Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lugassawan/swe-workbench --skill principle-distributed-systemsgit clone --depth 1 https://github.com/lugassawan/swe-workbenchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lugassawan/swe-workbench/principle-distributed-systems)<a href="https://agentmods.dev/skills/lugassawan/swe-workbench/principle-distributed-systems"><img src="https://agentmods.dev/badge/skills/lugassawan/swe-workbench/principle-distributed-systems/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lugassawan/swe-workbench/principle-distributed-systems"><img src="https://agentmods.dev/badge/skills/lugassawan/swe-workbench/principle-distributed-systems.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.01682 |
| Opus 5.5 | $0.00038 | $0.00673 |
| Sonnet 5.5 | $0.00019 | $0.00336 |
| Haiku 4.5 | $0.00010 | $0.00168 |
Grade A, and why
principle-distributed-systems scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Distributed Systems
Multi-node systems fail partially and non-deterministically. Every design decision spans a space of consistency, availability, and latency trade-offs — name them explicitly.
CAP and PACELC
CAP: under a network Partition, choose Consistency or Availability — you cannot guarantee both.
PACELC extends this to normal operation: even without a partition, every request trades Latency for Consistency. Always name both axes: "This is AP/EL — available under partition, latency-favoured otherwise."
Common placements: Cassandra (AP/EL), HBase (CP/EC), Spanner (CP/EC via TrueTime), DynamoDB (AP/EL by default, tunable).
Consistency Models
Ordered strongest → weakest:
| Model | Promise |
|---|---|
| Linearizable | Reads always see the most recent committed write; operations appear atomic. Requires consensus. |
| Sequential | All nodes observe operations in the same order, not necessarily wall-clock order. |
| Causal | Causally related writes are observed in order; concurrent writes may diverge. |
| Read-your-writes | A client always reads its own most recent write (single-session). |
| Monotonic reads | A client never reads a value older than one it already read. |
| Eventual | All replicas converge given sufficient time and no new writes. |
Choose the weakest model that satisfies correctness. Most user-facing apps need only read-your-writes + monotonic reads; linearizability is expensive.
Time, Clocks, and Ordering
Physical clocks drift — never use wall-clock timestamps to order events across nodes.
- Lamport timestamps: logical counter incremented on send/receive. Establishes causal order; cannot detect concurrency.
- Vector clocks: per-node counters. Detect both causality (
a → b) and concurrency (a ∥ b). Used by Riak. - Hybrid Logical Clocks (HLC): monotonically increasing, stays close to physical time. Used by CockroachDB.
- TrueTime (Spanner): GPS + atomic clocks with bounded uncertainty interval; enables external consistency.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 121 lines · 96 tokens per session scan A 126da5f7861b
principle-distributed-systems is a skill published in the GitHub repository lugassawan/swe-workbench (3 stars, last pushed yesterday), licensed MIT. It adds 96 tokens to every session and 1,682 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
code-forge
Generate implementation code from an approved design blueprint or verbal requirements. Composes context anchoring, architecture, clean code, DDD, security, and test quality into an inside-out implementation workflow. Use when moving from design to code, implementing approved contracts, or when the user says…
functions-development
Build serverless Go or Python functions for Falcon Foundry apps. TRIGGER when user asks to "create a function", "write a serverless function", "build backend logic", runs foundry functions create, or needs help with FDK handler patterns, function testing, or collection integration from functions. Also TRIGGER when…
clean-ddd-hexagonal
Design or review backend domain and dependency boundaries using DDD, Clean Architecture, and ports/adapters. Use for aggregate modeling, bounded contexts, use-case isolation, or architecture refactoring; not routine CRUD changes.
postgres-drizzle
Write or review PostgreSQL schemas, queries, migrations, and Drizzle ORM code. Use for Postgres/Drizzle relations, indexing, pooling, or query performance; not for unrelated databases or generic backend work.
slack-block-kit
Build or debug Slack Block Kit payloads for messages, modals, App Home, streaming responses, and Work Object unfurls. Use for block layout, elements, or interaction schemas; not plain Slack text formatting alone.
application
Use when creating application layer artifacts for a NestJS bounded context — use cases, CQRS command/query handlers, DTOs, ports (cross-module interfaces), application services, or read model projections. Supports three patterns: plain UseCase (A), CQRS native (B), and Handler-as-Orchestrator (C).