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Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation tha...\n\nTags: flow:1.0.0, latest:1.0.0, orchestration:1.0.0, pipeline:1.0.0, python:1.0.0, workflow:1.0.0\n\nVersion history:\n\nv0.1.0 | 2026-05-28T22:27:30.962Z | user\n\nInitial: lightweight Python flow orchestration library\n\nv1.0.0 | 2026-05-28T18:40:35.905Z | auto\n\nInitial release of python-flow-engine.\n\n- Lightweight Python flow orchestration library with no external dependencies.\n- Build data processing pipelines with serial (>>), parallel (//), and conditional (|/connect) execution.\n- Core API includes Node and Pipeline classes for pipeline construction and execution.\n- Supports node-level timeouts and retry logic for robust error handling.\n- Provides pipeline visualization via Mermaid.js flowcharts.\n- Includes core library and a demo script covering six scenarios.\n\nArchive index:\n\nArchive v0.1.0: 5 files, 9508 bytes\n\nFiles: scripts/pipeline_demo.py (11797b), scripts/pipeline.py (12815b), skill-card.md (1981b), SKILL.md (2255b), _meta.json (137b)\n\nFile v0.1.0:SKILL.md\n\n---\nname: python-flow-engine\ndescription: \"Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation that needs serial/parallel/conditional execution. Triggers: pipeline, flow, orchestrate, workflow, ETL, data pipeline, process chain.\"\n---\n\n# Python Flow Engine\n\nLightweight Python flow orchestration — no external dependencies. Build pipelines with serial (>>), parallel (//), and conditional (|) node execution.\n\n## Core API\n\n### Node\n\n```python\nfrom pipeline import Node\n\ndef my_fn(context, data):\n    # context: shared dict for the pipeline run\n    # data: input from previous node\n    return modified_data\n\nnode = Node(my_fn, name=\"step1\", timeout=10, retry=1)\n```\n\n### Pipeline\n\n```python\npipe = Pipeline()\npipe.add_node(node)\npipe.run(start_node, input_data)\n```\n\n### Connection Operators\n\n| Operator | Mode | Behavior |\n|----------|------|----------|\n| `a >> b` | Serial | a runs, then b runs with a's output |\n| `a // b` | Parallel | a and b run concurrently (bidirectional) |\n| `a \\| b` | Conditional | b runs based on a's output (use `connect`) |\n\nFor conditional connections, use `connect()`:\n\n```python\npipe.connect(node_a, node_b, \"conditional\",\n    condition=lambda ctx, data: ctx.get(\"is_valid\", False))\n```\n\nOr use `Node.condition` parameter:\n\n```python\nnode_b = Node(fn, condition=lambda ctx, data: data > 0)\n```\n\n### Visualization\n\n```python\nprint(pipe.visualize())  # outputs Mermaid.js flowchart\n```\n\n### Error Handling\n\n- **retry**: `Node(fn, retry=2)` retries up to 3 total attempts\n- **timeout**: `Node(fn, timeout=5)` raises `RuntimeError` if execution exceeds 5s\n\n## Available Scripts\n\n- `scripts/pipeline.py` — Core library (importable, no dependencies)\n- `scripts/pipeline_demo.py` — End-to-end demo (6 scenarios)\n\n## Quick Start\n\n```python\nfrom pipeline import Node, Pipeline\n\ndef double(ctx, x): return x * 2\ndef add_one(ctx, x): return x + 1\n\npipe = Pipeline()\nn1 = Node(double, name=\"double\")\nn2 = Node(add_one, name=\"add_one\")\npipe.add_node(n1)\npipe.add_node(n2)\nn1 >> n2\n\nresult = pipe.run(n1, 5)  # 5*2+1 = 11\nprint(result)  # 11\nprint(pipe.visualize())\n```\n\nRun the full demo:\n```\npython scripts/pipeline_demo.py\n```\n\nFile v0.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn71mebbt22z0hvy3y0s8j50s183m8ba\",\n  \"slug\": \"python-flow-engine\",\n  \"version\": \"0.1.0\",\n  \"publishedAt\": 1780007250962\n}\n\nFile v0.1.0:skill-card.md\n\n## Description: <br>\nLightweight Python flow orchestration library for building data processing pipelines, workflow automation, ETL tasks, and multi-step computations with serial, parallel, and conditional execution. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[dnaxxx-hub](https://clawhub.ai/user/dnaxxx-hub) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and engineers use this skill to build or explain lightweight Python pipelines that coordinate callable steps with serial, parallel, conditional, retry, timeout, and Mermaid visualization support. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The trigger words are generic, so the skill may appear for broad pipeline or workflow requests where a more specific skill is a better fit. <br>\nMitigation: Confirm the user wants lightweight Python orchestration guidance before applying this skill's examples or library code. <br>\nRisk: Pipeline nodes execute user-provided Python callables, so unsafe callables can still perform unintended local actions. <br>\nMitigation: Review node functions before execution and run untrusted pipeline code in an appropriate isolated environment. <br>\n\n\n## Reference(s): <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, code, shell commands, guidance] <br>\n**Output Format:** [Markdown with Python code examples and shell commands] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May include Mermaid flowchart text generated by the pipeline visualization helper.] <br>\n\n## Skill Version(s): <br>\n0.1.0 (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 v1.0.0: 5 files, 9563 bytes\n\nFiles: scripts/pipeline_demo.py (11797b), scripts/pipeline.py (12815b), skill-card.md (1946b), SKILL.md (2272b), _meta.json (137b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: python-flow-engine\nversion: \"1.0.0\"\ndescription: \"Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation that needs serial/parallel/conditional execution. Triggers: pipeline, flow, orchestrate, workflow, ETL, data pipeline, process chain.\"\n---\n\n# Python Flow Engine\n\nLightweight Python flow orchestration — no external dependencies. Build pipelines with serial (>>), parallel (//), and conditional (|) node execution.\n\n## Core API\n\n### Node\n\n```python\nfrom pipeline import Node\n\ndef my_fn(context, data):\n    # context: shared dict for the pipeline run\n    # data: input from previous node\n    return modified_data\n\nnode = Node(my_fn, name=\"step1\", timeout=10, retry=1)\n```\n\n### Pipeline\n\n```python\npipe = Pipeline()\npipe.add_node(node)\npipe.run(start_node, input_data)\n```\n\n### Connection Operators\n\n| Operator | Mode | Behavior |\n|----------|------|----------|\n| `a >> b` | Serial | a runs, then b runs with a's output |\n| `a // b` | Parallel | a and b run concurrently (bidirectional) |\n| `a \\| b` | Conditional | b runs based on a's output (use `connect`) |\n\nFor conditional connections, use `connect()`:\n\n```python\npipe.connect(node_a, node_b, \"conditional\",\n    condition=lambda ctx, data: ctx.get(\"is_valid\", False))\n```\n\nOr use `Node.condition` parameter:\n\n```python\nnode_b = Node(fn, condition=lambda ctx, data: data > 0)\n```\n\n### Visualization\n\n```python\nprint(pipe.visualize())  # outputs Mermaid.js flowchart\n```\n\n### Error Handling\n\n- **retry**: `Node(fn, retry=2)` retries up to 3 total attempts\n- **timeout**: `Node(fn, timeout=5)` raises `RuntimeError` if execution exceeds 5s\n\n## Available Scripts\n\n- `scripts/pipeline.py` — Core library (importable, no dependencies)\n- `scripts/pipeline_demo.py` — End-to-end demo (6 scenarios)\n\n## Quick Start\n\n```python\nfrom pipeline import Node, Pipeline\n\ndef double(ctx, x): return x * 2\ndef add_one(ctx, x): return x + 1\n\npipe = Pipeline()\nn1 = Node(double, name=\"double\")\nn2 = Node(add_one, name=\"add_one\")\npipe.add_node(n1)\npipe.add_node(n2)\nn1 >> n2\n\nresult = pipe.run(n1, 5)  # 5*2+1 = 11\nprint(result)  # 11\nprint(pipe.visualize())\n```\n\nRun the full demo:\n```\npython scripts/pipeline_demo.py\n```\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn71mebbt22z0hvy3y0s8j50s183m8ba\",\n  \"slug\": \"python-flow-engine\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1779993635905\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nPython Flow Engine is a lightweight Python flow orchestration library for serial, parallel, and conditional data-processing workflows.