narya-proofs
Mechanically verified proofs from Narya event logs. Verifies queue consistency, replay determinism, non-leakage, and GF(3) conservation. Use for proving system invariants, audit trails, or formal verification of event-sourced systems.
Best use case
narya-proofs is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Mechanically verified proofs from Narya event logs. Verifies queue consistency, replay determinism, non-leakage, and GF(3) conservation. Use for proving system invariants, audit trails, or formal verification of event-sourced systems.
Teams using narya-proofs should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/narya-proofs/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How narya-proofs Compares
| Feature / Agent | narya-proofs | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Mechanically verified proofs from Narya event logs. Verifies queue consistency, replay determinism, non-leakage, and GF(3) conservation. Use for proving system invariants, audit trails, or formal verification of event-sourced systems.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Narya Proofs Skill
Unified verification for event-sourced systems using JSONL interaction logs. Generates cryptographic proof certificates with GF(3) conservation guarantees.
## Four Verifiers with GF(3) Assignments
| Verifier | Trit | Role | Color Range |
|----------|------|------|-------------|
| `queue_consistency` | -1 | MINUS validator | Cold (180-300°) |
| `non_leakage` | -1 | MINUS validator | Cold (180-300°) |
| `replay_determinism` | 0 | ERGODIC coordinator | Neutral (60-180°) |
| `gf3_conservation` | +1 | PLUS generator | Warm (0-60°, 300-360°) |
**GF(3) Meta-Balance**: Sum = -1 + -1 + 0 + 1 = -1 ≡ 2 (mod 3). Runner adds meta-trit +1 → 0 ≡ 0 (mod 3) ✓
## Denotation
> **This skill generates cryptographic proof certificates for event-sourced systems, verifying that all invariants hold and ensuring consistency across distributed systems via mechanically checked proofs.**
```
ProofBundle = ∏_{verifier} (Events → VerifierResult)
Certificate = sha256(Merkle(ProofBundle))
Verdict: VERIFIED ⟺ ∀ verifier: passed = true
```
## Invariant Set
| Invariant | ID | Definition | Verifier |
|-----------|-----|------------|----------|
| `QueueConsistency` | INV-001 | No duplicate event IDs, monotonic timestamps | `queue_consistency` |
| `ReplayDeterminism` | INV-002 | Same seed → same content hash | `replay_determinism` |
| `NonLeakage` | INV-003 | No PII/secrets in event content | `non_leakage` |
| `GF3Conservation` | INV-004 | Context trit sum ≡ 0 (mod 3) | `gf3_conservation` |
| `ProofIntegrity` | INV-005 | Certificate hash covers all verifier outputs | Hash verification |
## GF(3) Typed Effects
| Verifier | Trit | Effect Type | Description |
|----------|------|-------------|-------------|
| `queue_consistency` | -1 | VALIDATOR | No state mutation, validates structure |
| `non_leakage` | -1 | VALIDATOR | No state mutation, validates schema |
| `replay_determinism` | 0 | COORDINATOR | Ensures deterministic replay coordination |
