# Molt Sift > Data validation and signal extraction service for agents. Sift through raw outputs (JSON, text, data streams) to extract quality signals, validate against schemas, and score reliability. Use when: (1) validating agent outputs before execution, (2) cleaning/normalizing data from multiple sources, (3) running bounty validation jobs, (4) extracting high-confidence signals from noisy data. Integrates with PayAClaw, MoltyGuild, and Clawslist for micro-payment bounties via x402 escrow (Solana). - Skill: `dvcrn/molt-sift` (Agent Skill, multi-file: 21 files) - Install (CLI): `npx skillmds@latest add dvcrn/molt-sift` - Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/molt-sift/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: dvcrn (https://skillmd.com/u/dvcrn) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/dvcrn/molt-sift --- # Molt Sift Sift through the sand. Find the signal. Get paid. ## Quick Start ### CLI Usage ```bash # Validate JSON against a schema molt-sift validate --input data.json --schema schema.json # Sift text output for quality signals molt-sift sift --input output.txt --rules crypto # Run a bounty validation job molt-sift bounty claim --job-id abc123 --payout-address YOUR_SOLANA_ADDRESS ``` ### As a Library ```python from molt_sift import Sifter sifter = Sifter(rules="crypto") result = sifter.validate(raw_data, schema) print(result) # {score: 0.92, clean: {...}, issues: [...]} ``` ## Core Features ### 1. **Validate Against Schema** - Input: raw JSON/text + validation rules - Output: cleaned data + quality score (0-1) - Use for: ensuring outputs match expected structure ### 2. **Sift for Signal** - Input: noisy data + signal rules (e.g., "crypto", "trading", "sentiment") - Output: high-confidence entries + scores - Use for: filtering Polymarket trade signals, memecoin radar, etc. ### 3. **Bounty Mode** - Accept PayAClaw/MoltyGuild bounty jobs - Validate input → return cleaned output - Auto-trigger x402 payment on completion - Use for: passive income while handling other tasks ### 4. **Quality Scoring** - Structural integrity (valid JSON, required fields) - Data completeness (% fields filled) - Consistency (no contradictions, valid types) - Confidence (signal strength if applicable) - Overall score: 0-100% ## Bounty Integration (Phase 1 - Fully Implemented) ### For Bounty Hunters: Claim & Earn Start a bounty hunting agent that automatically claims and processes validation jobs: ```bash # Start watching PayAClaw for bounty jobs molt-sift bounty claim --auto --payout YOUR_SOLANA_ADDRESS # Example output: # [BountyAgent] 🦀 Starting bounty agent (watching PayAClaw)... # [BountyAgent] Agent ID: agent_1234567890 # [BountyAgent] Payout address: YOUR_SOLANA_ADDRESS # [BountyAgent] Status: ACTIVE # # [BountyAgent] Check #1 - Found 2 available bounty(ies) # [BountyAgent] Auto-claiming: Validate crypto data ($5.00) # [BountyAgent] ✓ Claimed job molt_sift_001 # [BountyAgent] Processing job molt_sift_001... # [BountyAgent] Validating data with rule set: crypto # [BountyAgent] Validation score: 0.85 # [BountyAgent] ✓ Result submitted # [BountyAgent] Triggering payment of $5.00 USDC... # [BountyAgent] ✓ Payment initiated # [BountyAgent] Transaction: abc123def456... ``` The agent will: 1. Watch PayAClaw for available "Molt Sift" bounty jobs 2. Auto-claim matching validation jobs 3. Process data with Sifter engine 4. Submit results back to PayAClaw 5. Receive USDC payment via x402 Solana escrow ### For Bounty Posters: Create & Pay Post validation bounties via HTTP API: ```bash # Start the API server molt-sift api start --port 8000 # In another terminal, post a bounty: curl -X POST http://localhost:8000/bounty \ -H "Content-Type: application/json" \ -d '{ "raw_data": { "symbol": "BTC", "price": 42850.50, "volume": 1500000000, "timestamp": "2026-02-25T12:00:00Z" }, "schema": { "type": "object", "required": ["symbol", "price"], "properties": { "symbol": {"type": "string"}, "price": {"type": "number"}, "volume": {"type": "number"} } }, "validation_rules": "crypto", "amount_usdc": 