ssmdstorm

name	ssmdstorm
description	Multi-agent orchestration for ssmd market data system tasks. Extends waldstorm with domain-specific experts for secmaster, data feeds, trading APIs, and data quality. Use when working on connectors, exchanges, NATS pipelines, market metadata, or user says "ssmdstorm", "market data task", "exchange work".

ssmdstorm

Multi-agent orchestration for ssmd market data system work. Extends waldstorm with domain-specific experts.

Overview

ssmdstorm adds 7 ssmd-specific experts to waldstorm's general panel, plus 4 exchange domain agents from shadowagent:

ssmd experts (dlawskillz):

Expert	Focus Area
Secmaster	Market metadata, sync, lifecycle, CDC, Redis cache
Data Feed	Connectors, WebSocket, NATS streams, sharding
Access Feed	Trading APIs, orderbook, fills, positions
Data Quality	NATS vs API reconciliation, trade verification, DQ Python tools
CLI	ssmd CLI commands (Deno ops + Go GitOps), env management
Symbology	DB tables, NATS subjects, SQL queries for cross-exchange data
Parquet Backfill	Parquet generation: ssmd-parquet-gen CLI, feed/stream/prefix mappings, K8s Jobs, GCS verification
Harman Deploy	Harman OMS K8s deployment, new exchange instance setup, CR configuration
Integration Test	Kalshi demo integration tests, harman live tests, end-to-end validation
ssmd Architecture	Data pipeline end-to-end: component dependencies, data flow, NATS streams, CDC, impact analysis
Harman Architecture	OMS end-to-end: order lifecycle, EMS/OMS split, pump model, reconciliation, exchange adapters

Exchange domain agents (shadowagent):

Agent	Focus Area
Kalshi	Kalshi exchange: API, fees, market mechanics, crypto tickers
Kraken	Kraken Futures: perpetuals, funding rates, WebSocket API
Polymarket	Polymarket: CLOB, Gamma API, token model, sharding
Symbology	Cross-exchange identifier formats, ticker anatomy

Expert Selection Guide

Task Domain	ssmd Experts	+ shadowagent	+ General Experts	+ varlab-ops
Market metadata work	Secmaster, CLI	-	Database, Senior Dev	-
Adding new exchange	Data Feed	shadow:{exchange}	DevOps, Security	Operations
Trading API integration	Access Feed, Secmaster	shadow:{exchange}	Security, API Designer	-
Data pipeline architecture	ssmd Architecture	shadow:{exchange}	Senior Dev, Performance	-
OMS architecture decisions	Harman Architecture	shadow:{exchange}	Security, Senior Dev	-
Pipeline deployment	Data Feed, CLI	-	DevOps, Platform/Infra	Operations
Signal development	Data Feed, Secmaster, CLI	shadow:{exchange}	Performance, QA	-
CDC/cache work	Secmaster	-	Database, Performance	-
Orderbook integration	Access Feed, Data Feed	shadow:{exchange}	Performance, Security	-
CLI operations	CLI	-	DevOps	-
Data quality checks	Data Quality, CLI	shadow:{exchange}	QA	-
Parquet backfill/regen	Parquet Backfill, Data Quality	-	DevOps	Operations
Ticker/ID mapping	Symbology	shadow:symbology	Database	-
Cross-exchange correlation	Symbology, Data Feed	shadow:symbology	Performance	-
End-to-end deploy (code + K8s)	Data Feed, CLI	shadow:{exchange}	Security, Performance	Operations

Cross-Skill Pairing

Tasks that span code AND infrastructure need experts from both ssmdstorm and varlab-ops. The selection guide above includes the varlab-ops column for these cases. Common cross-skill patterns:

Pattern	Why Both Skills
New exchange end-to-end	ssmd: connector code, NATS subjects, writer. varlab-ops: K8s deployment, NATS stream, archiver CR, network policy
Image build + deploy	ssmd: Rust/Deno changes, tag format. varlab-ops: deployment YAML, Flux reconcile, image pull
Scale operations	ssmd: CLI commands. varlab-ops: Flux suspend/resume, kubectl context

Trigger Keywords

When analyzing a task description, match these keywords to experts:

Keywords	Expert
websocket, connector, exchange, feed, subscribe, channel	Data Feed
secmaster, market metadata, sync, CDC, Redis, lifecycle	Secmaster
orderbook, fill, position, trading, order, balance	Access Feed
nats count, match rate, reconciliation, missing trades, dq	Data Quality
cli, scale, deploy, env, schedule, deno task	CLI
ticker, symbol, pair_id, token_id, condition_id, identifier, cross-exchange	Symbology
parquet, backfill, regenerate, parquet-gen, jsonl to parquet	Parquet Backfill
harman deploy, new harman instance, harman CR, exchange instance	Harman Deploy
integration test, demo API, live test, harman test, end-to-end test	Integration Test
architecture, component dependency, impact analysis, data flow, pipeline design	ssmd Architecture
order lifecycle, EMS, OMS, pump model, reconciliation, exchange adapter, session model	Harman Architecture
kalshi, prediction market, contract, series, category, CFTC	shadow:kalshi
kraken, perpetual, funding rate, PF_, FF_, futures	shadow:kraken
polymarket, CLOB, condition_id, token, Gamma API, UMA	shadow:polymarket
ticker format, pair anatomy, cross-exchange mapping	shadow:symbology
mcp, api key, data access, market lookup, datasets:read, query parquet	Symbology, Data Feed
deployment, kustomization, flux, network policy, PVC	Operations (varlab-ops)
securityContext, input validation, sanitization, auth	Security (waldstorm)
max_message_size, buffer, latency, throughput, memory	Performance (waldstorm)

Instructions

Step 1: Understand the Task

Same as waldstorm - gather task description, constraints, context.