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dnaxxx-hub](https://clawhub.ai/user/dnaxxx-hub)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and engineers use this skill to build Python data pipelines, ETL workflows, and multi-step automations with serial, parallel, conditional, retry, timeout, and Mermaid visualization support.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Node callables execute in process and can perform any side effects available to the running Python process.\n\nMitigation: Run only trusted callables and review pipeline code before deployment.\n\nRisk: Timeout behavior is advisory and may not stop side-effecting work after a timeout is reported.\n\nMitigation: Do not rely on the timeout setting to stop file writes, API calls, database updates, or expensive computation; use idempotent tasks and external cancellation controls where needed.\n\n## Reference(s):\n\n- [Python Flow Engine ClawHub page](https://clawhub.ai/dnaxxx-hub/skills/python-flow-engine)\n\n## Skill Output:\n\n**Output Type(s):** [Code, Markdown, Shell commands, Configuration instructions, Guidance]\n\n**Output Format:** [Markdown with Python code snippets, shell commands, and Mermaid flowchart text]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Produces reusable Python pipeline code and examples; pipeline visualization output is Mermaid-compatible text.]\n\n## Skill Version(s):\n\n1.0.0 (source: frontmatter and server release metadata)\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.","readmeExcerpt":"Skill: Python Flow Engine Owner: dnaxxx-hub Summary: Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation tha... Tags: flow:1.0.0, latest:1.0.0, orchestration:1.0.0, pipeline:1.0.0, python:1.0.0, workflow:1.0.0 Version history: v0.1.0 | 2026-05-28T22:27:30.962Z | user Initial: lightweight Python flow orchestration lib","codeSnippets":[],"executableExamples":[{"language":"python","snippet":"from pipeline import Node\n\ndef my_fn(context, data):\n    # context: shared dict for the pipeline run\n    # data: input from previous node\n    return modified_data\n\nnode = Node(my_fn, name=\"step1\", timeout=10, retry=1)"},{"language":"python","snippet":"pipe = Pipeline()\npipe.add_node(node)\npipe.run(start_node, input_data)"},{"language":"python","snippet":"pipe.connect(node_a, node_b, \"conditional\",\n    condition=lambda ctx, data: ctx.get(\"is_valid\", False))"},{"language":"python","snippet":"node_b = Node(fn, condition=lambda ctx, data: data > 0)"},{"language":"python","snippet":"print(pipe.visualize())  # outputs Mermaid.js flowchart"},{"language":"python","snippet":"from pipeline import Node, Pipeline\n\ndef double(ctx, x): return x * 2\ndef add_one(ctx, x): return x + 1\n\npipe = Pipeline()\nn1 = Node(double, name=\"double\")\nn2 = Node(add_one, name=\"add_one\")\npipe.add_node(n1)\npipe.add_node(n2)\nn1 >> n2\n\nresult = pipe.run(n1, 5)  # 5*2+1 = 11\nprint(result)  # 11\nprint(pipe.visualize())"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: python-flow-engine\ndescription: \"Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation that needs serial/parallel/conditional execution. Triggers: pipeline, flow, orchestrate, workflow, ETL, data pipeline, process chain.\"\n---\n\n# Python Flow Engine\n\nLightweight Python flow orchestration — no external dependencies. Build pipelines with serial (>>), parallel (//), and conditional (|) node execution.