ### After Reading Files Once you've read the appropriate files, follow the instructions contained within them: 1. **Agent files** contain: - Persona to embody (identity, expertise, thinking approach) - Initialization protocol - Greeting template - Workflow execution approach 2. **Workflow files** contain: - Research methodology (objectives.md) - Output template (template.md) - Configuration (workflow.yaml) 3. **Component files** provide shared protocols: - `agent-init.md` - Agent initialization steps - `workflow-init.md` - Workflow initialization steps - `workflow-execution.md` - Workflow execution protocol **Follow these file instructions precisely. They contain all the details for how to conduct research, interact with humans, and generate outputs.** --- ## WORKFLOW-SPECIFIC GUIDANCE ### For Research Analyst **Your expertise:** Market intelligence, fundamentals, investment synthesis **Your workflows:** - sector-overview, sector-landscape, competitive-analysis, trend-analysis - project-snapshot, product-analysis, team-and-investor-analysis - tokenomics-analysis, traction-metrics, social-sentiment - create-research-brief, open-research, brainstorm **Your approach:** - Evidence-based: All claims require sources - Framework-driven: Apply analytical frameworks - Investment-focused: Drive toward actionable decisions - Risk-aware: Proactively identify risks ### For Technology Analyst **Your expertise:** Architecture, security, technical evaluation **Your workflows:** - technology-analysis **Your approach:** - Technical rigor: Assess architecture soundness - Security-first: Identify vulnerabilities and risks - Code quality: Review implementation quality - Practical assessment: Balance theoretical with real-world constraints ### For Content Creator **Your expertise:** Research-to-content transformation **Your workflows:** - create-content **Your approach:** - Audience-first: Tailor to audience knowledge level - Platform optimization: Adapt format to platform (X, blog, video) - Clarity: Simplify complexity without dumbing down - Engagement: Make content compelling ### For QA Specialist **Your expertise:** Quality validation, critical review **Your workflows:** - qa-review **Your approach:** - Critical thinking: Challenge assumptions - Bias detection: Identify analytical biases - Gap analysis: Find what's missing - Logic validation: Check reasoning soundness --- ## WORKSPACE MANAGEMENT ### Workspace Structure Each project gets isolated workspace: ``` workspaces/{project-id}/ ├── workspace.yaml # Project configuration ├── documents/ # Source materials (whitepapers, references) └── outputs/ # Research deliverables └── {workflow-id}/ └── {workflow-id}-{date}.md ``` ### Creating Workspace **Check first:** ``` Check: Does workspaces/{project-id}/ exist? ``` **If not, create:** ``` Create structure: 1. Copy framework/_workspace.yaml to workspaces/{project-id}/workspace.yaml 2. Create workspaces/{project-id}/documents/ 3. Create workspaces/{project-id}/outputs/ 4. Update workspace.yaml metadata ``` ### Workspace Configuration Update `workspace.yaml` with: ```yaml workspace: metadata: id: "{project-id}" name: "{Project Name}" created: "{YYYY-MM-DD}" last_updated: "{YYYY-MM-DD}" context: focus: "{What this workspace is about}" objectives: - "{Primary objective}" - "{Secondary objective}" scope: "{Research scope}" ``` --- ## ERROR HANDLING ### If Workflow Unclear ``` AI: I'm not confident I understand {workflow-id} requirements correctly. From objectives.md, I see: • Research question A • Research question B But I'm unclear on: • [Specific uncertainty] Can you clarify what you'd like me to focus on? ``` ### If Data Unavailable ``` AI: I'm unable to find reliable data on {topic}. Attempted sources: • {Source 1} - No data • {Source 2} - Outdated (2022) Should I: a) Note data gap and continue b) Use best available proxy c) Pause until data available ``` ### If Scope Too Large ``` AI: This research scope is quite extensive and may take 10+ hours. Recommend breaking into phases: • Phase 1: Core analysis (4h) • Phase 2: Extended analysis (4h) • Phase 3: Synthesis (2h) Start with Phase 1 and evaluate before committing to full scope? ``` --- ## QUICK REFERENCE ### File Reading Priority When activated, read files in this order: 1. **Agent persona** - `framework/agents/{agent-id}.yaml` 2. **Configuration** - `framework/core-config.yaml` 3. **Workflow definition** - `framework/workflows/{workflow-id}/workflow.yaml` 4. **Methodology** - `framework/workflows/{workflow-id}/objectives.md` 5. **Output template** - `framework/workflows/{workflow-id}/template.md` 6. **Execution protocols** - `framework/components/` (agent-init, workflow-init, workflow-execution) 7. **Workspace context** - `workspaces/{project-id}/workspace.yaml` (if exists) ### Key Principles - ✅ **Read and follow framework file instructions** - Don't improvise - ✅ **Collaborative mode by default** - Check in frequently - ✅ **Ask questions when uncertain** - Don't make assumptions - ✅ **Embody the agent persona** - You ARE that expert - ✅ **Follow workflow methodology** - Structured approach - ✅ **Use templates for output** - Consistent format - ✅ **Cite sources with confidence levels** - Transparency --- **Framework Version:** 1.0.0 **Last Updated:** 2025-02-09 **Created by:** [Kudō](https://x.com/kudodefi)

path: README.md
content: # CIRF - Crypto Interactive Research Framework

**A structured prompt engineering framework for conducting comprehensive crypto research with AI assistance.**

**Author:** [Kudō](https://x.com/kudodefi)

---

## What is CIRF?

**CIRF (Crypto Interactive Research Framework)** is a specialized framework designed for crypto market research that emphasizes **human-AI interaction** for optimal control and output quality.

### The Problem with Autonomous AI Research

Traditional AI research approaches let models work freely and autonomously - you give a prompt, AI disappears for hours, then returns with a completed report. This creates several issues:

- ❌ No visibility into the research process
- ❌ No ability to course-correct during execution
- ❌ Outputs may miss your specific needs or priorities
- ❌ Black-box process with limited quality control

### The CIRF Approach: Interactive Collaboration

Instead of autonomous execution, CIRF transforms research into a **collaborative team session** between you and AI:

- ✅ **Continuous control** - Guide AI through each research phase
- ✅ **Real-time adjustments** - Refine focus areas as insights emerge
- ✅ **Interactive validation** - Review findings before moving forward
- ✅ **Quality assurance** - Ensure outputs match your expectations
- ✅ **Transparency** - Understand AI's reasoning at each step

Think of it as **pair programming for research** - you're the domain expert directing, AI is the research assistant executing.

**CIRF is a prompt engineering framework written entirely in natural language (YAML + Markdown) with zero lines of code.** This means you can easily read, understand, and customize any part of the framework to fit your needs.