| `gf3_conservation` | +1 | GENERATOR | Generates proof of conservation |
## Narya Compatibility
| Field | Definition |
|-------|------------|
| `before` | Initial event log (JSONL) |
| `after` | Proof bundle with all verifier results |
| `delta` | Proof of state transition (certificate) |
| `birth` | Empty event log |
| `impact` | 1 if any verifier fails (state change from VERIFIED to FAILED) |
## Condensation Policy
**Trigger**: When all 4 verifiers pass for 3 consecutive verification cycles.
**Action**: Archive event log segment, emit condensed proof certificate.
## Proof Objects and Certificates
```python
from dataclasses import dataclass
@dataclass
class VerifierResult:
name: str # Verifier name
trit: int # GF(3) assignment {-1, 0, +1}
passed: bool # Verification passed
details: dict # Violation details
@dataclass
class ProofBundle:
log_path: str # Source JSONL file
events_total: int # Total events processed
verifiers: dict # Results per verifier
overall: str # "VERIFIED" or "FAILED"
proof_hash: str # sha256:... certificate
gf3_meta: dict # Trit conservation metadata
```
## Narya JSONL Log Format
Each line is a JSON object representing an event:
```jsonl
{"event_id": "e1", "timestamp": 1735084800.0, "thread_id": "t1", "trit": -1, "context": "workflow-A", "delta": {"type": "queue_item", "queue_id": "q1", "agent_of": 1, "item_of": "i1", "route": 1}, "content": {"action": "enqueue"}}
{"event_id": "e2", "timestamp": 1735084801.0, "thread_id": "t1", "trit": 0, "context": "workflow-A", "delta": {"type": "route_update", "interaction_id": "i1", "agent_id": 1}, "seed": 42}
{"event_id": "e3", "timestamp": 1735084802.0, "thread_id": "t1", "trit": 1, "context": "workflow-A", "delta": {"type": "agent_assignment", "queue_id": "q1", "agent_id": 1}}
```
**Required fields**: `event_id`
**Optional fields**: `timestamp`, `thread_id`, `trit`, `context`, `delta`, `content`, `seed`, `before_hash`, `after_hash`
## 1. Queue Consistency (Diagram Commutativity)
**Claim**: `agent_of(q) = route(item_of(q))` always holds.
```
agent_of
Queue q ────────────► Agent a
│ ▲
│ item_of │ route
▼ │
Interaction i ───────────┘
```
Verifies that the diagram commutes for every event touching `agent_of`, `item_of`, or `route`.
```python
from src.narya_proofs.queue_consistency import (
QueueConsistencyVerifier,
generate_proof_certificate,
NaryaEvent
)
verifier = QueueConsistencyVerifier(seed=1069)
log = [
NaryaEvent(event_id="e1", before_hash="000", after_hash="abc",
delta={"type": "queue_item", "queue_id": "q1",
"agent_of": 1, "item_of": "i1", "route": 1})
]
summary = verifier.verify_log(log)
cert = generate_proof_certificate(log, seed=1069)
# cert["verdict"] == "VERIFIED"
```
## 2. Replay Determinism (Hash Replay, Time-Travel)
**Claim**: Events with the same seed produce identical content hashes.
Verifies that replay is deterministic—running the same seed produces identical outputs regardless of execution order or timing.
```python
from src.narya_proofs.runner import replay_determinism
events = [
{"event_id": "e1", "seed": 42, "content": {"value": "hello"}},
{"event_id": "e2", "seed": 42, "content": {"value": "hello"}}, # Same seed → same hash ✓
{"event_id": "e3", "seed": 99, "content": {"value": "world"}},
]
result = replay_determinism(events)
# result.passed == True
# result.details["hash_matches"] == 2
```
## 3. Non-Leakage (Schema Conformance, PII Detection)
**Claim**: No secrets or PII appear in event content.
Detects:
- Email addresses
- SSNs (`\d{3}-\d{2}-\d{4}`)
- Credit card numbers (16 digits)
- Redaction markers `[REDACTED:...]`
- Credentials (`password=`, `api_key=`, etc.)
```python
from src.narya_proofs.runner import non_leakage
events = [
{"event_id": "e1", "content": {"user": "alice"}}, # Clean ✓
{"event_id": "e2", "content": {"email": "alice@example.com"}}, # Leak! ✗
]
result = non_leakage(events)
# result.passed == False
# result.details["leak_details"][0]["types"] == ["email"]
```
## 4. GF(3) Conservation (Workflow Law)
**Claim**: In any closed workflow context, sum of trits ≡ 0 (mod 3).
```python
from src.narya_proofs.gf3_conservation import (
GF3ConservationVerifier,
Event,
create_triadic_cycle
)
# Create verifier
verifier = GF3ConservationVerifier(auto_close=True)
# Valid triadic cycle (sum = -1 + 0 + 1 = 0)
events = [
Event("e1", "ctx-alpha", trit=-1),
Event("e2", "ctx-alpha", trit=0),
Event("e3", "ctx-alpha", trit=1),
]
for e in events:
verifier.add_event(e)
proof = verifier.verify_context_closure("ctx-alpha")
# proof.conserved == True
# proof.qed == True
```
### ASCII Visualization
```
─── Trit Flow: ctx-alpha ───
Event │ Trit │ Running Sum │ Visualization
──────────┼──────┼─────────────┼────────────────────────────────
e1 │ [-1] │ -1 (2) │ ◀── █████████████████████│
e2 │ [ 0] │ -1 (2) │ ─●─ █████████████████████│
e3 │ [+1] │ 0 (0) ◆ │ ──▶ │
──────────┴──────┴─────────────┴────────────────────────────────
Final: Σ = 0, ✓ CONSERVED (mod3=0)
```
## Unified Runner with CLI
```bash
# Run all verifiers on a JSONL log
python -m src.narya_proofs.runner path/to/events.jsonl
# Output to file
python -m src.narya_proofs.runner events.jsonl -o proof.json
# With custom seed
python -m src.narya_proofs.runner events.jsonl --seed 42
# Quiet mode (JSON only)
python -m src.narya_proofs.runner events.jsonl -q
```
### Programmatic Usage
```python
from src.narya_proofs import NaryaProofRunner
runner = NaryaProofRunner(seed=1069)
runner.load_log("events.jsonl")
runner.run_all_verifiers()
bundle = runner.generate_proof_bundle()
print(bundle.overall) # "VERIFIED" or "FAILED"
print(bundle.proof_hash) # "sha256:abc123..."