\n\n## Core API\n\n### Node\n\n```python\nfrom pipeline import Node\n\ndef my_fn(context, data):\n    # context: shared dict for the pipeline run\n    # data: input from previous node\n    return modified_data\n\nnode = Node(my_fn, name=\"step1\", timeout=10, retry=1)\n```\n\n### Pipeline\n\n```python\npipe = Pipeline()\npipe.add_node(node)\npipe.run(start_node, input_data)\n```\n\n### Connection Operators\n\n| Operator | Mode | Behavior |\n|----------|------|----------|\n| `a >> b` | Serial | a runs, then b runs with a's output |\n| `a // b` | Parallel | a and b run concurrently (bidirectional) |\n| `a \\| b` | Conditional | b runs based on a's output (use `connect`) |\n\nFor conditional connections, use `connect()`:\n\n```python\npipe.connect(node_a, node_b, \"conditional\",\n    condition=lambda ctx, data: ctx.get(\"is_valid\", False))\n```\n\nOr use `Node.condition` parameter:\n\n```python\nnode_b = Node(fn, condition=lambda ctx, data: data > 0)\n```\n\n### Visualization\n\n```python\nprint(pipe.visualize())  # outputs Mermaid.js flowchart\n```\n\n### Error Handling\n\n- **retry**: `Node(fn, retry=2)` retries up to 3 total attempts\n- **timeout**: `Node(fn, timeout=5)` raises `RuntimeError` if execution exceeds 5s\n\n## Available Scripts\n\n- `scripts/pipeline.py` — Core library (importable, no dependencies)\n- `scripts/pipeline_demo.py` — End-to-end demo (6 scenarios)\n\n## Quick Start\n\n```python\nfrom pipeline import Node, Pipeline\n\ndef double(ctx, x): return x * 2\ndef add_one(ctx, x): return x + 1\n\npipe = Pipeline()\nn1 = Node(double, name=\"double\")\nn2 = Node(add_one, name=\"add_one\")\npipe.add_node(n1)\npipe.add_node(n2)\nn1 >> n2\n\nresult = pipe.run(n1, 5)  # 5*2+1 = 11\nprint(result)  # 11\nprint(pipe.visualize())\n```\n\nRun the full demo:\n```\npython scripts/pipeline_demo.py\n```"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn71mebbt22z0hvy3y0s8j50s183m8ba\",\n  \"slug\": \"python-flow-engine\",\n  \"version\": \"0.1.0\",\n  \"publishedAt\": 1780007250962\n}"},{"path":"skill-card.md","content":"## Description: <br>\nLightweight Python flow orchestration library for building data processing pipelines, workflow automation, ETL tasks, and multi-step computations with serial, parallel, and conditional execution. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[dnaxxx-hub](https://clawhub.ai/user/dnaxxx-hub) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nDevelopers and engineers use this skill to build or explain lightweight Python pipelines that coordinate callable steps with serial, parallel, conditional, retry, timeout, and Mermaid visualization support. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The trigger words are generic, so the skill may appear for broad pipeline or workflow requests where a more specific skill is a better fit. <br>\nMitigation: Confirm the user wants lightweight Python orchestration guidance before applying this skill's examples or library code. <br>\nRisk: Pipeline nodes execute user-provided Python callables, so unsafe callables can still perform unintended local actions. <br>\nMitigation: Review node functions before execution and run untrusted pipeline code in an appropriate isolated environment. <br>\n\n\n## Reference(s): <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, code, shell commands, guidance] <br>\n**Output Format:** [Markdown with Python code examples and shell commands] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May include Mermaid flowchart text generated by the pipeline visualization helper.] <br>\n\n## Skill Version(s): <br>\n0.1.0 (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>"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation tha... Skill: Python Flow Engine Owner: dnaxxx-hub Summary: Lightweight Python flow orchestration library. Use when building data processing pipelines, workflow automation, ETL tasks, or any multi-step computation tha... 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