{"id":"8293b1b8-0648-4406-9e2d-86b46c69043d","entityType":"agent","slug":"clawhub-athola-nm-archetypes-architecture-paradigm-pipeline","name":"architecture-paradigm-pipeline","canonicalUrl":"https://www.xpersona.co/agent/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline","canonicalPath":"/agent/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline","generatedAt":"2026-10-10T04:55:16.511Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-10T02:08:11.020Z","emptyReason":null},"description":"Applies pipes-and-filters for sequential data transformations Skill: architecture-paradigm-pipeline Owner: athola Summary: Applies pipes-and-filters for sequential data transformations Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:05:47.958Z | user Release v1.9.19 v1.9.18 | 2026-08-15T21:29:37.729Z | user Release v1.9.18 v1.9.17 | 2026-07-30T05:29:24.974Z | user Release v1.9.17 v1.9.16 | 2026-07-14T19:46:31.641Z | user Release v1.9.16 v1.9.15 | 2026-07-04T21:20:3","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.8K downloads reported by the source. Last updated 10/10/2026.","installCommand":"clawhub skill install s17emme0e2m3cpf7k2jvp3a84984b8z9:nm-archetypes-architecture-paradigm-pipeline","sourceUrl":"https://clawhub.ai/athola/nm-archetypes-architecture-paradigm-pipeline","homepage":"https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline","primaryLinks":[{"label":"View on ClawHub","url":"https://clawhub.ai/athola/nm-archetypes-architecture-paradigm-pipeline","kind":"source"},{"label":"Homepage","url":"https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline","kind":"homepage"}],"safetyScore":84,"overallRank":62,"popularityScore":40,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"Applies pipes-and-filters for sequential data transformations Skill: architecture-paradigm-pipeline Owner: athola Summary: Applies pipes-and-filters for sequent"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-10-10T02:08:11.020Z","emptyReason":null},"protocols":[{"protocol":"OPENCLEW","label":"OpenClaw","status":"self-declared","notes":"Declared in the public agent profile."}],"capabilities":[],"verifiedCount":0,"selfDeclaredCount":1,"capabilityMatrix":{"rows":[{"key":"OPENCLEW","type":"protocol","support":"unknown","confidenceSource":"profile","notes":"Listed on profile"}],"flattenedTokens":"protocol:OPENCLEW|unknown|profile"}},"adoption":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T02:08:11.020Z","emptyReason":null},"stars":null,"forks":null,"downloads":1785,"packageName":null,"latestVersion":"1.9.19","tractionLabel":"1.8K downloads"},"release":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T02:08:11.020Z","emptyReason":null},"lastUpdatedAt":"2026-10-10T02:08:11.020Z","lastCrawledAt":"2026-10-10T02:08:11.020Z","lastIndexedAt":null,"nextCrawlAt":"2026-10-11T02:08:11.020Z","lastVerifiedAt":null,"highlights":[{"version":"1.9.19","createdAt":"2026-08-26T13:05:47.958Z","changelog":"Release v1.9.19","fileCount":3,"zipByteSize":3254},{"version":"1.9.18","createdAt":"2026-08-15T21:29:37.729Z","changelog":"Release v1.9.18","fileCount":3,"zipByteSize":3310},{"version":"1.9.17","createdAt":"2026-07-30T05:29:24.974Z","changelog":"Release v1.9.17","fileCount":3,"zipByteSize":3443},{"version":"1.9.16","createdAt":"2026-07-14T19:46:31.641Z","changelog":"Release v1.9.16","fileCount":3,"zipByteSize":3298},{"version":"1.9.15","createdAt":"2026-07-04T21:20:32.293Z","changelog":"Release v1.9.15","fileCount":3,"zipByteSize":3287},{"version":"1.9.14","createdAt":"2026-06-30T17:51:09.602Z","changelog":"Release v1.9.14","fileCount":3,"zipByteSize":3288},{"version":"1.9.13","createdAt":"2026-06-27T16:15:36.444Z","changelog":"Release v1.9.13","fileCount":3,"zipByteSize":3258},{"version":"1.9.12","createdAt":"2026-06-19T03:08:59.989Z","changelog":"Release v1.9.12","fileCount":3,"zipByteSize":3331}]},"execution":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"clawhub skill install s17emme0e2m3cpf7k2jvp3a84984b8z9:nm-archetypes-architecture-paradigm-pipeline","setupComplexity":"low","setupSteps":["Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.","Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"CLAWHUB","generatedAt":"2026-10-10T04:55:16.510Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-athola-nm-archetypes-architecture-paradigm-pipeline/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"high","updatedAt":"2026-10-10T02:08:11.020Z","emptyReason":null},"readme":"Skill: architecture-paradigm-pipeline\n\nOwner: athola\n\nSummary: Applies pipes-and-filters for sequential data transformations\n\nTags: latest:1.9.19\n\nVersion history:\n\nv1.9.19 | 2026-08-26T13:05:47.958Z | user\n\nRelease v1.9.19\n\nv1.9.18 | 2026-08-15T21:29:37.729Z | user\n\nRelease v1.9.18\n\nv1.9.17 | 2026-07-30T05:29:24.974Z | user\n\nRelease v1.9.17\n\nv1.9.16 | 2026-07-14T19:46:31.641Z | user\n\nRelease v1.9.16\n\nv1.9.15 | 2026-07-04T21:20:32.293Z | user\n\nRelease v1.9.15\n\nv1.9.14 | 2026-06-30T17:51:09.602Z | user\n\nRelease v1.9.14\n\nv1.9.13 | 2026-06-27T16:15:36.444Z | user\n\nRelease v1.9.13\n\nv1.9.12 | 2026-06-19T03:08:59.989Z | user\n\nRelease v1.9.12\n\nv1.8.6 | 2026-06-07T21:22:55.790Z | user\n\nRelease v1.9.11\n\nv1.8.5 | 2026-05-09T02:15:32.040Z | user\n\nRelease v1.9.5\n\nv1.8.4 | 2026-05-06T14:15:18.731Z | user\n\nRelease v1.9.4\n\nv1.8.3 | 2026-04-10T05:45:51.167Z | user\n\nRelease v1.8.3\n\nArchive index:\n\nArchive v1.9.19: 3 files, 3254 bytes\n\nFiles: skill-card.md (1886b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.19:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.19:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.19\",\n  \"publishedAt\": 1787749547958\n}\n\nFile v1.9.19:skill-card.md\n\n## Description:\n\nApplies pipes-and-filters for sequential data transformations.