plugins/agent-scaffolders/skills/create-agentic-workflow/SKILL.md
Scaffold a GitHub agentic workflow from an existing skill
npx skillsauth add richfrem/agent-plugins-skills create-agentic-workflowInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Follow the create-agentic-workflow skill workflow to convert a local skill into a
GitHub-native agentic workflow.
$1 — path to the existing skill directory to convert$2 — optional mode: ide (Copilot Chat slash command), cicd (autonomous quality gate),
or both. Defaults to guided selection if omitted.$1 provides a skill directory, resolve and validate the path; otherwise askscaffold_agentic_workflow.py with the gathered parametersgh aw compile, etc.)Generated GitHub workflow YAML and/or Copilot agent definition files, with implementation notes for the chosen agent type and pattern.
$ARGUMENTS is empty: begin with Phase 0 complexity gate, then guided discoverytools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.