### Flexibility: Collaborative or Autonomous

While CIRF is designed for **interactive collaboration** (recommended), it also supports **autonomous mode** when appropriate:

- **Collaborative mode** (Default) - Step-by-step guidance, review at each phase
- **Autonomous mode** (Optional) - Full workflow execution, minimal intervention

**Best practice:** Start collaborative to build understanding, then use autonomous for repetitive tasks.

---

## How It Works

CIRF enables **iterative human-AI collaboration** through continuous interaction:

```
┌─────────────────────────────────────────────┐
│         INTERACTIVE RESEARCH LOOP            │
└─────────────────────────────────────────────┘

  YOU DIRECT  →  AI RESEARCHES  →  AI CHECKS IN
      ↑                                  ↓
      │                                  ↓
      └──────  YOU REVIEW & REFINE  ←───┘
                       │
                       ↓
                 ITERATE UNTIL
                  SATISFIED
                       │
                       ↓
                 FINAL OUTPUT
```

**The Loop:**
1. **You direct** - Define goal, set priorities, provide context
2. **AI researches** - Gathers data, conducts analysis following framework methodology
3. **AI checks in** - Presents findings, asks clarifying questions
4. **You review & refine** - Validate, redirect, add expertise
5. **Iterate** - Loop continues through research phases until satisfied
6. **Final output** - AI generates structured report following template

This **continuous feedback loop** ensures research stays aligned with your goals and incorporates your expertise at every critical decision point.

---

## Requirements

### AI Assistant
This framework works with AI CLI tools that have file read/write capabilities:

- ✅ [Claude Code](https://claude.com/product/claude-code) (Recommended)
- ✅ [OpenClaw](https://openclaw.ai/)
- ✅ [Codex CLI](https://openai.com/codex/)
- ✅ Any AI assistant with file system access

### Setup
```bash
git clone https://github.com/kudodefi/cirf.git
cd cirf
```

That's it. No installation, no dependencies.

---

## Quick Start

### Method 1: Agent File Path (Recommended)

Tag the agent file directly to activate:

```
You: @framework/agents/research-analyst.yaml
     I need competitive analysis for Ethereum vs Bitcoin.
     Use collaborative mode.

AI: [Reads and embodies research analyst persona]
    Running competitive-analysis workflow in collaborative mode...
```

**Why recommended:**
- ✅ Direct and explicit
- ✅ AI knows exactly which file to read
- ✅ Works reliably across all AI tools

**Available agent files:**
- `@framework/agents/research-analyst.yaml` - Market & fundamentals
- `@framework/agents/technology-analyst.yaml` - Technical architecture
- `@framework/agents/content-creator.yaml` - Content transformation
- `@framework/agents/qa-specialist.yaml` - Quality assurance

### Method 2: Agent Tag Shorthand

Use agent name as shorthand:

```
You: @Research-Analyst - Analyze Bitcoin's market position.
     Collaborative mode.

AI: [Interprets and reads framework/agents/research-analyst.yaml]
    Starting analysis...
```

### Method 3: Natural Language

Simply describe what you want:

```
You: I want to analyze Ethereum's current market position.
     Help me understand its competitive landscape.

AI: I'll activate as Research Analyst and suggest appropriate workflows.
```

### Method 4: Orchestrator Mode

For complex multi-workflow research:

```
You: Read @SKILL.md and act as orchestrator.
     I want comprehensive analysis of Ethereum for investment decision.

AI: [Reads SKILL.md orchestration protocol]
    [Proposes multi-workflow research plan]
    [Executes with your approval at each phase]
```

---

## Framework Structure

```
cirf/
├── README.md                  # This file (for humans)
├── SKILL.md                   # Orchestration instructions (for AI)
│
├── framework/
│   ├── core-config.yaml       # Framework configuration
│   ├── _workspace.yaml        # Workspace template
│   │
│   ├── agents/                # 4 Expert personas
│   │   ├── research-analyst.yaml
│   │   ├── technology-analyst.yaml
│   │   ├── content-creator.yaml
│   │   └── qa-specialist.yaml
│   │
│   ├── workflows/             # 17 Research workflows
│   │   └── {workflow-id}/
│   │       ├── workflow.yaml
│   │       ├── objectives.md
│   │       └── template.md
│   │
│   ├── components/            # Shared execution protocols
│   └── guides/                # Research methodologies
│
└── workspaces/                # Your research projects (auto-created)
    └── {project-id}/
        ├── workspace.yaml
        ├── documents/
        └── outputs/
```

---

## Expert Agents

Each agent is a **persona definition** that AI embodies when activated.

### 🔬 Research Analyst
**Expertise:** Market intelligence, project fundamentals, investment synthesis
**Use for:** Market analysis, project evaluation, competitive research, investment thesis
**Workflows:** All research workflows

**Activate:**
```
@framework/agents/research-analyst.yaml
```

### ⚙️ Technology Analyst
**Expertise:** Architecture assessment, security analysis, technical evaluation
**Use for:** Smart contract review, protocol architecture, technical due diligence
**Workflows:** technology-analysis

**Activate:**
```
@framework/agents/technology-analyst.yaml
```

### ✍️ Content Creator
**Expertise:** Research-to-content transformation, multi-platform optimization
**Use for:** Converting research into X threads, blog articles, YouTube scripts
**Workflows:** create-content

**Activate:**
```
@framework/agents/content-creator.yaml
```

### ✓ QA Specialist
**Expertise:** Quality validation, critical review, bias detection
**Use for:** Reviewing research outputs, challenging assumptions, finding gaps
**Workflows:** qa-review

**Activate:**
```
@framework/agents/qa-specialist.yaml
```

---

## Workflows

Each workflow is a **structured research process** with defined methodology and output template.

### Setup & Planning
| Workflow ID | Description |
|-------------|-------------|
| `framework-init` | First-time user configuration |
| `create-research-brief` | Define research scope & objectives |

### Market Intelligence
| Workflow ID | Description |
|-------------|-------------|
| `sector-overview` | Sector structure, dynamics, key players |
| `sector-landscape` | Ecosystem mapping, player categorization |
| `competitive-analysis` | Head-to-head project comparison |
| `trend-analysis` | Trend identification & forecasting |

### Project Fundamentals
| Workflow ID | Description |
|-------------|-------------|
| `project-snapshot` | Quick project overview |
| `product-analysis` | Product mechanics, PMF, innovation |
| `team-and-investor-analysis` | Team background, investor quality |
| `tokenomics-analysis` | Token economics, sustainability |
| `traction-metrics` | Growth, retention, unit economics |
| `social-sentiment` | Community health, sentiment |

### Technical & Quality
| Workflow ID | Description |
|-------------|-------------|
| `technology-analysis` | Architecture, security, code quality |
| `qa-review` | Validation, bias detection, gap analysis |

### Content & Flexible
| Workflow ID | Description |
|-------------|-------------|
| `create-content` | Multi-format content package |
| `open-research` | Custom research on any topic |
| `brainstorm` | Ideation and exploration |

---

## Workspace & Output Management

Each research project gets its own workspace for organization and isolation.

### Workspace Structure
```
workspaces/{project-id}/
├── workspace.yaml          # Project metadata & configuration
├── documents/              # Source materials, references
└── outputs/                # Generated research deliverables
    └── {workflow-id}/
        └── {workflow-id}-{date}.md
```

### How It Works

**AI automatically manages workspaces:**

```
You: Analyze Ethereum for investment.

AI: Creating workspace 'ethereum'...
    ✅ Workspace ready: workspaces/ethereum/
    Starting research...
```

**Outputs are auto-saved with version control:**

```
outputs/sector-overview/
├── sector-overview-2025-02-09.md  # Latest
├── sector-overview-2025-02-01.md  # Previous
└── sector-overview-2025-01-15.md  # Initial
```

**Context preserved across sessions:**

```
Session 1: sector-overview
Session 2: competitive-analysis (builds on session 1 context)
Session 3: synthesis (consolidates all previous work)
```

---

## Best Practices

### 1. Prefer Collaborative Mode

**Why collaborative is recommended:**
- ✅ Quality control at each phase
- ✅ Inject domain expertise when needed
- ✅ Course-correct as insights emerge
- ✅ Ensure outputs match expectations

**When to use autonomous:**
- ✅ Repetitive analysis with clear requirements
- ✅ After calibrating AI's approach collaboratively
- ✅ Time-sensitive updates using established framework

### 2. Build Research in Phases

Don't try to do everything at once:

```
Phase 1: sector-overview (market context)
Phase 2: project-snapshot (subject overview)
Phase 3: Specialized deep-dives (based on Phase 1-2 findings)
Phase 4: Synthesis (investment thesis or recommendation)
```

### 3. Use Research Briefs for Complex Projects

Define clear objectives upfront:

```
You: Create research brief for Ethereum analysis.

AI: [Helps structure objectives, scope, approach]
    [Suggests workflow sequence]
```

### 4. Leverage AI for Research, You for Judgment

**AI excels at:** Data gathering, synthesis, pattern identification
**You excel at:** Strategic direction, domain expertise, investment judgment

```
You: Analyze competitive moat. I believe network effects matter more than tech specs.

AI: Understood. Weighting analysis: High priority on network effects, switching costs.
    [Executes with your strategic framing]
```

### 5. Iterate on Outputs

Don't settle for first draft:

```
AI: Report complete.

You: Competitive landscape needs more depth. Add emerging players.

AI: [Refines section]
    Better?

You: Good. Now add risk factors for each incumbent.

AI: [Final version saved]
```

### 6. Use QA Review for Critical Decisions

Validate important research:

```
You: @framework/agents/qa-specialist.yaml
     Review my Ethereum investment analysis.

AI: ⚠️ Potential bias: Heavy weight on developer ecosystem, value accrual link unclear.
    📊 Gap: Missing regulatory risk for DeFi platforms.
    ❓ Logic: Bull case assumes L2 success but doesn't model fragmentation risk.

    Recommend addressing before final decision.
```

### 7. Start Broad, Then Go Deep

**Recommended sequence:**
1. Sector-overview (context)
2. Project-snapshot (overview)
3. Specialized workflows (deep dives)
4. Synthesis (conclusions)

### 8. Use Autonomous for Monitoring

After initial collaborative research, use autonomous for updates:

```
Initial: Comprehensive Bitcoin analysis (collaborative, multi-session)
3 months later: Update with Q1 2025 data (autonomous, same framework)
```

---

## Troubleshooting

### AI doesn't understand framework
**Solution:** Explicitly tell AI to read framework files
```
You: Read @SKILL.md first, then help me with research.
```

### Outputs not following template
**Solution:** Remind AI to follow template
```
You: Execute sector-overview. Follow template.md exactly.
```

### Research lacks depth
**Solution:** Specify research depth
```
You: Run product-analysis with deep research depth.
     Follow methodology in framework/guides/research-methodology.md.
```

---

## Contributing

Found issues or have suggestions?
[Open an issue](https://github.com/kudodefi/cirf/issues) or submit a pull request.

---

## License

MIT License - See LICENSE file for details.

---

## Credits

**Created by:** [Kudō](https://x.com/kudodefi)
**Framework Version:** 1.0.0
**Last Updated:** 2025-02-09

---

**Ready to start researching?**

```
You: @research-analyst.yaml
     Help me research [your topic]
```


path: SECURITY.md
content: # Security & Permissions

## Overview

**CIRF (Crypto Interactive Research Framework)** is a prompt engineering framework designed for conducting comprehensive crypto market research with AI assistance.

---

## What is CIRF?

CIRF is **NOT a traditional software application** - it is a **prompt engineering framework** written entirely in natural language (YAML + Markdown files, zero code).

### Core Components

```
framework/
├── agents/*.yaml           # AI persona definitions
├── workflows/*/           # Research methodologies
│   ├── objectives.md      # What to research
│   └── template.md        # How to format output
└── components/*.md        # Execution protocols
```

**How it works:**
1. User activates an AI agent (e.g., Research Analyst)
2. AI reads YAML/Markdown files to understand persona and methodology
3. AI conducts research following structured instructions
4. AI generates formatted research outputs

**Think of it as:** Research SOPs (Standard Operating Procedures) that AI assistants follow to conduct professional-grade crypto analysis.

---

## Required Permissions

### 1. File System Access - READ

**Scope:**
```
framework/agents/*.yaml
framework/workflows/**/*.md
framework/components/*.md
framework/guides/*.md
framework/core-config.yaml
framework/_workspace.yaml
```

**Purpose:**
- Read AI agent persona definitions
- Read research methodology instructions
- Read output templates
- Load user preferences and configuration

**Why needed:**
CIRF is a prompt engineering framework - AI must read these instruction files to understand how to conduct research. This is the core functionality.

**Data flow:**
```
Framework files → AI reads → AI understands how to work
```

### 2. File System Access - WRITE

**Scope:**
```
workspaces/{project-id}/workspace.yaml
workspaces/{project-id}/documents/
workspaces/{project-id}/outputs/**/*.md
```

**Purpose:**
- Create isolated workspace for each research project
- Save research outputs (reports, analyses)
- Manage project configuration

**Why needed:**
Research framework must save outputs somewhere. All writes are confined to `workspaces/` directory within the project.

**Data flow:**
```
AI conducts research → Generates report → Saves to workspaces/{project}/outputs/
```

### 3. Network Access - WebSearch

**Purpose:**
- Search for current crypto market data
- Find news, trends, and market developments
- Gather information for research

**Example queries:**
- "Bitcoin ETF approval news 2024"
- "Ethereum scaling roadmap"
- "DeFi TVL statistics"

**Why needed:**
Cannot conduct crypto market research without access to current market data, news, and public information.

**Data flow:**
```
Research question → WebSearch → Public data → Synthesized into report
```

### 4. Network Access - WebFetch

**Purpose:**
- Fetch specific documents (whitepapers, documentation, blog posts)
- Read protocol specifications
- Access public blockchain data sources

**Example URLs:**
- Protocol documentation sites
- GitHub repositories (for technical analysis)
- Official project blogs
- Public blockchain explorers

**Why needed:**
Crypto research requires reading primary sources like whitepapers, technical docs, and official announcements.

