cirfInteractive crypto deep-research framework with human-AI collaboration for superior research outcomes
Install via ClawdBot CLI:
clawdbot install kudodefi/cirfThis 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.
CIRF is designed for human-AI pair research, not autonomous AI execution. Your role is to:
COLLABORATIVE MODE (Default & Recommended)
AUTONOMOUS MODE (Optional)
framework/
βββ core-config.yaml # User preferences, workflow registry
βββ agents/ # Agent persona definitions
β βββ research-analyst.yaml
β βββ technology-analyst.yaml
β βββ content-creator.yaml
β βββ qa-specialist.yaml
βββ workflows/ # Research workflows
β βββ {workflow-id}/
β βββ workflow.yaml # Workflow config
β βββ objectives.md # Research methodology
β βββ template.md # Output format
βββ components/ # Shared execution protocols
β βββ agent-init.md
β βββ workflow-init.md
β βββ workflow-execution.md
βββ guides/ # Research methodologies
workspaces/ # User research projects
βββ {project-id}/
βββ workspace.yaml # Project config
βββ documents/ # Source materials
βββ outputs/ # Research deliverables
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:
framework/agents/research-analyst.yaml to embody the agent personaframework/core-config.yaml for user preferencesScenario 2: Agent Name Shorthand
Human: @Research-Analyst - Analyze Bitcoin's market position.
What to do:
framework/agents/research-analyst.yamlframework/agents/research-analyst.yaml and framework/core-config.yamlScenario 3: Natural Language Request
Human: I want to analyze Ethereum's competitive landscape.
What to do:
framework/core-config.yaml for available workflowsframework/agents/{agent-id}.yamlScenario 4: Orchestrator Mode
Human: Read @SKILL.md and act as orchestrator.
I want comprehensive Ethereum analysis.
What to do:
framework/core-config.yaml for workflows and preferencesScenario 5: Direct Workflow Request
Human: Run sector-overview for DeFi lending.
What to do:
framework/agents/research-analyst.yamlframework/core-config.yamlframework/workflows/sector-overview/Once you've read the appropriate files, follow the instructions contained within them:
agent-init.md - Agent initialization stepsworkflow-init.md - Workflow initialization stepsworkflow-execution.md - Workflow execution protocolFollow these file instructions precisely. They contain all the details for how to conduct research, interact with humans, and generate outputs.
Your expertise: Market intelligence, fundamentals, investment synthesis
Your workflows:
Your approach:
Your expertise: Architecture, security, technical evaluation
Your workflows:
Your approach:
Your expertise: Research-to-content transformation
Your workflows:
Your approach:
Your expertise: Quality validation, critical review
Your workflows:
Your approach:
Each project gets isolated workspace:
workspaces/{project-id}/
βββ workspace.yaml # Project configuration
βββ documents/ # Source materials (whitepapers, references)
βββ outputs/ # Research deliverables
βββ {workflow-id}/
βββ {workflow-id}-{date}.md
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
Update workspace.yaml with:
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}"
AI: I'm not confident I understand {workflow-id} requirements correctly.
From objectives.md, I see:
β’ Research question A
β’ Research question B
But I'm unclear on:
β’ [Specific uncertainty]
Can you clarify what you'd like me to focus on?
AI: I'm unable to find reliable data on {topic}.
Attempted sources:
β’ {Source 1} - No data
β’ {Source 2} - Outdated (2022)
Should I:
a) Note data gap and continue
b) Use best available proxy
c) Pause until data available
AI: This research scope is quite extensive and may take 10+ hours.
Recommend breaking into phases:
β’ Phase 1: Core analysis (4h)
β’ Phase 2: Extended analysis (4h)
β’ Phase 3: Synthesis (2h)
Start with Phase 1 and evaluate before committing to full scope?
When activated, read files in this order:
framework/agents/{agent-id}.yamlframework/core-config.yamlframework/workflows/{workflow-id}/workflow.yamlframework/workflows/{workflow-id}/objectives.mdframework/workflows/{workflow-id}/template.mdframework/components/ (agent-init, workflow-init, workflow-execution)workspaces/{project-id}/workspace.yaml (if exists)Framework Version: 1.0.0
Last Updated: 2025-02-09
Created by: KudΕ
AI Usage Analysis
Analysis is being generated⦠refresh in a few seconds.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready.
Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection