Implement KNU token distribution system with v6.0 nomenclature compliance

config/tokenomics/README.md

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+# KNU Token Distribution System
+
+## Overview
+
+The **KNU Token Distribution System** distributes token rewards to contributors based on a weighted combination of **predicted effort** and **measured impact** (including spillover effects to adjacent KNOTs).
+
+This system implements the distribution formula:
+
+```
+w_i = α·Ê_i + (1-α)·Î_i
+T_i = P_k · w_i
+```
+
+Where:
+- `P_k` = Prize pool (total KNU tokens for the KNOT)
+- `E_i` = Predicted effort (normalized)
+- `Ê_i` = Normalized effort: `E_i / Σ E_i`
+- `I_i` = Effective impact: `ΔR_k,i + λ·S_i`
+- `Î_i` = Normalized impact: `I_i / Σ I_i`
+- `S_i` = Spillover: `Σ(a_k→j · ΔR_j,i)` for adjacent KNOTs
+- `ΔR_k,i` = Direct residue reduction attributed to KNU
+- `a_k→j` = Adjacency weight between KNOTs
+
+**Default Parameters:**
+- `α = 0.30` (30% effort, 70% impact)
+- `λ = 0.50` (spillover worth 50% of direct impact)
+
+## Files
+
+### Configuration
+- **`config/tokenomics/knu_distribution.yaml`** - Central configuration for distribution parameters, eligibility criteria, KNOT pools, and adjacency graph
+
+### Schema
+- **`schemas/knu_reward_record.schema.json`** - JSON Schema for reward record validation
+
+### Templates
+- **`templates/knu-reward-ledger.csv`** - CSV template for tracking KNU rewards
+
+### Scripts
+- **`scripts/knu_token_distribution.py`** - Main distribution calculator with CLI commands
+
+## Usage
+
+### 1. Prepare KNU Entries
+
+Create a CSV file with your KNU entries following the template in `templates/knu-reward-ledger.csv`:
+
+```csv
+knot_id,knu_id,owner,E_pred,dR_primary,dR_adj_sum,status,artifacts,validated_by,validated_at,weight,tokens_awarded
+K06,KNU-K06-00-001,CM WG,5.0,30.0,10.0,merged,schema_v2.json;migration_guide.md,Lead Architect,2026-01-10T12:00:00Z,,
+K06,KNU-K06-00-002,AI/ML Engineering,3.0,15.0,5.0,accepted,trace_validator.py,QA Team,2026-01-10T14:00:00Z,,
+```
+
+**Field Descriptions:**
+- `knot_id` - KNOT identifier (K01-K14)
+- `knu_id` - Unique KNU identifier (format: `KNU-{KNOT}-{PARTITION}-{SEQUENCE}`)
+- `owner` - Person or team responsible for resolution
+- `E_pred` - Predicted effort score (arbitrary units, normalized during calculation)
+- `dR_primary` - Direct residue reduction in primary KNOT (0-100%)
+- `dR_adj_sum` - Sum of residue reductions in adjacent KNOTs (0-100%)
+- `status` - Current status (`draft`, `submitted`, `in_review`, `accepted`, `merged`, `rejected`)
+- `artifacts` - Semicolon-separated list of artifact references
+- `validated_by` - Name of validator who confirmed impact
+- `validated_at` - Validation timestamp (ISO 8601 format)
+- `weight` - Calculated distribution weight (filled by script)
+- `tokens_awarded` - Number of tokens awarded (filled by script)
+
+### 2. Distribute Tokens
+
+Run the distribution command:
+
+```bash
+python scripts/knu_token_distribution.py distribute \
+  --knot K06 \
+  --pool 1000 \
+  --input knu_entries.csv \
+  --output knu_rewards.csv
+```
+
+**Options:**
+- `--knot` (required) - KNOT ID (e.g., K06)
+- `--pool` (optional) - Pool amount (overrides config default)
+- `--input` (required) - Input CSV file with KNU entries
+- `--output` (optional) - Output CSV file (defaults to overwriting input)
+- `--alpha` (optional) - Effort weight (0.0-1.0, default: 0.30)
+- `--lambda` (optional) - Spillover multiplier (0.0-1.0, default: 0.50)
+
+**Example with custom parameters:**
+```bash
+python scripts/knu_token_distribution.py distribute \
+  --knot K06 \
+  --pool 1000 \
+  --input knu_entries.csv \
+  --alpha 0.25 \
+  --lambda 0.60
+```
+
+### 3. Generate Distribution Report
+
+Generate a JSON report with detailed distribution information:
+
+```bash
+python scripts/knu_token_distribution.py report \
+  --knot K06 \
+  --input knu_rewards.csv \
+  --output distribution_report.json
+```
+
+**Report Contents:**
+- Distribution timestamp
+- KNOT ID and pool configuration
+- Distribution parameters (α, λ)
+- All KNU entries with calculated weights and token awards
+- Total tokens distributed
+- Number of eligible entries
+
+### 4. Validate KNU Eligibility
+
+Check if a specific KNU entry qualifies for rewards:
+
+```bash
+python scripts/knu_token_distribution.py validate \
+  --knot K06 \
+  --knu KNU-K06-00-001 \
+  --input knu_entries.csv
+```
+
+**Eligibility Criteria (default):**
+- Status must be `accepted` or `merged`
+- Must have at least one artifact
+- Must be validated by an authorized reviewer
+
+## Example Calculation
+
+Given the reference example from the requirements:
+
+**Input:**
+- Pool: 1000 tokens
+- α = 0.30, λ = 0.50
+- KNU1: E=5, ΔR_k=30, S=10
+- KNU2: E=3, ΔR_k=15, S=5
+- KNU3: E=2, ΔR_k=5, S=0
+
+**Calculation:**
+
+1. **Effective Impact:** `I_i = ΔR_k,i + λ·S_i`
+   - KNU1: 30 + 0.50×10 = 35
+   - KNU2: 15 + 0.50×5 = 17.5
+   - KNU3: 5 + 0.50×0 = 5
+
+2. **Normalized Effort:** `Ê_i = E_i / ΣE_i`
+   - E_total = 10
+   - KNU1: 5/10 = 0.50
+   - KNU2: 3/10 = 0.30
+   - KNU3: 2/10 = 0.20
+
+3. **Normalized Impact:** `Î_i = I_i / ΣI_i`
+   - I_total = 57.5
+   - KNU1: 35/57.5 = 0.6087
+   - KNU2: 17.5/57.5 = 0.3043
+   - KNU3: 5/57.5 = 0.0870
+
+4. **Weights:** `w_i = α·Ê_i + (1-α)·Î_i`
+   - KNU1: 0.30×0.50 + 0.70×0.6087 = 0.5761
+   - KNU2: 0.30×0.30 + 0.70×0.3043 = 0.3030
+   - KNU3: 0.30×0.20 + 0.70×0.0870 = 0.1209
+
+5. **Token Awards:** `T_i = P_k · w_i`
+   - KNU1: 1000 × 0.5761 = **576 tokens**
+   - KNU2: 1000 × 0.3030 = **303 tokens**
+   - KNU3: 1000 × 0.1209 = **121 tokens**
+
+## Configuration
+
+### Adjusting Distribution Parameters
+
+Edit `config/tokenomics/knu_distribution.yaml`:
+
+```yaml
+parameters:
+  alpha: 0.30           # Effort weight (0.0-1.0)
+  lambda_spillover: 0.50 # Spillover multiplier (0.0-1.0)
+```
+
+### Modifying Eligibility Criteria
+
+```yaml
+eligibility:
+  required_status:
+    - "accepted"
+    - "merged"
+  require_artifacts: true
+  require_validation: true
+```
+
+### Updating KNOT Pools
+
+```yaml
+knot_pools:
+  K06:
+    base_pool: 1000
+    description: "Data governance SSOT schemas identifiers"
+```
+
+### Defining Adjacency Relationships
+
+The adjacency graph defines how impact in one KNOT spills over to adjacent KNOTs:
+
+```yaml
+adjacency_graph:
+  K06:
+    K01: 0.3  # Data governance → Certification
+    K05: 0.3  # Data governance → Model fidelity
+    K07: 0.4  # Data governance → AI/ML
+    K08: 0.5  # Data governance → DPP
+```
+
+## Integration with Existing Code
+
+The token distribution system integrates with existing AMPEL360 Space-T components:
+
+### KNOT-AoR Mapping
+- Imports `KNOT_AOR_MAPPING` from `scripts/knot_aor_mapping.py`
+- Uses KNOT definitions to derive adjacency relationships
+
+### NKU Scoring Patterns
+- Follows CSV handling patterns from `scripts/nku_scoring.py`
+- Implements CSV injection prevention
+- Uses similar dataclass patterns and CLI structure
+
+## Security Considerations
+
+### CSV Injection Prevention
+All CSV fields are sanitized to prevent injection attacks:
+- Dangerous characters (`=`, `+`, `-`, `@`, tabs, newlines) are prefixed with a single quote
+- Embedded newlines are replaced with spaces
+
+### Input Validation
+- KNOT IDs validated against pattern `^K(0[1-9]|1[0-4])$`
+- KNU IDs validated against pattern `^KNU-K(0[1-9]|1[0-4])-[A-Z0-9]{2,6}-\d{3}$`
+- Numeric values constrained (effort ≥ 0, residue 0-100%)
+- Required fields enforced by JSON Schema
+
+## Troubleshooting
+
+### Error: "No eligible KNU entries"
+- Check that KNU entries have status `accepted` or `merged`
+- Verify artifacts are provided
+- Ensure entries are validated by an authorized reviewer
+
+### Error: "No pool configuration found"
+- Check that the KNOT ID exists in `config/tokenomics/knu_distribution.yaml`
+- Verify KNOT ID format (K01-K14)
+
+### Calculation discrepancies
+- Verify input data is correct (E_pred, dR_primary, dR_adj_sum)
+- Check that spillover values (dR_adj_sum) are pre-calculated correctly
+- Ensure normalization is working (weights should sum to 1.0)
+
+## References
+
+- **Problem Statement**: See issue requirements for full specification
+- **KNOT Governance**: K01-K14 definitions with uncertainty focus
+- **NKU Scoring**: `scripts/nku_scoring.py` for related uncertainty tracking
+- **KNOT-AoR Mapping**: `scripts/knot_aor_mapping.py` for KNOT relationships