**Data flow:**
```
Document URL → WebFetch → Content → Analyzed for research
```

---

## What CIRF Does NOT Do

### ❌ No Data Exfiltration
- Does not send user data to unauthorized external servers
- Does not transmit research outputs to third parties
- All outputs saved locally in `workspaces/` directory
- WebSearch/WebFetch only used for gathering PUBLIC research data

### ❌ No System Modifications
- Does not modify system files
- Does not install background processes
- Does not create persistence mechanisms
- Only operates within project directory

### ❌ No Remote Control
- Does not connect to command & control servers
- Does not receive remote commands
- Does not establish backdoors
- All instructions come from LOCAL framework files

### ❌ No Credential Harvesting
- Does not collect API keys, passwords, or credentials
- Does not access sensitive system data
- Does not scan for private information

### ❌ No Obfuscation
- All framework files are plain text (YAML/Markdown)
- Zero binary code
- Zero encryption or obfuscation
- Fully readable and modifiable by users

---

## Transparency & Verification

### All Code is Readable

Every instruction in CIRF is plain text:

```
framework/agents/research-analyst.yaml  ← AI persona definition (YAML)
framework/workflows/*/objectives.md     ← Research methodology (Markdown)
framework/components/*.md               ← Execution protocols (Markdown)
```

**Anyone can:**
- Read every instruction
- Understand what AI will do
- Modify behavior by editing files
- Verify no malicious content

### No Hidden Behavior

Unlike malware that:
- ❌ Hides code in encrypted binaries
- ❌ Obfuscates logic
- ❌ Uses stealth techniques

CIRF:
- ✅ All instructions in plain text
- ✅ Fully transparent design
- ✅ User-modifiable
- ✅ Open source

### Data Flow is Transparent

```
User request
  ↓
AI reads framework files (LOCAL)
  ↓
AI conducts research (WebSearch/WebFetch PUBLIC data)
  ↓
AI generates report
  ↓
Output saved to workspaces/ (LOCAL)
```

No step involves:
- Sending user data to unauthorized servers
- Hidden network connections
- Unauthorized data access

---

## Security Best Practices

### For Users

1. **Review framework files** before use
   - All instructions are readable YAML/Markdown
   - Verify no unauthorized endpoints
   - Check workspace paths are correct

2. **Monitor workspace directory**
   - All outputs saved to `workspaces/`
   - Review what's being saved
   - Delete projects when done

3. **Understand permissions**
   - Framework needs file read/write for workspace management
   - Framework needs network access for research
   - All permissions are for stated functionality

### For Platform Reviewers

1. **This is a prompt engineering framework**
   - "Prompt injection" detection is a FALSE POSITIVE
   - Instructions for AI are the framework's PURPOSE
   - Designed to GUIDE AI behavior, not HIJACK it

2. **Network access is essential**
   - Crypto research requires market data access
   - WebSearch/WebFetch used for PUBLIC data gathering
   - No unauthorized data transmission

3. **File operations are legitimate**
   - Workspace management for research projects
   - Similar to any productivity tool
   - All operations within project scope

4. **Verify by inspection**
   - All code is plain text YAML/Markdown
   - No binary executables
   - No obfuscation
   - Fully transparent design

---

## Framework Architecture

### Design Principles

1. **Transparency First**
   - All instructions in human-readable format
   - No hidden logic or obfuscated code
   - Users can audit everything

2. **Least Privilege**
   - Only accesses necessary files
   - Workspace writes confined to `workspaces/`
   - Network access only for research purposes

3. **User Control**
   - Users can modify any instruction
   - Users decide when to activate framework
   - Users control what research is conducted

4. **No Persistence**
   - Framework only active during AI session
   - No background processes
   - No system-level modifications

### Security by Design

```
┌─────────────────────────────────────────────┐
│  User activates framework                   │
│  (explicit action required)                 │
└────────────────┬────────────────────────────┘
                 ↓
┌─────────────────────────────────────────────┐
│  AI reads LOCAL framework files             │
│  (plain text YAML/Markdown)                 │
└────────────────┬────────────────────────────┘
                 ↓
┌─────────────────────────────────────────────┐
│  AI conducts research                       │
│  (WebSearch/WebFetch PUBLIC data)           │
└────────────────┬────────────────────────────┘
                 ↓
┌─────────────────────────────────────────────┐
│  AI saves output to workspaces/             │
│  (user's local directory)                   │
└─────────────────────────────────────────────┘
```

**No step involves:**
- Unauthorized data access
- Hidden network connections
- System modifications
- Remote control

---

## Conclusion

**CIRF is a legitimate research tool that may be flagged by automated security scanners due to its novel architecture as a prompt engineering framework.**

### Key Points

1. ✅ **Not malware** - Framework for AI-assisted crypto research
2. ✅ **Transparent design** - All code is readable plain text
3. ✅ **Required permissions** - File system and network access for research functionality
4. ✅ **No malicious behavior** - No data exfiltration, no system modifications, no remote control
5. ✅ **False positive** - Security scanners don't yet recognize prompt engineering frameworks as legitimate

### Safe to Use

CIRF is safe because:
- All behavior is transparent and documented
- No hidden functionality
- Users maintain full control
- Open source and auditable
- Designed for legitimate research purposes

---

**Framework Version:** 1.0.0
**Last Updated:** 2025-02-09
**Created by:** [Kudō](https://x.com/kudodefi)

---

## Appendix: Technical Details

### File Manifest

All framework files and their purposes:

```
framework/
├── core-config.yaml              # User preferences, workflow registry
├── _workspace.yaml               # Workspace template
├── agents/
│   ├── research-analyst.yaml     # Market research persona
│   ├── technology-analyst.yaml   # Technical analysis persona
│   ├── content-creator.yaml      # Content creation persona
│   └── qa-specialist.yaml        # Quality assurance persona
├── workflows/
│   ├── sector-overview/          # Market sector analysis
│   ├── competitive-analysis/     # Competitor comparison
│   ├── project-snapshot/         # Quick project overview
│   └── [14 more workflows]       # Various research methodologies
├── components/
│   ├── agent-init.md             # Agent initialization protocol
│   ├── workflow-init.md          # Workflow setup protocol
│   └── workflow-execution.md     # Research execution protocol
└── guides/
    ├── research-methodology.md   # How to conduct research
    ├── output-standards.md       # Output quality standards
    └── [more guides]             # Best practices and guides
```

**Total files:** ~100 YAML/Markdown files
**Total code:** 0 lines (all natural language)
**Binary files:** 0

### Network Endpoints

CIRF only accesses:
- Public web search (via AI assistant's WebSearch capability)
- Public websites (via AI assistant's WebFetch capability)
- No custom endpoints
- No proprietary servers
- No data collection services

### Data Storage

All data stored locally:
```
workspaces/
└── {project-id}/
    ├── workspace.yaml        # Project configuration
    ├── documents/            # User-provided source materials
    └── outputs/              # Generated research reports
```

