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Distributed Systems

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yonatangross
distributed-systems

Distributed systems patterns for locking, resilience, idempotency, and rate limiting. Use when implementing distributed locks, circuit breakers, retry policies, idempotency keys, token bucket rate limiters, or fault tolerance patterns.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namedistributed-systems
Stars
298
Forks
35
Bundled files
24
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 24 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yonatangross on GitHub. Read the source before you install it.

Installation

Install the Distributed Systems AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/distributed-systems
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/distributed-systems .claude/skills/distributed-systems
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Distributed Systems in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Distributed Systems on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Distributed Systems is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Distributed Systems Patterns

Comprehensive patterns for building reliable distributed systems. Each category has individual rule files in rules/ loaded on-demand.

Quick Reference

CategoryRulesImpactWhen to Use
Distributed Locks1CRITICALFencing tokens, owner validation; Redis/Redlock and Postgres advisory via upstream docs
Resilience3CRITICALCircuit breakers, retry with backoff, bulkhead isolation
Idempotency1HIGHIdempotency keys; dedup and database-backed storage via upstream docs
Rate Limiting2HIGHToken bucket, sliding window; SlowAPI integration via upstream docs
Edge Computing2HIGHEdge workers, V8 isolates, CDN caching, geo-routing
Event-Driven2HIGHEvent sourcing, CQRS, transactional outbox, sagas

Total: 11 rules across 6 categories. Removed topics point at first-party sources in Upstream coverage; ork-specific scars live in references/ork-delta.md.

Quick Start

python
# Redis distributed lock with Lua scripts
async with RedisLock(redis_client, "payment:order-123"):
    await process_payment(order_id)

# Circuit breaker for external APIs
@circuit_breaker(failure_threshold=5, recovery_timeout=30)
@retry(max_attempts=3, base_delay=1.0)
async def call_external_api():
    ...

# Idempotent API endpoint
@router.post("/payments")
async def create_payment(
    data: PaymentCreate,
    idempotency_key: str = Header(..., alias="Idempotency-Key"),
):
    return await idempotent_execute(db, idempotency_key, "/payments", process)

# Token bucket rate limiting
limiter = TokenBucketLimiter(redis_client, capacity=100, refill_rate=10)
if await limiter.is_allowed(f"user:{user_id}"):
    await handle_request()

Distributed Locks

Coordinate exclusive access to resources across multiple service instances.

RuleFileKey Pattern
Fencing Tokensrules/locks-fencing-tokens.mdOwner validation, TTL, heartbeat extension

Redis single-node locks, Redlock quorum, and PostgreSQL advisory locks are first-party documented; see Upstream coverage.

Resilience

Production-grade fault tolerance for distributed systems.

RuleFileKey Pattern
Circuit Breakerrules/resilience-circuit-breaker.mdCLOSED/OPEN/HALF_OPEN states, sliding window
Retry & Backoffrules/resilience-retry-backoff.mdExponential backoff, jitter, error classification
Bulkhead Isolationrules/resilience-bulkhead.mdSemaphore tiers, rejection policies, queue depth

Idempotency

Ensure operations can be safely retried without unintended side effects.

RuleFileKey Pattern
Idempotency Keysrules/idempotency-keys.mdDeterministic hashing, Stripe-style headers

Event-consumer dedup and database-backed idempotency storage follow the Stripe pattern; see Upstream coverage.

Rate Limiting

Protect APIs with distributed rate limiting using Redis.

RuleFileKey Pattern
Token Bucketrules/ratelimit-token-bucket.mdRedis Lua scripts, burst capacity, refill rate
Sliding Windowrules/ratelimit-sliding-window.mdSorted sets, precise counting, no boundary spikes

SlowAPI + Redis wiring and tiered limits are first-party documented; see Upstream coverage.

Edge Computing

Edge runtime patterns for Cloudflare Workers, Vercel Edge, and Deno Deploy.

RuleFileKey Pattern
Edge Workersrules/edge-workers.mdV8 isolate constraints, Web APIs, geo-routing, auth at edge
Edge Cachingrules/edge-caching.mdCache-aside at edge, CDN headers, KV storage, stale-while-revalidate

Event-Driven

Event sourcing, CQRS, saga orchestration, and reliable messaging patterns.

RuleFileKey Pattern
Event Sourcingrules/event-sourcing.mdEvent-sourced aggregates, CQRS read models, optimistic concurrency
Event Messagingrules/event-messaging.mdTransactional outbox, saga compensation, idempotent consumers

Upstream coverage (do not restate)

These topics were removed from this skill on 2026-07-31 (wrap-plus-delta thinning) because a first-party source maintains them. Consult the source; do not re-add tutorials here. Ork-specific scars for these topics live in references/ork-delta.md.

