zettel-brainstormer

Build structured brainstorming notes from a seed zettel by retrieving linked notes, preprocessing each note with subagents for relevance extraction, drafting...

This skill appears internally consistent and implements the described local-note brainstorming pipeline, but consider these practical cautions before installing: - Privacy: the workflow reads your local vault (zettel_dir) and the SKILL.md explicitly requires the agent to "ALWAYS include" the final list of references in chat replies — that may expose note titles and absolute file paths in conversation logs. If you keep sensitive notes in the vault, either avoid using the skill or remove/segregate sensitive files first. - External dependency: the skill will optionally call an external `zettel-link` skill if available. Review that other skill before allowing it to run, since its semantic retrieval behavior could include different I/O or network interactions. - Model calls & cost: the workflow spawns one subagent per candidate note plus drafting/publisher subagents. This can result in many model invocations (cost and data sent to your model provider). Confirm which models are configured in config/models.json and whether those model invocations are acceptable for your privacy/cost constraints. - Local writes: the setup script saves config to config/models.json and the scripts write intermediate outputs (e.g., /tmp). If you prefer not to persist config, run setup in a safe environment or edit config manually. - Audit before use: because this is instruction + script bundle, you can safely inspect and run the Python scripts in a sandbox to confirm behavior. If you plan to allow autonomous agent invocation, limit the agent's permissions or review platform audit logs so you can see which notes were accessed. If none of these concerns are blocking, the skill is consistent with its stated purpose and reasonable to use.

Type: OpenClaw Skill Name: zettel-brainstormer Version: 1.1.2 The zettel-brainstormer skill bundle is a well-structured tool for synthesizing Obsidian/Zettelkasten notes into blog posts. The Python scripts (scripts/find_links.py, scripts/compile_preprocess.py, scripts/obsidian_utils.py) focus on local file I/O, regex-based parsing of wikilinks and tags, and relevance filtering without any evidence of network exfiltration or dangerous shell execution. The agent instructions in SKILL.md and the agents/ directory are strictly aligned with the stated purpose of research and drafting, and no malicious prompt injection or unauthorized access patterns were detected.