**No cloud storage**
**No external databases**
**No telemetry**


path: framework/_workspace.yaml
content: # ═══════════════════════════════════════════════════════════════════════════
# CIRF - CRYPTO INTERACTIVE RESEARCH FRAMEWORK - WORKSPACE DEFINITION FILE
# ═══════════════════════════════════════════════════════════════════════════
# NAME: Workspace Template (_workspace)
# TYPE: Workspace Definition
# PURPOSE: Template for creating new workspace configurations with metadata, context, and state
# STRUCTURE: METADATA → CONTEXT → PATHS → SESSION_VARIABLES → WORKFLOWS
# USAGE: Copy template → Customize metadata & context → Load at workspace activation
# ═══════════════════════════════════════════════════════════════════════════

---
workspace:
  # ===========================================================================
  # METADATA - Workspace Identity
  # ===========================================================================
  metadata:
    id: "CHANGE_ME"                    # Example: bitcoin-analysis | content-hub | weekly-reports
    name: "CHANGE_ME"                  # Example: Bitcoin Analysis | Content Creation Hub
    description: "CHANGE_ME"           # Brief description of workspace purpose
    created: "YYYY-MM-DD"              # Auto-populated on creation
    last_updated: "YYYY-MM-DD"         # Auto-updated on each activity

  # ===========================================================================
  # CONTEXT - Workspace Purpose & Scope
  # ===========================================================================
  # PURPOSE: Define what this workspace is for and what it aims to achieve
  # FLEXIBILITY: Support any type of work - research, content, monitoring, etc.

  context:
    # What is this workspace about
    focus: "CHANGE_ME"                 # Example: Morpho Protocol | DeFi Market | Tech Tutorials

    # What are we trying to achieve
    objectives:
      - "CHANGE_ME"                    # Example: Investment decision | Create content | Monitor competitors

    # Scope and boundaries
    scope: "CHANGE_ME"                 # Example: Market + Fundamentals + Technical analysis

    # Additional context (free-form key-value pairs)
    notes: {}
      # Example for research:
      # subject_type: "DeFi Lending Protocol"
      # target_completion: "2025-Q1"
      #
      # Example for content creation:
      # content_type: "Educational tutorials"
      # target_audience: "Crypto beginners"
      #
      # Example for monitoring:
      # monitoring_frequency: "Weekly"
      # competitors: ["Aave", "Compound"]

  # ===========================================================================
  # SESSION VARIABLES - Active Configuration Snapshot
  # ===========================================================================
  # PURPOSE: Cached snapshot of session variables for quick context recovery
  # UPDATED: Auto-managed by agents at initialization (Step 2.7)
  #          Only updates when variables actually change (smart comparison)
  # USAGE: After conversation compact, agents restore context from this cache
  #
  # SOURCE OF TRUTH:
  #   - Core variables (8): ./framework/core-config.yaml
  #   - Workspace variables (8): workspace.yaml sections above
  #
  # NOTE: Framework paths use static references (./framework/...)
  #       Workspace paths constructed from workspace_id (./workspaces/{workspace_id}/...)

  session_variables:
    # Metadata (auto-managed, do not manually edit)
    _cached_at: ""                    # ISO timestamp of last cache update
    _cached_by_agent: ""              # Agent that last updated cache
    _core_config_version: ""          # Core-config version used
    _total_variables: 15              # Total session variables count

    # --- Core-config variables (7) ---
    # User settings
    user_name: ""
    user_date_format: ""
    user_currency: ""

    # Language settings
    language_communication: ""
    language_output: ""
    language_terminology: ""

    # Workflow registry
    workflows: {}                     # Workflow ID → path mapping

    # --- Workspace variables (8) ---
    # Workspace metadata
    workspace_id: ""
    workspace_name: ""
    workspace_description: ""
    workspace_created: ""
    workspace_last_updated: ""

    # Workspace context
    workspace_focus: ""
    workspace_objectives: []
    workspace_scope: ""

  # ===========================================================================
  # WORKFLOWS - Per-Workflow State & History
  # ===========================================================================
  # PURPOSE: Track workflow execution state and history
  # CREATION: New workflow section added on first execution
  # UPDATES: Modified at END of each workflow execution session
  # PERSISTENCE: Survives conversation compacts
  #
  # CONFIGURATION NOTE:
  # Workflow configuration (research_depth, output_type, paths, format) is read
  # from workflow.yaml on each execution. Only execution state and user
  # customization are stored here.
  #
  # IMPORTANT - RESEARCH DEPTH vs OUTPUT LENGTH:
  # • research_depth (HOW DEEPLY to research): quick 2-4h | standard 4-8h | deep 8-16h
  # • output_type (HOW to present): brief ~1500-2500 | standard ~3000-5000 | comprehensive ~5000-8000 words
  # These are INDEPENDENT: deep research can produce brief reports, and vice versa.
  # Override via execution_depth_override below (only affects research thoroughness, not output length).

  workflows: {}
    # -------------------------------------------------------------------------
    # STRUCTURE: Auto-created when workflow first executes
    # -------------------------------------------------------------------------
    #
    # {workflow_id}:
    #   metadata:
    #     workflow_id: "{workflow-id}"
    #     workflow_name: "{Workflow Name}"
    #     last_executed: ""             # ISO timestamp - updated at END
    #     latest_output: ""             # Path to latest output - updated at END
    #
    #   user_requirements:
    #     # User customization - set BEFORE execution, persists across runs
    #     execution_depth_override: ""  # Override default from workflow.yaml
    #     special_focus: []             # Topics/areas to emphasize
    #     avoid_topics: []              # Topics/areas to skip
    #     additional_instructions: ""   # Free-form instructions
    #     notes: {}                     # User-defined key-value pairs
    #
    #   executions:
    #     # Full execution history - appended at END of each run
    #     # - executed_at: "2025-01-15T10:30:00"
    #     #   executed_by_agent: "research-analyst"
    #     #   execution_depth: "standard"        # Actual depth used
    #     #   status: "completed"                # in_progress | completed | failed
    #     #   duration_minutes: 12
    #     #   output_primary: "./workspaces/morpho/outputs/sector-overview/sector-overview-2025-01-15.md"
    #     #   output_secondary: ""
    #     #   error_message: ""                  # If failed
    #     #   notes: ""                          # Execution notes
    #
    # -------------------------------------------------------------------------
    # EXAMPLE 1: Research workflow with custom requirements
    # -------------------------------------------------------------------------
    #
    # competitive_analysis:
    #   metadata:
    #     workflow_id: "competitive-analysis"
    #     workflow_name: "Competitive Analysis"
    #     last_executed: ""
    #     latest_output: ""
    #
    #   user_requirements:
    #     execution_depth_override: "deep"
    #     special_focus: ["Aave v3 comparison", "Technical moats"]
    #     avoid_topics: ["Historical price"]
    #     additional_instructions: "Focus on defensibility analysis"
    #     notes:
    #       competitors: ["Aave", "Compound", "Euler"]
    #       comparison_framework: "Porter's Five Forces"
    #
    #   executions: []

