Workflow Memory Hub
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About this skill
Problem
Recurring tasks often depend on one-off instructions, leaving tool choices, parameters, and step order scattered across chat history. WorkflowHub externalizes these repeated working patterns into a local workflow library, with files as the source of truth instead of burying process knowledge in a temporary prompt.
How It Works And Boundaries
The skill operates in four stages: match the current request, confirm reuse, compile an execution brief, and save or update the workflow. Matching uses keywords, toolchain overlap, and recent session actions. Before execution, it generates a brief with the goal, ordered steps, and preferred tools rather than pasting raw JSON.
Key capabilities include:
- match_workflows.py for deterministic similarity checks
- render_workflow_prompt.py for drafting briefs
- new_workflow.py, capture_workflow.py, and save_workflow.py for capturing and saving
- ui/server.py for a local editable UI
Boundaries: it is not intended for one-off micro tasks. A match only suggests reuse; it does not auto-execute. Workflows with writes, external calls, or other side effects must be explicitly requested in the current turn.
Use Cases
- After a code review, save the checklist, tool order, and recurring keywords as a reusable `pr-review` workflow.
- When a similar review request arrives, match the saved workflow and ask whether to follow that SOP.
- While producing repeated data reports, draft the actual steps into a JSON workflow with `capture_workflow.py`.
- In the local UI, search and edit workflow keywords, steps, tool preferences, and last-updated metadata.
Best For
- Engineers who want to save recurring code-review processes as local SOPs
- Analysts or operations staff who repeat similar reports and want reusable tool and parameter order
- Developers using AI agents for multi-step tasks and wanting a local editor for workflow files
- Agent users who want personal cross-project work habits stored as diff-friendly JSON
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