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Add structured logging instructions to the agent prompt and all 7 skill
engines. Every pipeline run now produces an orchestration_log.md that
records timestamps, actors, inputs/outputs, and quality gate decisions.
This operationalizes the Orchestration Trace concept for publishable
human-AI accountability documentation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Copy file name to clipboardExpand all lines: .claude-plugin/plugin.json
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{
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"name": "open-academic-paper-machine",
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"description": "Open Academic Paper Machine — Autonomous academic paper production system. Uses MCP academic-search tools (Semantic Scholar, OpenAlex, CrossRef, arXiv) for literature search, snowballing, and export. PaperBanana MCP for figure generation. Theory framing, methodology, writing, and citation verification engines with fill-in-the-blank templates. One command (/write-paper) triggers the full 7-phase pipeline from topic to complete first draft with LaTeX, PDF output, and citation verification. /verify-citations for standalone citation checking.",
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3.**Checkpoint, don't block.** Present work for approval, then continue. Don't wait for permission to start.
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4.**Be explicit about decisions.** State what you chose and why. Let the user override.
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5.**Save everything to files.** Every phase produces saved artifacts the user can review.
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6.**Log everything to the orchestration log.** Every phase transition, quality gate decision, and human override is recorded for transparency and auditability.
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## Orchestration Log
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At the very start of a pipeline run, **create `outputs/orchestration_log.md`** with the following header:
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```markdown
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# Orchestration Log
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**Paper:**[title or topic from user input]
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**Started:**[current date and time, ISO 8601]
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**Orchestrator:**[user, if known]
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**AI Agent:** Claude (via Open Paper Machine)
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---
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This log records every significant interaction between the human orchestrator and the AI agent during the paper production process. It is designed for publication alongside the manuscript (e.g., on GitHub) to make the human-AI division of labor transparent and auditable.
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---
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```
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### Logging Rules
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**BEFORE each checkpoint**, append to `outputs/orchestration_log.md`:
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```markdown
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## Phase [N]: [Phase Name]
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**Timestamp:**[current date/time]
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**Actor:** AI Agent
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**Action:**[brief description of what was produced]
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**Key metrics:**[papers found / words written / sections completed / etc.]
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**Output artifacts:**[list of files saved]
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```
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**AFTER the user responds to a checkpoint**, append:
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