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Each has its own role, goal, and allowed actions.\n- **Review loop.** The Reviewer can send a draft back to the Writer with specific, structured critique. Default loop cap is 2 iterations.\n- **Three output formats** per source: Twitter thread, LinkedIn post, email newsletter.\n- **Prompt-sensitivity study.** Run the same crew against a single source with N prompt variants and measure output variance.\n- **Runs without any API key** via `MockLLM` that produces deterministic, plausible outputs. Swap to real CrewAI + LiteLLM by flipping a backend flag.\n- **Full transcripts** written to `runs/<run_id>/` for every run.\n\n## Quickstart\n\n```bash\npip install -r requirements.txt\n\n# Repurpose a single source file into 3 formats\npython -m src.main run --source examples/sample_input.md\n\n# Study prompt sensitivity (produces a report)\npython -m src.main sensitivity --source examples/sample_input.md --n 8\n\n# Use real CrewAI + LLM (optional)\nexport OPENAI_API_KEY=sk-...\npython -m src.main run --source examples/sample_input.md --backend crewai\n```\n\n## Example output\n\n```\nruns/run_20260422_153011/\n├── transcript.json\n├── research.md\n├── draft_twitter.md\n├── draft_twitter_v2.md        ← after reviewer critique\n├── draft_linkedin.md\n├── draft_email.md\n├── critique_twitter.md\n└── final.md\n```\n\n## Architecture\n\n```\n                        ┌─────────────────┐\n                        │   Researcher    │  extracts key claims, audience,\n                        │   (readonly)    │  tone, do-not-say list\n                        └────────┬────────┘\n                                 │ research brief\n                                 ▼\n        ┌──────────────┐  ┌─────────────────┐  ┌──────────────┐\n        │   Writer     │─▶│    Draft v1     │─▶│   Reviewer   │\n        └──────────────┘  └─────────────────┘  └──────┬───────┘\n              ▲                                       │\n              │                                       │ critique\n              └───────────── revision ←───────────────┘\n                                 │\n                                 ▼\n                           Draft v_final\n```\n\n## Project structure\n\n```\nmulti-agent-content-repurposer/\n├── src/\n│   ├── agents.py          # Agent classes + roles\n│   ├── crew.py            # Orchestrator + review loop\n│   ├── backends.py        # LLM abstraction\n│   ├── sensitivity.py     # Prompt-perturbation study\n│   ├── formats.py         # Output format specs\n│   └── main.py            # CLI\n├── examples/\n│   └── sample_input.md    # A blog post about AI interpretability\n├── tests/\n│   ├── test_agents.py\n│   ├── test_crew.py\n│   └── test_sensitivity.py\n└── requirements.txt\n```\n\n## Design decisions\n\n- **Critique is structured, not free-text.** The Reviewer returns a JSON object with specific axes (length, tone, accuracy, format adherence). This is auditable and makes the revision loop convergent rather than drifting.\n- **The loop has a cap.** Two revisions is the default. After that, ship what we have or escalate. Unbounded critique loops are a classic multi-agent anti-pattern; the cap is deliberate.\n- **The Researcher is read-only.** It cannot write the draft. This prevents the common failure mode where the research step and the writing step get fused and the final output cites nothing.\n- **Every run writes a transcript.** If you can't read what each agent said to each other, you cannot debug multi-agent behavior. Transcripts are the first thing I added, not the last.\n\n## Prompt-sensitivity study\n\nMulti-agent systems are notoriously sensitive to minor prompt wording. The `sensitivity` command runs the same crew N times over the same input, perturbing the writer prompt each run. It then measures:\n\n- **Length variance** (chars per output).\n- **Vocabulary overlap** (Jaccard similarity between runs).\n- **Structural drift** (did the output hit the required format?).\n\nSee `reports/SENSITIVITY.md` (generated) for a written analysis.\n\n## License\n\nMIT\n","readmeExcerpt":"Multi-Agent Content Repurposer A CrewAI-style multi-agent workflow that turns one source document into Twitter, LinkedIn, and email versions — with an inter-agent review loop and a prompt-sensitivity study built in as a first-class artifact. 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