# =============================================================================
# END OF WORKSPACE CONFIG
# =============================================================================


path: framework/core-config.yaml
content: # ═══════════════════════════════════════════════════════════════════════════
# CIRF - CRYPTO INTERACTIVE RESEARCH FRAMEWORK - FRAMEWORK CONFIGURATION FILE
# ═══════════════════════════════════════════════════════════════════════════
# NAME: Core Configuration (core-config)
# TYPE: Framework Configuration
# PURPOSE: Centralized configuration for framework paths, settings, and workflow registry
# STRUCTURE: FRAMEWORK → USER → LANGUAGE → PATHS → WORKFLOWS
# USAGE: Load at initialization → Extract session variables → Reference throughout execution
# ═══════════════════════════════════════════════════════════════════════════

---

# =============================================================================
# FRAMEWORK INFORMATION
# =============================================================================

framework:
  name: "CIRF - Crypto Interactive Research Framework"
  version: "1.0.0"
  description: "Interactive crypto deep-research framework with human-AI collaboration for superior research outcomes"
  organization: "CIRF"
  author:
    name: "Kudō"
    social: "https://x.com/kudodefi"

# -----------------------------------------------------------------------------
# INITIALIZATION STATUS
# -----------------------------------------------------------------------------
# Tracks whether framework has been configured via framework-init workflow
# Research Analyst checks this field - if false, prompts user to run framework-init

status:
  initialized: false              # Set to true after framework-init completes
  initialized_at: ""              # Date of initialization (YYYY-MM-DD)
  initialized_by: ""              # Agent that performed initialization (research-analyst)

# =============================================================================
# USER SETTINGS
# =============================================================================
# Personalization settings for user interactions and formatting

user:
  name: ""
  date_format: "YYYY-MM-DD"
  currency: "$"

# =============================================================================
# LANGUAGE SETTINGS
# =============================================================================
# Controls agent communication and output generation languages

language:
  communication: "en"          # Agent communication language ("en" = English, "vi" = Vietnamese, "zh" = Chinese)
  output: "en"                 # Generated content language ("en" = English, "vi" = Vietnamese, "zh" = Chinese)
  terminology: "en"            # Preserve domain-specific terms (always English)

# =============================================================================
# DIRECTORY PATHS (Reference Only)
# =============================================================================
# All paths relative to framework root: /path/to/cirf/
# NOTE: These paths are for DOCUMENTATION and REFERENCE only.
#       Framework files use STATIC paths directly (e.g., "./framework/guides/...")
#       NOT loaded as session variables - reduces cognitive load and errors.

paths:
  # Framework resources (shared across all workspaces and agents)
  agents: "./framework/agents"                  # Agent definition files (YAML)
  workflows: "./framework/workflows"            # Workflow definitions (workflow.yaml, objectives.md, template.md)
  guides: "./framework/guides"                  # Shared guides (HOW to do things - universal techniques)
  components: "./framework/components"          # Shared components

  # Workspaces root (contains individual project workspaces)
  workspaces: "./workspaces"                    # User workspaces for projects and outputs
  workspace_template: "./framework/_workspace.yaml"  # Template for creating new workspaces

# =============================================================================
# WORKFLOWS REGISTRY
# =============================================================================
# Central registry mapping workflow IDs to their file paths
# Format: workflow-id: "path/to/workflow/folder"
# Each workflow folder contains: workflow.yaml, objectives.md, template.md

workflows:
  # Setup & Initialization - First-time setup and configuration
  framework-init: "./framework/workflows/framework-init"

  # Planning & Coordination - Research planning and prompt creation
  create-research-brief: "./framework/workflows/create-research-brief"

  # Market Intelligence - Market research, competitive analysis, and sector mapping
  sector-overview: "./framework/workflows/sector-overview"
  competitive-analysis: "./framework/workflows/competitive-analysis"
  sector-landscape: "./framework/workflows/sector-landscape"
  trend-analysis: "./framework/workflows/trend-analysis"

  # Project Fundamentals - Analyzing project components: product, team, investors, tokenomics
  product-analysis: "./framework/workflows/product-analysis"
  project-snapshot: "./framework/workflows/project-snapshot"
  team-and-investor-analysis: "./framework/workflows/team-and-investor-analysis"
  # investor-analysis: DEPRECATED - Merged into team-and-investor-analysis
  tokenomics-analysis: "./framework/workflows/tokenomics-analysis"
  traction-metrics: "./framework/workflows/traction-metrics"
  social-sentiment: "./framework/workflows/social-sentiment"

  # Technical Analysis - Technology evaluation
  technology-analysis: "./framework/workflows/technology-analysis"

  # Open Research - Flexible research on any topic
  open-research: "./framework/workflows/open-research"

  # Interactive Sessions - Brainstorming and ideation
  brainstorm: "./framework/workflows/brainstorm"

  # Quality Assurance - Reviewing and validating research outputs
  qa-review: "./framework/workflows/qa-review"

  # Content Creation - Creating multi-format content packages
  create-content: "./framework/workflows/create-content"

# =============================================================================
# END OF CONFIGURATION
# =============================================================================


path: framework/guides/content-style.md
content: # CONTENT STYLE

> **CIRF - CRYPTO INTERACTIVE RESEARCH FRAMEWORK - GUIDE**
> **TYPE:** Customizable Style Guide
> **PURPOSE:** Define writing style standards for content outputs
> **USAGE:** Defaults are institutional-grade crypto research style. Customize as needed.

---

<!--
============================================================================
HOW TO USE THIS TEMPLATE
============================================================================
1. Defaults reflect institutional-grade crypto research standards
2. Modify any section to match your preferences
3. Delete sections you don't need
4. Add new sections as needed

Agents will reference this guide when creating content.
============================================================================
-->

## BRAND VOICE

<!-- Define how your content should "sound" to readers -->

**Voice description:**
Professional, analytical, and objective. Research-first approach with data-driven insights. Authoritative yet accessible to sophisticated investors.

**Tone attributes:**
- Analytical - Lead with data and evidence
- Objective - Present balanced perspectives
- Precise - Use accurate terminology
- Substantive - Depth over surface-level takes
- Measured - Avoid hype and sensationalism

---

## TARGET AUDIENCE

<!-- Define who you're writing for -->

**Primary audience:**
Institutional investors, fund managers, and sophisticated crypto-native participants seeking actionable research.

**Audience knowledge level:**
Advanced - Familiar with crypto fundamentals, DeFi mechanics, on-chain analysis, and market dynamics. Expects professional-grade analysis without basic explanations.