print(runner.to_json()) # Full JSON certificate
```
## Example Verification Output
```json
{
"log_path": "/path/to/events.jsonl",
"events_total": 150,
"verifiers": {
"queue_consistency": {
"passed": true,
"events_checked": 150,
"violations": 0,
"violation_details": []
},
"non_leakage": {
"passed": true,
"clean": 150,
"leaked": 0,
"leak_details": []
},
"replay_determinism": {
"passed": true,
"hash_matches": 45,
"total_seeds": 45,
"mismatches": []
},
"gf3_conservation": {
"passed": true,
"contexts": 5,
"conserved": 5,
"violated": 0,
"total_trit_sum": 0,
"total_mod3": 0
}
},
"overall": "VERIFIED",
"proof_hash": "sha256:8a4f2e1b3c5d7e9f...",
"gf3_meta": {
"verifier_trits": {
"queue_consistency": -1,
"non_leakage": -1,
"replay_determinism": 0,
"gf3_conservation": 1
},
"verifier_trit_sum": -1,
"meta_trit": 1,
"total_sum": 0,
"conserved": true
}
}
```
## Reference Files
- [runner.py](file:///Users/alice/agent-o-rama/agent-o-rama/src/narya_proofs/runner.py) — Unified runner, CLI, all 4 verifiers
- [queue_consistency.py](file:///Users/alice/agent-o-rama/agent-o-rama/src/narya_proofs/queue_consistency.py) — Diagram commutativity, Čech cohomology integration
- [gf3_conservation.py](file:///Users/alice/agent-o-rama/agent-o-rama/src/narya_proofs/gf3_conservation.py) — Context tracking, triadic cycles
- [non_leakage.py](file:///Users/alice/agent-o-rama/agent-o-rama/src/narya_proofs/non_leakage.py) — PII detection patterns
- [replay_determinism.py](file:///Users/alice/agent-o-rama/agent-o-rama/src/narya_proofs/replay_determinism.py) — Seed→hash verification
## GF(3) Triadic Integration
Forms valid triads with complementary skills:
```
narya-proofs (-1) ⊗ ordered-locale (0) ⊗ gay-mcp (+1) = 0 ✓
narya-proofs (-1) ⊗ bisimulation-game (-1) ⊗ gf3_conservation (+1) = -1 ≡ 2 (mod 3)
sheaf-cohomology (-1) ⊗ narya-proofs (-1) ⊗ topos-generate (+1) + meta(+1) = 0 ✓
```
## Commands
```bash
# Run verification demo
just narya-verify events.jsonl
# Generate proof certificate
just narya-cert events.jsonl -o cert.json
# Queue consistency only
just narya-queue-check events.jsonl
# GF(3) conservation report (ASCII visualization)
just narya-gf3-report events.jsonl
```
---
**Skill Name**: narya-proofs
**Type**: Formal Verification / Proof Generation / Event Sourcing
**Trit**: -1 (MINUS - overall validator role)
**GF(3)**: Conserved via meta-trit balancing
**Proof Hash**: SHA-256 Merkle root over all proof objects
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Graph Theory
- **networkx** [○] via bicomodule
- Universal graph hub
### Bibliography References
- `cryptography`: 1 citations in bib.duckdb
## Cat# Integration
This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:
```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```
### GF(3) Naturality
The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```
This ensures compositional coherence in the Cat# equipment structure.Related Skills
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