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 athola/claude-night-market --skill architecture-paradigm-pipelinegit clone --depth 1 https://github.com/athola/claude-night-marketWrote 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/athola/claude-night-market/architecture-paradigm-pipeline)<a href="https://agentmods.dev/skills/athola/claude-night-market/architecture-paradigm-pipeline"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/architecture-paradigm-pipeline/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/athola/claude-night-market/architecture-paradigm-pipeline"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/architecture-paradigm-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.00883 |
| Opus 5 | $0.00018 | $0.00441 |
| Sonnet 5 | $0.00007 | $0.00177 |
| Haiku 4.5 | $0.00004 | $0.00088 |
Grade A, and why
architecture-paradigm-pipeline 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 9d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Pipeline (Pipes and Filters) Paradigm
When to Employ This Paradigm
- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.
- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.
- When failure isolation between stages is a critical requirement.
When NOT To Use
- Interactive request/response systems (use
archetypes:architecture-paradigm-client-server) - Stages that must share mutable state, which the pattern cannot express
Adoption Steps
- Define Filters: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.
- Connect via Pipes: Connect the filters using "pipes," which can be implemented as streams, message queues, or in-memory channels. validate these pipes support back-pressure and buffering.
- Maintain Stateless Filters: Where possible, design filters to be stateless. Any required state should be persisted externally or managed at the boundaries of the pipeline.
- Instrument Each Stage: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.
- Orchestrate Deployments: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.
Key Deliverables
- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.
- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.
- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).
Risks & Mitigations
- Single-Stage Bottlenecks:
- Mitigation: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.
- Schema Drift Between Stages:
- Mitigation: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.
- Back-Pressure Failures:
- Mitigation: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.
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.
- 9d ago First seen · 80 lines · 37 tokens per session scan A 546c1fd18a05
architecture-paradigm-pipeline is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 883 once invoked, about $0.0002 per session on Opus 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.
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