---

## CONTENT STRUCTURE

<!-- Define how content should be organized -->

**Preferred formats:**
- [x] Research reports
- [x] Investment memos
- [x] Market analysis
- [x] Due diligence documents

**Structure preferences:**
- Executive summary upfront with key findings
- Thesis statement clearly articulated
- Evidence-based argumentation
- Risk assessment included
- Actionable conclusions

---

## WRITING GUIDELINES

<!-- Define specific writing rules -->

**Do:**
- Lead with the conclusion, then support with evidence
- Quantify claims with specific data points
- Cite sources for all external data
- Acknowledge uncertainty and limitations
- Include bull/bear case perspectives
- Use precise language and avoid ambiguity

**Don't:**
- Make price predictions or financial advice
- Use promotional or hype language
- Present speculation as fact
- Omit material risks or counterarguments
- Use vague qualifiers ("many", "significant", "soon")
- Include unverified information

---

## QUALITY STANDARDS

<!-- Define your quality checklist -->

- [ ] Thesis is clearly stated
- [ ] All claims supported by cited data
- [ ] Multiple sources cross-referenced
- [ ] Risks and counterarguments addressed
- [ ] Time-sensitive data dated
- [ ] No promotional language
- [ ] Conclusions are actionable
- [ ] Formatted for professional presentation

---

<!--
============================================================================
CUSTOMIZATION NOTES
============================================================================
- This file is referenced by the create-content workflow
- Defaults reflect institutional research standards
- Modify to match your organization's style guide
============================================================================
-->


path: framework/guides/brainstorming-guide.md
content: # BRAINSTORMING

> **CIRF - CRYPTO INTERACTIVE RESEARCH FRAMEWORK - GUIDE**
> **TYPE:** Execution Mode Guide
> **PURPOSE:** Define AI behaviors and techniques for interactive brainstorm sessions
> **USAGE:** Core reference for brainstorm workflow - apply throughout session

---

## PURPOSE

This guide defines how AI facilitates **Brainstorm Sessions** - interactive explorations where AI helps users discover insights, generate ideas, and reach clarity through structured ideation techniques.

**Core Philosophy:** AI facilitates thinking, user owns the discoveries. Lead with questions, not answers. The exploration IS the output.

---

## SESSION MODES

User selects mode at session start, or AI operates in `auto` mode.

| Mode | Purpose | AI Behavior |
|------|---------|-------------|
| `diverge` | Generate many ideas | Encourage quantity over quality, no judgment, build on everything |
| `converge` | Evaluate & prioritize | Critical assessment, ranking, trade-off analysis |
| `deep-dive` | Explore single idea deeply | Focused questioning, drill down, research supporting data |
| `challenge` | Devil's advocate | Counter-arguments, stress-test assumptions, find weaknesses |
| `auto` | AI selects dynamically | Adapt technique to conversation flow (recommended) |

**Mode Switching Signals:**
- User seems stuck → Suggest technique change
- Too many ideas, no direction → Suggest `converge`
- Surface-level discussion → Suggest `deep-dive`
- User too attached to one idea → Suggest `challenge`

---

## TECHNIQUES LIBRARY

### Exploration Techniques (Divergent Thinking)

#### Starbursting
Generate questions using 6 dimensions around a central idea:
- **Who** - Who is affected? Who decides? Who benefits?
- **What** - What is it? What does it do? What's the impact?
- **Where** - Where does this apply? Where are the limits?
- **When** - When is this relevant? When does it fail?
- **Why** - Why does this matter? Why now?
- **How** - How does it work? How do we implement?

**Use when:** Exploring a new idea comprehensively

#### Mind Mapping
Branch out from central concept:
- Start with core topic in center
- Add primary branches (main themes)
- Extend secondary branches (sub-ideas)
- Draw connections between distant branches

**Use when:** Organizing complex, interconnected ideas

#### SCAMPER
Transform existing ideas using 7 lenses:
- **S**ubstitute - What can we replace?
- **C**ombine - What can we merge?
- **A**dapt - What can we borrow from elsewhere?
- **M**odify - What can we change (bigger, smaller, different)?
- **P**ut to other uses - What else could this do?
- **E**liminate - What can we remove?
- **R**everse - What if we did the opposite?

**Use when:** Improving or iterating on existing concepts

#### How Might We (HMW)
Reframe problems as opportunity questions:
- "The problem is X" → "How might we [turn X into opportunity]?"
- Keep questions broad enough for creativity
- Keep questions narrow enough for actionability

**Use when:** Shifting from problem-focus to solution-focus

---

### Depth Techniques (Analytical Thinking)

#### Five Whys
Drill to root cause by asking "Why?" repeatedly:
1. State the observation
2. Ask "Why is this true?"
3. Answer, then ask "Why?" again
4. Repeat until reaching fundamental cause (usually 5 levels)
5. Address root cause, not symptoms

**Use when:** Understanding underlying causes, not just surface issues

#### First Principles Thinking
Break down to fundamental truths, then rebuild:
1. Identify current assumptions
2. Question each: "Is this actually true, or just believed?"
3. Strip away assumptions to reach base facts
4. Rebuild solution from base facts only

**Use when:** Breaking out of conventional thinking, innovation

#### Assumption Mapping
Surface and challenge hidden beliefs:
1. List all assumptions (explicit and implicit)
2. Categorize: Critical vs Nice-to-have
3. Rate confidence: High / Medium / Low
4. Challenge low-confidence critical assumptions first

**Use when:** Validating thinking before commitment

---

### Evaluation Techniques (Convergent Thinking)

#### Six Thinking Hats
Examine from 6 perspectives sequentially:
- 🎩 **White** - Facts only. What do we know? What data exists?
- ❤️ **Red** - Emotions. How do we feel? Gut reactions?
- ⚫ **Black** - Risks. What could go wrong? Weaknesses?
- 💛 **Yellow** - Benefits. What's good? Opportunities?
- 💚 **Green** - Creativity. New ideas? Alternatives?
- 🔵 **Blue** - Process. What's next? How to decide?

**Use when:** Balanced evaluation without argument

#### Pros / Cons / Risks Matrix
Structured evaluation:
| Aspect | Pros | Cons | Risks |
|--------|------|------|-------|
| {Dimension 1} | + | - | ⚠️ |
| {Dimension 2} | + | - | ⚠️ |

**Use when:** Comparing options or evaluating single idea

#### Impact-Effort Matrix
Prioritization grid:
```
High Impact │ Quick Wins    │ Major Projects
            │ (Do First)    │ (Plan Carefully)
────────────┼───────────────┼─────────────────
Low Impact  │ Fill-ins      │ Thankless Tasks
            │ (Do If Time)  │ (Avoid)
            └───────────────┴─────────────────
              Low Effort      High Effort
```

**Use when:** Prioritizing among multiple ideas

---

## AI BEHAVIORS

### Interaction Principles

1. **Questions over Statements**
   - Lead by asking, not telling
   - "What if..." instead of "You should..."
   - Help user discover, don't deliver conclusions

2. **Build, Don't Block**
   - "Yes, and..." instead of "No, but..."
   - Add to ideas before critiquing
   - Save criticism for `converge` or `challenge` modes

3. **Reflect Understanding**
   - Paraphrase to confirm: "So you're saying..."
   - Surface implicit assumptions: "It sounds like you assume..."
   - Connect to earlier points: "This relates to what you said about..."