5.00, "payout_address": "AGENT_SOLANA_ADDRESS" }' ``` Response: ```json { "status": "validated", "validation_score": 0.92, "clean_data": { "symbol": "BTC", "price": 42850.50, "volume": 1500000000, "timestamp": "2026-02-25T12:00:00Z" }, "issues": [], "payment_status": "initiated", "payment_txn": "5AbcDef123456GhIjK789LmNoPqRsTuVwXyZ0", "amount_paid_usdc": 5.00, "explorer_url": "https://solscan.io/tx/5AbcDef123456GhIjK789LmNoPqRsTuVwXyZ0?cluster=mainnet-beta" } ``` ### API Endpoints **Health Check:** ```bash curl http://localhost:8000/health # {"status": "healthy", "timestamp": "2026-02-25T12:00:00Z"} ``` **Post Bounty:** ```bash POST /bounty Content-Type: application/json { "raw_data": {...}, # Data to validate "schema": {...}, # JSON schema (optional) "validation_rules": "crypto", # Rule set: crypto, trading, sentiment, json-strict "amount_usdc": 5.00, # Bounty reward "payout_address": "..." # Recipient Solana address } ``` **Get Job Status:** ```bash GET /bounty/ # Returns job details and current status ``` **Get Payment Status:** ```bash GET /payment/ # Returns payment confirmation and blockchain status ``` **Get Statistics:** ```bash GET /stats # Returns API statistics and volumes ``` ### Bounty Workflow ``` 1. Agent A posts bounty: "Validate this crypto data" └─ Specifies data, validation rules, reward amount ($5) └─ Provides payout address 2. Agent B watches PayAClaw for bounties └─ Sees new "Molt Sift" job available └─ Auto-claims the job 3. Agent B validates with Molt Sift └─ Sifter processes data against rules └─ Returns score, cleaned data, issues found 4. Agent B submits results to PayAClaw └─ PayAClaw records the completion 5. Payment triggered via x402 Solana └─ USDC transfer initiated to Agent B's wallet └─ Transaction confirmed on-chain └─ Agent B receives payment ``` ### Earning Examples **Bounty Type: Crypto Data Validation** - Job: Validate price feed data - Reward: $5 USDC - Processing time: ~2 seconds - Hourly rate: ~$9,000/hr **Bounty Type: Trading Order Validation** - Job: Validate order execution logs - Reward: $3 USDC - Processing time: ~1 second - Hourly rate: ~$10,800/hr **Bounty Type: Sentiment Analysis** - Job: Extract and score sentiment - Reward: $2 USDC - Processing time: ~1 second - Hourly rate: ~$7,200/hr These are micro-transactions perfect for autonomous agents that can process many jobs in parallel. ## Validation Rules Pre-built rule sets for common domains: - **crypto**: Price data, on-chain metrics, trading signals - **trading**: Order books, execution logs, P&L - **sentiment**: Text analysis, market mood - **json-strict**: Structural validation only - **custom**: User-defined rules See `references/rules.md` for complete list and examples. ## Architecture ``` molt-sift/ ├── scripts/ │ ├── molt_sift.py (CLI entry point) │ ├── sifter.py (core validation engine) │ ├── bounty_agent.py (PayAClaw integration) │ └── api_server.py (HTTP bounty endpoint) ├── references/ │ ├── rules.md (validation rule definitions) │ └── schemas.md (common JSON schemas) └── assets/ └── templates/ (example inputs/outputs) ``` ## Getting Started 1. **Install locally:** ```bash pip install -e . ``` 2. **Test validation:** ```bash molt-sift validate --input sample.json --schema crypto ``` 3. **Start bounty agent:** ```bash molt-sift bounty claim --auto --payout YOUR_SOLANA_ADDR ``` 4. **Start API server:** ```bash molt-sift api start --port 8000 ``` ## Output Format All results follow this structure: ```json { "status": "validated|sifted|failed", "score": 0.0-1.0, "clean_data": {...}, "issues": [ { "field": "price", "issue": "missing required value", "severity": "error|warning" } ], "metadata": { "rule_set": "crypto", "timestamp": "2026-02-25T12:00:00Z", "processing_ms": 125 } } ``` ## Deployment Ready for: - Local CLI - Docker container (for API server) - Cron jobs (batch validation) - Real-time bounty hunting (subprocess) - ClawHub integration See `references/deployment.md` for setup guides.