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A screenshot shows what worked once. FlowStacks shows what still works: CI re-runs every recipe on each change, so the badge goes red the moment one breaks. Browse by tool, by use case, or by what it replaces.
$ npm run verify ✓ obsidian-claude-weekly-review 5/5 ✓ tldraw-make-real-sketch-to-code 5/5 ✓ obsidian-agent-harness 7/7 ✓ dedupe-and-rank-a-keyword-list 2/2 38/38 fixtures green · machine-verified
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Popular workflows
View all →One AGENTS.md, no drift: prove CLAUDE.md is a real symlink and the generated files are in sync
Keep one AGENTS.md as the single source of agent rules, symlink CLAUDE.md to it, and prove with CI that the symlink is committed as a symlink (not a Windows-materialised copy) and that any generated per-tool file byte-matches a fresh regeneration from the source, so nothing silently drifts.
One shared memory for every coding agent: prove the configs actually point at the same server
Wire a Markdown memory MCP server (Basic Memory or an Obsidian MCP) into Claude Code, Cursor and Cline, and prove with CI that all three resolve to one server, that a write-the-memory house rule exists, and that the vault is under git before any agent gets write access.
Self-hosting the open-source stack? Prove your backup actually restores before you need it
Make the one self-hosting discipline that matters a machine check: back up your database, destroy the live copy, restore from the backup, and assert the restored data matches the original exactly, so you find a broken backup in CI instead of at 2am.
Prove your meeting-notes pipeline never phones home (and gates on consent)
Run capture -> whisper.cpp transcription -> Ollama summary fully on your machine, with a CI check that every endpoint is loopback, no cloud host or API key appears anywhere in the config, and recording is gated on a consent acknowledgment.
Validate a WrenAI semantic model's references before an agent queries through it
Before letting an agent query through WrenAI's governed semantic layer (MDL), validate that every relationship and metric resolves to a model and column that actually exist, so a stale definition fails a check instead of quietly returning wrong-but-plausible numbers.
Chat with a CSV, but pin a known-answer guardrail so a wrong query cannot pass
Ask a CSV or dataframe questions in plain English with PandasAI, but wrap it in a deterministic known-answer check so a confident-but-wrong generated query is caught instead of trusted.
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