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[athola](https://clawhub.ai/user/athola)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and architects use this skill to decide when and how to apply the pipes-and-filters architecture pattern to ETL jobs, streaming analytics, CI/CD pipelines, and other sequential transformation workflows.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill may activate on broad architecture, pipeline, ETL, streaming, or data-processing prompts even when the user did not specifically ask for the pipes-and-filters paradigm.\n\nMitigation: Confirm the intended architecture paradigm before applying the guidance to a design decision.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline)\n- [Publisher profile](https://clawhub.ai/user/athola)\n- [Architecture archetypes homepage](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes)\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, configuration]\n\n**Output Format:** [Markdown architecture guidance with adoption steps, deliverables, risks, mitigations, and component vocabulary.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [No code execution, persistence, credential access, or external tool use is described in the security evidence.]\n\n## Skill Version(s):\n\n1.9.19 (source: server release metadata and changelog; source frontmatter reports 1.9.8)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.9.18: 3 files, 3310 bytes\n\nFiles: skill-card.md (1989b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.18:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.18:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.18\",\n  \"publishedAt\": 1786829377729\n}\n\nFile v1.9.18:skill-card.md\n\n## Description:\n\nApplies pipes-and-filters for sequential data transformations.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[athola](https://clawhub.ai/user/athola)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and architects use this skill to decide when to apply a pipes-and-filters pipeline and to plan stages, pipes, schemas, observability, testing, scaling, and failure isolation.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Broad triggers such as architecture, pipeline, and streaming may activate the skill in conversations where pipeline architecture guidance is not relevant.\n\nMitigation: Review and narrow activation triggers before installing in constrained environments.\n\nRisk: Architecture recommendations may be incomplete if applied without workload, schema, back-pressure, observability, and failure-mode validation.\n\nMitigation: Have developers or architects review generated ADRs, schema contracts, load tests, and stage-level observability plans before implementation.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline)\n- [OpenClaw homepage](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Guidance]\n\n**Output Format:** [Markdown architecture guidance with checklists and risk mitigations]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Advisory content only; no executable commands, files, API calls, or tool invocations are produced by the skill.]\n\n## Skill Version(s):\n\n1.9.18 (source: release evidence; artifact frontmatter states 1.9.8)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.9.17: 3 files, 3443 bytes\n\nFiles: skill-card.md (2350b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.17:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.17:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.17\",\n  \"publishedAt\": 1785389364974\n}\n\nFile v1.9.17:skill-card.md\n\n## Description: <br>\nApplies pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and software architects use this skill to evaluate, plan, and document pipes-and-filters pipeline architectures for ETL, streaming analytics, CI/CD, and other sequential transformation workflows. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Broad architecture and pipeline triggers may activate the skill when a different architecture pattern is intended. <br>\nMitigation: Narrow the trigger terms for local deployments or ask the agent to confirm that pipes-and-filters is the intended pattern before applying the guidance. <br>\nRisk: The artifact references a broader Claude Code plugin outside this release. <br>\nMitigation: Review that external plugin separately before installing or relying on its agents, hooks, or commands. <br>\nRisk: Pipeline designs can fail around bottlenecks, schema drift, or back-pressure if the guidance is applied without system-specific validation. <br>\nMitigation: Validate stage contracts, load behavior, buffering, retry logic, and observability requirements before deployment. <br>\n\n\n## Reference(s): <br>\n- [ClawHub Skill Page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [Claude Night Market Archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Guidance, Markdown, Configuration] <br>\n**Output Format:** [Markdown prose with adoption steps, deliverables, risks, and mitigation guidance.] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Advisory architecture guidance only; no executable output or privileged access.] <br>\n\n## Skill Version(s): <br>\n1.9.17 (source: server release evidence; artifact frontmatter lists 1.9.8) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.9.16: 3 files, 3298 bytes\n\nFiles: skill-card.md (2045b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.16:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.16:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.16\",\n  \"publishedAt\": 1784058391641\n}\n\nFile v1.9.16:skill-card.md\n\n## Description: <br>\nApplies pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and architects use this skill to decide when and how to apply a pipes-and-filters pipeline for ETL, streaming analytics, CI/CD, and other sequential data transformations. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The skill may be triggered for broad architecture, pipeline, or streaming discussions where pipes-and-filters guidance is not intended. <br>\nMitigation: Confirm that the user is asking about sequential data transformations before applying the pattern-specific guidance. <br>\nRisk: Architecture guidance can be incomplete or misleading if adopted without review. <br>\nMitigation: Review the proposed pipeline stages, schemas, back-pressure behavior, and operational assumptions before implementation. <br>\n\n\n## Reference(s): <br>\n- [ClawHub Skill Page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [Project Homepage from ClawHub Metadata](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Text, Markdown, Guidance] <br>\n**Output Format:** [Markdown guidance with architecture recommendations, deliverables, and risk mitigations] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Documentation-only; no tools, MCP references, credential variables, or shell commands were detected.] <br>\n\n## Skill Version(s): <br>\n1.9.16 (source: ClawHub release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.9.15: 3 files, 3287 bytes\n\nFiles: skill-card.md (2050b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.15:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.15:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.15\",\n  \"publishedAt\": 1783200032293\n}\n\nFile v1.9.15:skill-card.md\n\n## Description: <br>\nApplies pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and architects use this skill to reason about pipeline architectures for ETL jobs, streaming analytics, CI/CD pipelines, and other workflows built from sequential transformation stages. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The broad architecture and pipeline triggers may activate the skill in conversations where another architecture skill is more specific. <br>\nMitigation: Confirm the user is asking about pipes-and-filters or sequential data transformation before relying on the guidance. <br>\nRisk: Architecture advice can be incomplete or misapplied to a production pipeline. <br>\nMitigation: Review proposed filters, pipe technology, schemas, scaling model, and failure handling with project-specific requirements before implementation. <br>\n\n\n## Reference(s): <br>\n- [ClawHub Skill Page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [OpenClaw Homepage](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, guidance] <br>\n**Output Format:** [Markdown guidance with architecture recommendations and risk mitigations] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Provides architecture advice only; no tools, API calls, credential use, or shell execution are evidenced.] <br>\n\n## Skill Version(s): <br>\n1.9.15 (source: ClawHub release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.9.14: 3 files, 3288 bytes\n\nFiles: skill-card.md (2058b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.14:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.14:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.14\",\n  \"publishedAt\": 1782841869602\n}\n\nFile v1.9.14:skill-card.md\n\n## Description: <br>\nApplies pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and architects use this skill to apply the pipeline, or pipes-and-filters, architecture pattern to ETL jobs, streaming analytics, CI/CD pipelines, and other sequential data transformation workflows. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The skill may activate on generic architecture, pipeline, or streaming prompts where pipeline-pattern guidance is not wanted. <br>\nMitigation: Disable the skill or narrow its activation scope if it becomes noisy in normal agent use. <br>\nRisk: Architecture guidance may be incomplete or unsuitable for a specific production system without local review. <br>\nMitigation: Review generated architecture decisions, schemas, tests, and observability recommendations before implementation. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [OpenClaw homepage](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Text, Markdown, Guidance, Configuration] <br>\n**Output Format:** [Markdown guidance with architecture recommendations and deliverables] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Focuses on architecture decision records, contract tests, integration tests, and observability dashboards for pipeline systems.] <br>\n\n## Skill Version(s): <br>\n1.9.14 (source: server release evidence) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.9.13: 3 files, 3258 bytes\n\nFiles: skill-card.md (1961b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.13:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.13:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.13\",\n  \"publishedAt\": 1782576936444\n}\n\nFile v1.9.13:skill-card.md\n\n## Description: <br>\nApplies pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and architecture reviewers use this skill to decide when a pipes-and-filters pipeline fits ETL, streaming analytics, CI/CD, or other sequential transformation work, and to identify adoption steps, deliverables, and risks. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Broad architecture and pipeline triggers may activate the skill in discussions where pipes-and-filters is only one possible pattern. <br>\nMitigation: Treat the guidance as advisory and compare it with other architecture patterns before adopting it. <br>\nRisk: The referenced full plugin was not part of the reviewed artifact. <br>\nMitigation: Review and scan any separately installed plugin before relying on behavior outside this documentation-only skill. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [OpenClaw homepage](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [guidance, markdown] <br>\n**Output Format:** [Markdown guidance for architecture discussions] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Documentation-only guidance; no tools, API keys, or MCP servers are required.] <br>\n\n## Skill Version(s): <br>\n1.9.13 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.9.12: 3 files, 3331 bytes\n\nFiles: skill-card.md (2114b), SKILL.md (3662b), _meta.json (164b)\n\nFile v1.9.12:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.9.12:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.12\",\n  \"publishedAt\": 1781838539989\n}\n\nFile v1.9.12:skill-card.md\n\n## Description: <br>\nApplies pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and architects use this skill to structure sequential data transformations such as ETL jobs, streaming analytics, and CI/CD pipelines with reusable filters and explicit pipes. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Broad triggers such as architecture, pipeline, ETL, and streaming may activate the skill in conversations where pipeline architecture guidance is not relevant. <br>\nMitigation: Confirm the conversation is about pipes-and-filters or sequential data transformation before following the skill's recommendations. <br>\nRisk: Architecture guidance can be applied incorrectly if treated as a complete implementation plan. <br>\nMitigation: Review the proposed filters, schemas, back-pressure behavior, tests, and observability needs against the target system before adoption. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/athola/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [OpenClaw homepage metadata](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Guidance, Markdown, Configuration, Code] <br>\n**Output Format:** [Markdown guidance with architecture steps, deliverables, risks, and concrete component vocabulary] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [Documentation-only guidance; no tool use, code execution, or credential access is declared.] <br>\n\n## Skill Version(s): <br>\n1.9.12 (source: server release evidence) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.8.6: 3 files, 3296 bytes\n\nFiles: skill-card.md (2062b), SKILL.md (3662b), _meta.json (163b)\n\nFile v1.8.6:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n\n## Concrete Components\n\nThese vocabulary items name the concrete tools and abstractions\nthat show up when the paradigm is implemented. They are not\nrequired dependencies and they are not part of the skill's\n``tools:`` frontmatter (which is reserved for Claude Code tool\nrestrictions). Use this list to disambiguate during architecture\ndiscussions.\n\n- ``stream-processor`` -- the runtime that executes a filter (e.g. Flink, Apache Beam, Faust)\n- ``message-queue`` -- the durable pipe between filters (e.g. Kafka, RabbitMQ, in-memory channel)\n- ``data-validator`` -- schema-checks every record at filter input and output\n\nFile v1.8.6:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.8.6\",\n  \"publishedAt\": 1780867375790\n}\n\nFile v1.8.6:skill-card.md\n\n## Description: <br>\nApplies pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and software architects use this skill to plan pipes-and-filters systems for ETL jobs, streaming analytics, CI/CD pipelines, and other sequential data transformations. It helps frame stages, pipe technology, failure isolation, observability, testing, and operational risks. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Broad architecture and pipeline triggers may surface this skill in general design conversations. <br>\nMitigation: Treat its recommendations as optional guidance and confirm applicability before implementation. <br>\nRisk: Pipeline recommendations may introduce incorrect assumptions about bottlenecks, schema compatibility, or back-pressure behavior. <br>\nMitigation: Validate designs with contract tests, integration tests, observability, and load testing before deployment. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/athola/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [Archetypes homepage](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Guidance, Markdown, Configuration] <br>\n**Output Format:** [Markdown prose with architecture checklists and risk mitigations] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [No executable behavior; recommendations should be reviewed before implementation.] <br>\n\n## Skill Version(s): <br>\n1.8.6 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nArchive v1.8.5: 3 files, 3181 bytes\n\nFiles: skill-card.md (2116b), SKILL.md (3250b), _meta.json (163b)\n\nFile v1.8.5:SKILL.md\n\n---\nname: architecture-paradigm-pipeline\ndescription: Design pipes-and-filters for sequential data transformations\nversion: 1.9.5\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through processing stages\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mechanisms behave as expected under stress.\n## Troubleshooting\n\n### Common Issues\n\n**Command not found**\nEnsure all dependencies are installed and in PATH\n\n**Permission errors**\nCheck file permissions and run with appropriate privileges\n\n**Unexpected behavior**\nEnable verbose logging with `--verbose` flag\n\nFile v1.8.5:_meta.json\n\n{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.8.5\",\n  \"publishedAt\": 1778292932040\n}\n\nFile v1.8.5:skill-card.md\n\n## Description: <br>\nDesign pipes-and-filters for sequential data transformations. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[athola](https://clawhub.ai/user/athola) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and architecture-focused agents use this skill to plan pipes-and-filters systems for ETL jobs, streaming analytics, CI/CD pipelines, and other sequential data transformation workflows. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Broad architecture or streaming prompts may activate the skill when its pipeline guidance is only partly relevant. <br>\nMitigation: Review the suggested architecture advice for fit before applying it to a design or implementation. <br>\nRisk: Pipeline design guidance can be incomplete or unsuitable for a specific system's reliability, scaling, or schema constraints. <br>\nMitigation: Validate recommendations through architecture review, contract tests, load testing, and observability planning before deployment. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/athola/nm-archetypes-architecture-paradigm-pipeline) <br>\n- [Clawdis homepage link](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, guidance, configuration] <br>\n**Output Format:** [Markdown guidance with architecture recommendations, adoption steps, deliverables, risks, and mitigations] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [No code execution, credential handling, persistence, or hidden behavior reported by security evidence] <br>\n\n## Skill Version(s): <br>\n1.8.5 (source: server release metadata; artifact frontmatter states 1.9.5) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>","readmeExcerpt":"Skill: architecture-paradigm-pipeline Owner: athola Summary: Applies pipes-and-filters for sequential data transformations Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:05:47.958Z | user Release v1.9.19 v1.9.18 | 2026-08-15T21:29:37.729Z | user Release v1.9.18 v1.9.17 | 2026-07-30T05:29:24.974Z | user Release v1.9.17 v1.9.16 | 2026-07-14T19:46:31.641Z | user Release v1.9.16 v1.9.15 | 2026-07-04T21:20:3","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: architecture-paradigm-pipeline\ndescription: Applies pipes-and-filters for sequential data transformations\nversion: 1.9.8\ntriggers:\n  - architecture\n  - pipeline\n  - pipes-filters\n  - ETL\n  - streaming\n  - data-processing\n  - data flows through discrete stages like ETL\n  - streaming analytics\n  - or CI/CD pipelines\nmetadata: {\"openclaw\": {\"homepage\": \"https://github.com/athola/claude-night-market/tree/master/plugins/archetypes\", \"emoji\": \"\\ud83c\\udfd7\\ufe0f\"}}\nsource: claude-night-market\nsource_plugin: archetypes\n---\n\n> **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin.\n\n\n# The Pipeline (Pipes and Filters) Paradigm\n\n## When to Employ This Paradigm\n- When data must flow through a fixed sequence of discrete transformations, such as in ETL jobs, streaming analytics, or CI/CD pipelines.\n- When reusing individual processing stages is needed, either independently or to scale bottleneck stages separately from others.\n- When failure isolation between stages is a critical requirement.\n\n## Adoption Steps\n1. **Define Filters**: Design each stage (filter) to perform a single, well-defined transformation. Each filter must have a clear input and output data schema.\n2. **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.\n3. **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.\n4. **Instrument Each Stage**: Implement monitoring for each filter to track key metrics such as latency, throughput, and error rates.\n5. **Orchestrate Deployments**: Design the deployment strategy to allow each stage to be scaled horizontally and upgraded independently.\n\n## Key Deliverables\n- An Architecture Decision Record (ADR) documenting the filters, the chosen pipe technology, the error-handling strategy, and the tools for replaying data.\n- A suite of contract tests for each filter, plus integration tests that cover representative end-to-end pipeline executions.\n- Observability dashboards that visualize stage-level Key Performance Indicators (KPIs).\n\n## Risks & Mitigations\n- **Single-Stage Bottlenecks**:\n  - **Mitigation**: Implement auto-scaling for individual filters. If a single filter remains a bottleneck, consider refactoring it into a more granular sub-pipeline.\n- **Schema Drift Between Stages**:\n  - **Mitigation**: Centralize schema definitions in a shared repository and enforce compatibility tests as part of the CI/CD process to prevent breaking changes.\n- **Back-Pressure Failures**:\n  - **Mitigation**: Conduct rigorous load testing to simulate high-volume scenarios. Validate that buffering, retry logic, and back-pressure mech"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7d107jg9jv602h9ytsegydq184a42s\",\n  \"slug\": \"nm-archetypes-architecture-paradigm-pipeline\",\n  \"version\": \"1.9.19\",\n  \"publishedAt\": 1787749547958\n}"},{"path":"skill-card.md","content":"## Description:\n\nApplies pipes-and-filters for sequential data transformations.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[athola](https://clawhub.ai/user/athola)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and architects use this skill to decide when and how to apply the pipes-and-filters architecture pattern to ETL jobs, streaming analytics, CI/CD pipelines, and other sequential transformation workflows.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill may activate on broad architecture, pipeline, ETL, streaming, or data-processing prompts even when the user did not specifically ask for the pipes-and-filters paradigm.\n\nMitigation: Confirm the intended architecture paradigm before applying the guidance to a design decision.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/athola/skills/nm-archetypes-architecture-paradigm-pipeline)\n- [Publisher profile](https://clawhub.ai/user/athola)\n- [Architecture archetypes homepage](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes)\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, configuration]\n\n**Output Format:** [Markdown architecture guidance with adoption steps, deliverables, risks, mitigations, and component vocabulary.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [No code execution, persistence, credential access, or external tool use is described in the security evidence.]\n\n## Skill Version(s):\n\n1.9.19 (source: server release metadata and changelog; source frontmatter reports 1.9.8)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Applies pipes-and-filters for sequential data transformations Skill: architecture-paradigm-pipeline Owner: athola Summary: Applies pipes-and-filters for sequential data transformations Tags: latest:1.9.19 Version history: v1.9.19 | 2026-08-26T13:05:47.958Z | user Release v1.9.19 v1.9.18 | 2026-08-15T21:29:37.729Z | user Release v1.9.18 v1.9.17 | 2026-07-30T05:29:24.974Z | user Release v1.9.17 v1.9.16 | 2026-07-14T19:46:31.641Z | user Release v1.9.16 v1.9.15 | 2026-07-04T21:20:3","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":1038,"uniquenessScore":55,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-10T02:08:11.020Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-10T02:08:11.020Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-10T04:55:16.511Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs","url":"https://github.com/CherryHQ/cherry-studio","homepage":"https://cherry-ai.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-11T14:38:40.986Z","createdAt":"2026-02-25T03:38:19.379Z","downloads":null},{"id":"6f6582d0-5d76-4f0f-b81d-86520247950b","entityType":"agent","canonicalPath":"/agent/copilotkit-copilotkit","slug":"copilotkit-copilotkit","name":"CopilotKit","description":"The Frontend for Agents & Generative UI. React + Angular","url":"https://github.com/CopilotKit/CopilotKit","homepage":"https://docs.copilotkit.ai","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-03-25T09:50:57.846Z","createdAt":"2026-02-25T03:39:14.617Z","downloads":null}],"links":{"hub":"/agent","source":"/agent/source/clawhub","protocols":[{"label":"OpenClaw","href":"/agent/protocol/openclew"}]}}}