Step 2: Select Experts

Always include at least one ssmd expert. Use the selection guide above.

MANDATORY: Always include the QA/Testing Expert (dlaw:qa-testing-expert) in every expert panel. Complete unit test coverage is required for all code changes.

Agent definitions in agents/ provide each ssmd expert as a spawnable agent with memory: local for persistent learnings across sessions.

Available ssmd agents (dlawskillz, in ./agents/):

• ssmd-secmaster - Market metadata, sync, CDC
• ssmd-data-feed - Connectors, NATS, archiving
• ssmd-access-feed - Trading APIs, orderbook, fills
• ssmd-data-quality - NATS vs API reconciliation, trade verification, DQ Python tools
• ssmd-cli - ssmd CLI commands (Deno ops + Go GitOps), env management
• ssmd-symbology - DB tables, NATS subjects, SQL queries for cross-exchange data
• ssmd-parquet-backfill - Parquet generation CLI, feed/stream/prefix mappings, K8s Jobs, GCS verification
• ssmd-harman-deploy - Harman OMS K8s deployment, new exchange instance setup
• ssmd-integration-test - Kalshi demo integration tests, harman live tests, end-to-end validation
• ssmd-architecture - Data pipeline architecture: component deps, data flow, NATS, CDC, impact analysis
• harman-architecture - OMS architecture: order lifecycle, EMS/OMS, pump, reconciliation, exchange adapters

Available exchange agents (shadowagent, shadow: prefix):

• shadow:kalshi - Kalshi exchange: API, fees, market mechanics, crypto tickers
• shadow:kraken - Kraken Futures: perpetuals, funding rates, WebSocket API
• shadow:polymarket - Polymarket: CLOB, Gamma API, token model, sharding
• shadow:symbology - Cross-exchange identifier formats, ticker anatomy

Combine with waldstorm's general agents (Security, DevOps, etc.) as needed.

Step 3-9: Follow waldstorm

Use team primitives (TeamCreate, TaskCreate, Task tool for teammates, SendMessage) to run expert analysis, then synthesize, plan, and execute per waldstorm workflow. This includes:

• Step 6 (ECC Code Review): After synthesis, optionally invoke everything-claude-code:code-review for an independent structured review of proposed changes. Recommended for any task touching data pipelines, trading APIs, or security-sensitive code.
• Step 7 (Writing Plans): Use superpowers:writing-plans to save implementation plan
• Step 8 (Executing Plans): Use superpowers:executing-plans with checkpoints
• Step 9 (Cleanup): shutdown_request + TeamDelete

Step 8: Track Records (automated via agent memory)

Agents with memory: local automatically read/write their own MEMORY.md in .claude/agent-memory-local/<name>/MEMORY.md. Manual track record updates are no longer needed when using agents.

The Expert Track Record table below serves as historical reference. Agents now self-update their learnings via memory: local.

Expert Track Record

Track which experts produced actionable findings per task type. Use this to inform future selection.

Session	Task	Experts Used	Key Findings
2026-02-06	Kraken exchange (end-to-end)	Data Feed, Operations, Security, Performance	Security: missing securityContext, NATS subject injection via unsanitized symbols. Performance: WebSocket max_message_size default too large. Data Feed: archiver-per-exchange, serde untagged pitfalls. Operations: static Deployment vs CR, Flux suspend lifecycle. All 4 experts produced HIGH-priority findings.

Observations:

• End-to-end exchange tasks need 4+ experts across skills (cross-skill pairing essential)
• Security and Performance generals caught infra/config issues the domain experts missed
• Data Feed expert most valuable for exchange-specific protocol details
• Operations expert essential whenever K8s manifests are involved

Key ssmd Context

Architecture:

Kalshi WS  -> Connector -> NATS JetStream -> Archiver/Signal/Notifier
Kraken WS  -> Connector ---^
                              |
PostgreSQL <- secmaster sync <- CDC -> dynamic subscriptions

Current state (Feb 2026):

• Exchanges: Kalshi (prediction markets), Kraken Futures (crypto perps), Polymarket (prediction markets)
• Kalshi channels: ticker, trade, market_lifecycle_v2
• Kraken channels: ticker, trade
• Polymarket channels: last_trade_price, price_change, book, best_bid_ask
• Pending: orderbook_delta, fill, market_positions (Kalshi)
• Environments: prod (GKE Autopilot, us-east1)
• ssmd-mcp: Local Python MCP server with 6 tools (query_trades, query_prices, query_raw, lookup_market, list_feeds, check_freshness) — DuckDB for GCS parquet, httpx for data-ts API
• data-ts LoadBalancer: External access from allowed CIDRs, externalTrafficPolicy: Local

Key paths:

• ssmd code: project root
• K8s manifests: clusters/gke-prod/apps/ssmd/ (in 899bushwick)
• CLI reference: docs/reference/cli-reference.md (in 899bushwick)

Model Routing

Inherit waldstorm's model routing table. Additional ssmd-specific guidance:

Context	Model	Rationale
ssmd domain experts (data-feed, secmaster, etc.)	sonnet	Domain analysis is focused and well-scoped
Exchange agents (shadow:kalshi, etc.)	sonnet	Protocol-specific analysis
Cross-expert synthesis with domain conflicts	opus	Requires deep understanding of ssmd architecture
Parquet backfill / CLI operations	haiku	Mechanical, well-documented procedures

Skills & Plugins Used

• superpowers:writing-plans - Convert recommendations to implementation plan
• superpowers:executing-plans - Execute with review checkpoints
• everything-claude-code:code-review - Independent structured review of proposed changes