TopicFirst-party source
Redis single-node locks, Redlock algorithm and quorumhttps://redis.io/docs/latest/develop/use/patterns/distributed-locks/
PostgreSQL advisory locks (session and transaction level)https://www.postgresql.org/docs/current/explicit-locking.html#ADVISORY-LOCKS
Circuit breaker pattern, thresholds, setup and rollout guideshttps://learn.microsoft.com/azure/architecture/patterns/circuit-breaker
Bulkhead pattern deep dive (thread pool, semaphore, tiers)https://learn.microsoft.com/azure/architecture/patterns/bulkhead
Retry strategies, exponential backoff, jitter, retry budgetshttps://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/ and https://tenacity.readthedocs.io
HTTP and LLM provider error classification (retryable vs not)https://docs.claude.com/en/api/errors and https://platform.openai.com/docs/guides/error-codes
Idempotency keys, request dedup, database-backed idempotencyhttps://docs.stripe.com/api/idempotent_requests
Token bucket algorithm and Redis rate-limiting patternshttps://redis.io/glossary/rate-limiting/
FastAPI distributed rate limiting (SlowAPI middleware, tiers)https://slowapi.readthedocs.io/
LLM fallback chains, provider failover, cost trackinghttps://vercel.com/docs/ai-gateway; model ids and pricing at https://docs.claude.com/en/docs/about-claude/pricing

Key Decisions

DecisionRecommendation
Lock backendRedis for speed, PostgreSQL if already using it, Redlock for HA
Lock TTL2-3x expected operation time
Circuit breaker recoveryHalf-open probe with sliding window
Retry algorithmExponential backoff + full jitter
Bulkhead isolationSemaphore-based tiers (Critical/Standard/Optional)
Idempotency storageRedis (speed) + DB (durability), 24-72h TTL
Rate limit algorithmToken bucket for most APIs, sliding window for strict quotas
Rate limit storageRedis (distributed, atomic Lua scripts)

When NOT to Use

No separate event-sourcing/saga/CQRS skills exist; they are rules within distributed-systems. But most projects never need them.

PatternInterviewHackathonMVPGrowthEnterpriseSimpler Alternative
Event sourcingOVERKILLOVERKILLOVERKILLOVERKILLWHEN JUSTIFIEDAppend-only table with status column
Saga orchestrationOVERKILLOVERKILLOVERKILLSELECTIVEAPPROPRIATESequential service calls with manual rollback
Circuit breakerOVERKILLOVERKILLBORDERLINEAPPROPRIATEREQUIREDTry/except with timeout
Distributed locksOVERKILLOVERKILLBORDERLINEAPPROPRIATEREQUIREDDatabase row-level lock (SELECT FOR UPDATE)
CQRSOVERKILLOVERKILLOVERKILLOVERKILLWHEN JUSTIFIEDSingle model for read/write
Transactional outboxOVERKILLOVERKILLOVERKILLSELECTIVEAPPROPRIATEDirect publish after commit
Rate limitingOVERKILLOVERKILLSIMPLE ONLYAPPROPRIATEREQUIREDNginx rate limit or cloud WAF

Rule of thumb: If you have a single server process, you do not need distributed systems patterns. Use in-process alternatives. Add distribution only when you actually have multiple instances.

Anti-Patterns (FORBIDDEN)

python
# LOCKS: Never forget TTL (causes deadlocks)
await redis.set(f"lock:{name}", "1")  # WRONG - no expiry!

# LOCKS: Never release without owner check
await redis.delete(f"lock:{name}")  # WRONG - might release others' lock

# RESILIENCE: Never retry non-retryable errors
@retry(max_attempts=5, retryable_exceptions={Exception})  # Retries 401!

# RESILIENCE: Never put retry outside circuit breaker
@retry  # Would retry when circuit is open!
@circuit_breaker
async def call(): ...

# IDEMPOTENCY: Never use non-deterministic keys
key = str(uuid.uuid4())  # Different every time!

# IDEMPOTENCY: Never cache error responses
if response.status_code >= 400:
    await cache_response(key, response)  # Errors should retry!

# RATE LIMITING: Never use in-memory counters in distributed systems
request_counts = {}  # Lost on restart, not shared across instances

Detailed Documentation

ResourceDescription
scripts/Templates: lock implementations, circuit breaker, rate limiter
references/ork-delta.mdOrk-specific scars and house decisions kept after the wrap-plus-delta thinning

Related Skills

  • caching - Redis caching patterns, cache as fallback
  • background-jobs - Job deduplication, async processing with retry
  • observability-monitoring - Metrics and alerting for circuit breaker state changes
  • error-handling-rfc9457 - Structured error responses for resilience failures
  • auth-patterns - API key management, authentication integration

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Distributed Systems AI skill do?

Distributed systems patterns for locking, resilience, idempotency, and rate limiting. Use when implementing distributed locks, circuit breakers, retry policies, idempotency keys, token bucket rate limiters, or fault tolerance patterns.

Why use Distributed Systems on TypingMind?

Because you install it once and use it with any model. Distributed Systems is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Distributed Systems in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/distributed-systems. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Distributed Systems?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Distributed Systems?

As many as you like. As long as a model supports skills, you can use Distributed Systems with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Distributed Systems AI skill free?

Yes. It is published on GitHub by yonatangross under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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