skills/knowledge-distillation-from-discussion/SKILL.md
Distills a raw, text-based discussion (e.g., meeting notes, user-AI conversations in Markdown) into one or more structured, permanent 'evergreen' knowledge notes.
npx skillsauth add jaimeparker/stable-jarvis knowledge-distillation-from-discussionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Distills a raw, text-based discussion (e.g., meeting notes, user-AI conversations in Markdown) into one or more structured, permanent 'evergreen' knowledge notes. This skill transforms a chronological 'fleeting note' into conceptual 'permanent notes', following the Zettelkasten/Evergreen methodology. It is designed to extract key insights, decisions, and concepts for a user's personal knowledge base (PKB).
Trigger: Use when the user wants to process a text-based discussion, conversation, or meeting notes to extract durable knowledge.
Input: The path to the source Markdown discussion note in the user's Obsidian vault. Output: Creates one or more new, atomic notes with declarative titles, updates relevant MOCs, and moves the original source note to the discussions archive.
Follow this 4-stage process to execute the skill.
ask_user(question="Please provide the path to the Markdown discussion note you want to process.")mcp_obsidian_read_note to load the content of the source file. Treat this content as a temporary "fleeting note".Select Distillation Lens: The goal is to extract specific types of insights, not just a generic summary. Ask the user what "lens" they want to apply to the discussion. Use ask_user with the following choices:
Execute Distillation: Based on the user's choice, apply the corresponding advanced prompt to the text content you read in Step 1.
"Analyze this discussion and extract the 1-3 core principles or 'atomic ideas' that were debated. For each idea, provide a one-sentence summary. Then, for each idea, detail the main supporting argument and any counter-arguments that were raised. Format the output so it can be structured into 'Core Insight' and 'Supporting Arguments' sections."
"Review this discussion transcript. Format the output to clearly separate the 'Problem', 'Final Decision', and 'Action Items'. For the final decision, explain the consensus that was reached. For action items, list them as a Markdown checklist."
"Act as a CTO reviewing a technical discussion. Structure the output to clearly distinguish between 'Strategic Implications', 'Trade-offs', and 'Risks', based on the content of the discussion."
Propose Title: Based on the distilled output from the LLM, formulate a declarative title for a new, permanent note. The title should be a full sentence that captures the core insight (e.g., "Decoupling navigation and manipulation simplifies aerial grasping policies").
Confirm Title and Path: Present the proposed title to the user and ask them to confirm or edit it. At the same time, ask for the desired folder path for this new permanent note (e.g., 30 Zettelkasten/32 Permanent/).
ask_user(questions=[...])Structure Content: Create the Markdown content for the new note using the exact template below. You MUST map the insights from the distillation lens chosen in Step 2 into the appropriate sections of this template.
Template:
---
tags: [distilled-insight, add-2-to-3-relevant-topic-tags]
date: {{current_date}}
type: evergreen
---
# {{The Declarative Title}}
**TL;DR:** [Write a one-sentence summary of the core insight or decision.]
### Context / Problem
[Based on the distilled output, briefly describe the context or the problem that was being discussed.]
### Core Insight / Decision
[Place the primary distilled knowledge here. For example, if the lens was 'Outcomes & Decisions', this section should contain the final consensus. If the lens was 'Core Concepts', this should contain the main principle.]
### Supporting Arguments / Caveats
[Place secondary information here. For example, supporting arguments, counter-arguments, unresolved issues, or identified risks and trade-offs.]
---
Source: [[50 Archive/Discussions/{{original_filename}}]]
Write Note: Use mcp_obsidian_write_note to save the new evergreen note to the confirmed path.
mcp_obsidian_search_notes with the query "MOC" to find potentially relevant Maps of Content.mcp_obsidian_patch_note or mcp_obsidian_read_note followed by mcp_obsidian_write_note to add the wikilink to the selected MOC(s). Provide a brief summary of the new note's context next to the link.50 Archive/Discussions/ + (original filename).mcp_obsidian_move_note. The oldPath is the source path from Step 1, and the newPath is the constructed archive path.50 Archive/Discussions/.research
精读文献。快速泛读请用paper-quick-read。
data-ai
泛读:快速概览Zotero库中的文献,单轮LLM生成摘要级Markdown报告,并上传为Zotero Note。深度精读请用paper-deep-reader。
tools
# Obsidian Semantic Search Search Obsidian vault notes by conceptual meaning, not just keywords. Builds a local embedding index over all vault notes (per-section chunking), then performs cosine similarity search at query time. ## When to Use - User asks a fuzzy/conceptual question about their own notes ("What papers discuss exploration in RL?") - Keyword search (`mcp__obsidian__search_notes`) returns too many or too few results - User wants to find notes related to a concept without knowing e
development
Expert 10x engineer with comprehensive knowledge of web development, internet protocols, and web standards. Use when working with HTML, CSS, JavaScript, web APIs, HTTP/HTTPS, web security, performance optimization, accessibility, or any web/internet concepts. Specializes in translating web terminology accurately and implementing modern web standards across frontend and backend development.