4. **Research Proactively**
   - When facts would help, use tools (WebSearch, etc.)
   - Don't wait for user to ask
   - Present findings as input, not conclusions

### Probing Question Templates

**Clarifying:**
- "When you say [X], what do you mean exactly?"
- "Can you give a specific example?"
- "How is this different from [Y]?"

**Challenging:**
- "What makes you believe [X] is true?"
- "What if [assumption] is wrong?"
- "Who would disagree? What would they say?"

**Expanding:**
- "What other perspectives are there?"
- "What happens if we reverse the approach?"
- "If there were no constraint [X], what would you do?"

**Deepening:**
- "Why does this matter?"
- "What's behind that?"
- "What's the real root cause?"

**Connecting:**
- "How does this relate to [earlier point]?"
- "What patterns do you see?"
- "Is there a common theme?"

---

## MILESTONE DETECTION

### When to Suggest Save

**Trigger save suggestion when ANY of:**
- 3+ significant insights accumulated
- Major breakthrough or "aha moment"
- Direction shift (pivoting to new angle)
- ~15-20 minutes of continuous exploration
- User expresses satisfaction with a thread
- Before switching to very different topic

### Save Point Format

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📝 MILESTONE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Insights so far:
• {Insight 1}
• {Insight 2}
• {Insight 3}

Options:
1. 💾 Save & continue
2. ➡️ Continue (no save)
3. 🔄 Switch technique
4. ✅ End session

Select: ___
```

### What to Capture in Save

- **Key insights** - Main discoveries with context
- **Ideas list** - All ideas generated (even rough ones)
- **Assumptions challenged** - What beliefs were questioned
- **Open questions** - Unanswered threads
- **Next steps** - Actionable follow-ups

---

## SESSION LIFECYCLE

### Phase 1: FRAME

**Goal:** Deeply understand what user wants to explore

**Actions:**
1. User presents topic/problem
2. AI clarifies:
   - "What's the core question?"
   - "Are there any constraints?"
   - "What have you already thought about this topic?"
   - "What does success look like?"
3. AI summarizes understanding
4. AI suggests starting technique/mode
5. User confirms or adjusts

**Output:** Clear framing before exploration begins

---

### Phase 2: EXPLORE (Iterative Loop)

**Goal:** Generate insights through guided exploration

**Loop:**
```
User input
    ↓
AI responds:
  • Reflect understanding
  • Ask probing questions (from templates above)
  • Offer alternative perspectives
  • Apply current technique
  • Research if facts needed
    ↓
Check: Milestone reached?
  YES → Suggest save (see format above)
  NO → Continue
    ↓
Check: User stuck or wants change?
  YES → Suggest technique switch
  NO → Continue
    ↓
Check: User wants to end?
  YES → Go to Phase 3
  NO → Loop back
```

**Key behaviors:**
- Stay in current mode unless signal to switch
- Summarize periodically (every 5-7 exchanges)
- Research proactively when facts would help
- Track insights in memory for later synthesis

---

### Phase 3: SYNTHESIZE

**Goal:** Organize discoveries into coherent summary

**Actions:**
1. Announce session closing
2. Present organized summary:
   - Key discoveries by theme
   - Ideas generated (full list)
   - Assumptions challenged
   - Open questions remaining
   - Suggested next steps
3. Offer to save summary
4. Write file if requested

**Output:** Coherent synthesis of session value

---

## TECHNIQUE SWITCHING

### When to Suggest Switch

| Signal | Current State | Suggested Switch |
|--------|---------------|------------------|
| Too many ideas, no direction | `diverge` | → `converge` |
| Surface-level, no depth | Any | → `deep-dive` |
| User too attached to one idea | Any | → `challenge` |
| Stuck, no new ideas | Any | → Different exploration technique |
| Analysis paralysis | `converge` | → `diverge` (generate more options) |

### Switch Prompt Format

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔄 TECHNIQUE SUGGESTION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Current: {current technique/mode}
Suggestion: {new technique} because {reason}

Example: "Try Five Whys to dig deeper into why
         this assumption feels true."

Switch? (y/n): ___
```

---

## ANTI-PATTERNS TO AVOID

| Anti-Pattern | Why It's Bad | Instead Do |
|--------------|--------------|------------|
| Lecturing | User doesn't discover | Ask questions |
| Judging early | Kills creativity | Save judgment for `converge` |
| Single-tracking | Misses alternatives | Explore multiple angles |
| Over-researching | Disrupts flow | Research briefly, return to dialogue |
| Ignoring emotions | Misses important signals | Acknowledge feelings, then explore |
| Forcing technique | Feels mechanical | Adapt naturally to conversation |

---

**End of Brainstorming Guide**

*Apply throughout brainstorm sessions. Adapt techniques to user's thinking style. The goal is user insight, not AI performance.*


path: framework/guides/_guide.md
content: # {GUIDE NAME}

> **CIRF - CRYPTO INTERACTIVE RESEARCH FRAMEWORK - GUIDE**
> **TYPE:** {Methodology Guide | Execution Mode Guide | Standard Guide | Specialized Guide}
> **PURPOSE:** {One-line description - focus on WHAT, not WHEN}
> **USAGE:** {When and how to apply this guide}

---

## [CORE SECTION TITLE]
<!-- Common titles: CORE PRINCIPLES | CORE PHILOSOPHY | WHEN TO USE | KEY CONCEPTS -->

[Core concepts, principles, or philosophical foundation]

### [Subsection]

[Content]

---

## [MAIN CONTENT SECTIONS]
<!-- Structure varies by guide type - use what fits your needs -->

### [Section Title]

[Content]

---

## [ADDITIONAL SECTIONS]
<!-- Optional sections as needed - examples:
     EDGE CASE HANDLING | DECISION SUMMARY | VALIDATION CHECKLIST
     QUICK REFERENCE | COMPARISON TABLE | SUMMARY
-->

### [Section Title]

[Content]

---

**End of [Guide Name] Guide**


path: framework/workflows/create-content/templates/tiktok-script.md
content: <!--
TIKTOK SCRIPT TEMPLATE
INSTRUCTIONS:
- Recall & Apply: {content-style} (./framework/guides/content-style.md)
- Validation: See create-content/objectives.md

PURPOSE: Transform research into short-form TikTok video script
OUTPUT: Table-format script with time, script, and visual/audio cues

LANGUAGE:
- Output: Follow core-config.yaml → language.output
- Terminology: English (crypto domain terms always preserved)

CONTENT CONSTRAINTS:
- Duration (±30%): 30s - 3min
- Format: Table (Time | Script | Visual/Audio)
- Structure: Hook → Context → Main content → CTA
-->

# {Video Title}

| Time | Script | Visual/Audio |
|------|--------|--------------|
| 0:00-0:03 | {Hook} | {Visual cue} |
| 0:03-0:10 | {Context} | {Visual cue} |
| 0:10-0:XX | {Main content} | {Visual cue} |
| 0:XX-END | {CTA} | {Visual cue} |

---

**Caption:** {Caption text}

**Hashtags:** #tag1 #tag2 #tag3


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