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zettel-brainstormer泽特尔集思广益

Agent Skill

zettel-brainstormer 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:zettel-brainstormer(泽特尔集思广益)
来源仓库:https://github.com/hxy9243/zettel-brainstormer
安装命令:
openclaw skills install zettel-brainstormer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install zettel-brainstormer

简介

基于链接笔记的结构化头脑风暴构建工具。zettel-brainstormer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 适合知识管理和创意发散阶段的文档整理需求。
  • 通过子代理预处理实现相关性提取和结构化组织。
  • 安装命令:openclaw skills install zettel-brainstormer
  • 需确认笔记存储位置和访问权限,确保数据安全

SKILL.md

name
zettel-brainstormer
description
Build structured brainstorming notes from a seed zettel by retrieving linked notes, preprocessing each note with subagents for relevance extraction, drafting with cited evidence, and publishing a natural blog-style post with a final References section. Use when asked to expand, research, synthesize, or publish from local Obsidian/Zettelkasten notes.

Zettel Brainstormer

Run this workflow in order. Keep each stage separate so relevance decisions happen before drafting.

Configure Once

  1. Run setup:
python zettel-brainstormer/scripts/setup.py
  1. Confirm zettel-brainstormer/config/models.json contains:
  • zettel_dir
  • output_dir
  • models and agent_models
  • retrieval.link_depth and retrieval.max_links

Stage 1: Retrieval

Goal: retrieve candidate notes from the seed note.

Required order for this stage:

  1. Read retrieval limits from config and target candidate count using retrieval.max_links.
  2. Check if the external zettel-link skill is available. If it exists, run semantic retrieval via its scripts/search.py command using the seed note's topic or title. If it doesn't exist, warn the user and skip this step.
  3. Run local retrieval with scripts/find_links.py to gather exact wikilinks and tag-overlap notes.
  4. Merge and deduplicate candidates from both sources. Prioritize semantic candidates first, and trim to the configured count.
  5. Exclude the seed note itself.

Local retrieval command:

python zettel-brainstormer/scripts/find_links.py \
  --input "/absolute/path/to/Seed Note.md" \
  --output /tmp/zettel_candidates.json

Treat /tmp/zettel_candidates.json as the candidate pool for preprocessing.

Stage 2: Preprocess (Subagent Per Note)

Goal: preprocess each candidate note and decide relevance to the seed note.

  1. Read agents/preprocess.md as the per-note instruction.
  2. Spawn one subagent per candidate note.
  3. For each note, require:
  • Relevance score against the seed note topic.
  • Concise summary.
  • Distinct key points.
  • Short evidence quotes when useful.
  1. Save each subagent output as markdown (one file per source note).

Quality rules:

  • Reject notes with weak relevance.
  • Prefer concrete claims and non-duplicated points.
  • Keep outputs compact and structured for downstream merge.

Stage 3: Draft (Synthesis Subagent)

Goal: gather only relevant preprocess outputs and generate a referenced draft.

  1. Run the aggregation helper:
python zettel-brainstormer/scripts/compile_preprocess.py \
  --seed "/absolute/path/to/Seed Note.md" \
  --preprocess-dir /tmp/zettel_preprocess \
  --output /tmp/zettel_draft_packet.json
  1. Read agents/draft.md.
  2. Use one drafting subagent with:
  • Seed note content
  • Filtered relevant notes from /tmp/zettel_draft_packet.json
  • Required citation mapping from the packet
  1. Produce a draft that cites source notes inline and preserves traceability.

Stage 4: Publish (Publisher Subagent)

Goal: rewrite the draft into natural long-form writing while preserving evidence quality.

  1. Read agents/publisher.md.
  2. Use one publisher subagent to rewrite the draft with these constraints:
  • Remove generic AI phrasing.
  • Use natural language and a coherent author voice.
  • Organize points with clear tiered argument structure.
  • Remove irrelevant points.
  • Do not force weak connections between notes.
  • Keep explicit citations for all retained claims.
  • Do not publish the draft's internal "Argument Spine" section.
  • Append valid frontmatter properties, including article-relevant tags.
  1. Always end with a ## References section listing every cited note.

Stage 5: Delivery

Goal: Present the final output to the user.

  1. Deliver or summarize the published draft for the user.
  2. Crucial: When responding to the user, ALWAYS include the final list of references/notes that were actually used and cited in the brainstorm.

Bundled Resources

  • agents/retriever.md: retrieval-stage instructions
  • agents/preprocess.md: per-note preprocessing instruction
  • agents/draft.md: synthesis drafting instruction
  • agents/publisher.md: publication rewrite instruction
  • scripts/find_links.py: retrieval script for wikilinks + tag overlap
  • scripts/compile_preprocess.py: filter and merge preprocess outputs into a draft packet
  • scripts/obsidian_utils.py: wikilink and tag helpers
  • scripts/config_manager.py: shared config loader
  • scripts/setup.py: interactive config setup

Maintenance Rules

  • Keep stage boundaries strict: retrieval -> preprocess -> draft -> publish.
  • Keep prompts in agents/ and scripts in scripts/.
  • Remove deprecated scripts instead of keeping parallel legacy paths.

适合场景

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用户想查找某类 Agent Skill 时

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能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install zettel-brainstormer 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

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