Log exploratory work, archive files, and capture Git state.
npx skills add https://github.com/robdmc/claude_tools --skill scribe-robdmc
Please help me install this Agent Skill. Skill: scribe Source: https://github.com/robdmc/claude_tools/tree/main/scribe/skills Command: npx skills add https://github.com/robdmc/claude_tools --skill scribe-robdmc
This Skill helps researchers and developers keep a structured, auditable narrative of exploratory work by logging entries, archiving relevant files, and capturing the Git state for reproducibility.
Start a new entry by running the prepare subcommand, fill in the title/body, optionally archive files, then finalize to append to the daily log.
High-intent search queries and answers about installing and using this skill.
To maintain a living log of experiments, use a propose-confirm workflow to capture decisions and outcomes, structure entries with frontmatter, and append finalized records to a daily log for retrieval by time or topic.
You capture Git state to anchor research experiments by recording the repository state and optionally committing the log entry, ensuring your exploratory work remains reproducible and auditable alongside code changes.
The best way to archive files in iterative research workflows is to stage important files during your session and retrieve them later for review, preserving the context of your exploratory work.
Yes, you need PyYAML installed to enforce structured frontmatter for research logging, as the dependency enables proper parsing and validation of the metadata anchoring your experiment entries.
Yes, you can retrieve archived experiment files by time, topic, or related entries, because the logging workflow enforces structured frontmatter that organizes records for targeted retrieval during iterative research.
A limitation of using a daily log for exploratory research is that entries are appended sequentially, meaning retrieval depends on structured frontmatter and manual staging rather than automatic relational database querying.
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