Agent Skills: Make Distilled Content

Make Distilled Content

When to use

Use this skill when:

• The user has a raw file in raw/ and wants to extract structured insights
• You need to convert unprocessed source material into organized knowledge
• You're preparing content for integration into notes or essays

Keywords: distill, extract, summarize, patterns, principles, organize

Inputs

Required:

• raw_paths (list[string]): Relative or absolute paths to one or more raw files in raw/

Optional:

• focus (string): Specific aspect to emphasize (e.g., "patterns", "principles", "practical advice")
• distilled_filename (string): Output filename (useful when distilling multiple raw files into one distilled file)

Outputs

This skill produces:

1. A new file in distilled/ with the same timestamp-slug naming pattern as the raw file
2. An update to the raw file front matter to add a backlink to one (or more) distilled outputs
3. Metadata returned to the agent:
   • distilled_path: full path to the created distilled file
   • title: title of the distilled content
   • distilled_at: ISO timestamp (UTC)
   • status: initial status (typically "draft")

Procedure

1. Read the raw files

• Use the read_file tool to load each raw file content
• Parse front matter from each raw file to extract:
  • title
  • source_url
  • captured_at
  • capture_type
  • author (if available)
  • published_at (if available)
  • distilled_refs (if present; used for appending a new backlink)
  • any other relevant metadata

2. Generate distilled content structure

Extract and organize the following elements from the raw content:

Topics / outline (5-15 bullets)

• What gets discussed, in order
• Prefer timestamped bullets when the source provides timestamps (common for videos)
• Keep this distinct from “Key points” (outline is coverage; key points are takeaways)

Summary (5-10 lines)

• Concise overview of the main topic and purpose
• What problem/question does this address?
• What approach or solution is presented?

Key points (bullet list)

• 5-10 actionable or memorable insights
• Focus on practical takeaways
• Avoid restating the summary

Concepts / principles

• Abstract ideas or mental models
• Transferable patterns
• Underlying theory or philosophy
• Trade-offs and decision factors

Patterns (if relevant)

• Concrete, reusable approaches
• Workflow patterns
• Code/architectural patterns
• Team/organizational patterns

Entities (if relevant)

• Tools, frameworks, libraries mentioned
• People or organizations referenced
• Related projects or initiatives
• Terminology and definitions

Quotes (select 3-8 notable ones)

• Verbatim excerpts that are particularly insightful
• Include just enough context to be standalone
• Format as blockquotes with inline attribution if author is known

Open questions / follow-ups

• Gaps or areas for deeper research
• Connections to explore
• Uncertainties or ambiguities in the source

Next steps (agent-suggested)

• Suggested integration targets in notes/
• Related topics to capture or research
• Concepts worth expanding into standalone notes

3. Build front matter

Create YAML front matter for the distilled file:

---
title: "<original title> (or improved title if clearer)"
source_url: "<primary source URL (optional)>"
captured_at: "<captured_at from a representative raw file>"
distilled_at: "<current ISO timestamp UTC>"
raw_refs:
  - "[[raw/<raw file 1>]]"
  - "[[raw/<raw file 2>]]"
capture_type: "<capture_type from a representative raw file>"
status: draft
agent: github-copilot
model: claude-sonnet-4.5
confidence_notes: "<any uncertainty flags, hallucination risks, or missing context>"
tags: ["tag1", "tag2", "tag3"]  # 3-7 relevant tags
---

Critical rules:

• Use raw_refs as an Obsidian-style internal link list (e.g., [[raw/20260102-095107Z--patterns-for-ai-assisted-software-development]]).
• Always use raw_refs (plural) as a list, even when there is only one raw source.
• Also add (or append to) a backlink in each raw file front matter:
  • distilled_refs should be a YAML list of Obsidian-style internal links (e.g., [[distilled/20260102-095107Z--patterns-for-ai-assisted-software-development]])
• In YAML front matter, quote Obsidian links (e.g., - "[[raw/...]]") so the file stays valid YAML.
• Be explicit in confidence_notes if:
  • Author/date was inferred rather than explicit
  • Content was truncated or unclear
  • You're uncertain about any extraction
• The status should be draft unless the user specifies otherwise

4. Apply the distilled content template

Combine the extracted elements into this structure:

---
<front matter from step 3>
---

## Summary

<5-10 line summary>

## Topics

- <topic 1>
- <topic 2>
- ...

## Key points

- <point 1>
- <point 2>
- ...

## Concepts / principles

<extracted concepts, one per paragraph or sub-section>

## Patterns

<if relevant: concrete patterns found>

## Entities

<if relevant: tools, people, projects, terms>

## Quotes

> <quote 1>
> 
> — <attribution if known>

> <quote 2>

...

## Open questions / follow-ups

- <question or gap 1>
- <question or gap 2>

## Next steps

- <suggested action 1>
- <suggested action 2>

## Links

- Source: [<source_url>](<source_url>)
- (Optional) Additional external links extracted from the source (avoid duplicating `raw_refs` here; they live in front matter)
- <Other links extracted from content if relevant>

5. Generate filename and write the file

• If distilled_filename is provided, use it.
• Otherwise:
  • If raw_paths contains exactly one file, use the same timestamp and slug as that raw file
  • If raw_paths contains multiple files, generate a new YYYYMMDD-HHMMSSZ--<slug>.md filename based on the distilled title
• Full path: distilled/<filename>
• Content: front matter + blank line + distilled structure
• Use create_file tool with the full content

6. Update raw front matter (backlink)

• For each file in raw_paths:
  • Keep the raw body unchanged; only update front matter.
  • Add or update distilled_refs as a YAML list:
    • If distilled_refs is missing, add it.
    • If distilled_refs exists, append the new distilled path (avoid duplicates).
  • Use Obsidian-style internal links, e.g.:

distilled_refs:
  - "[[distilled/20260102-095107Z--patterns-for-ai-assisted-software-development]]"

7. Confirm to user

Provide a brief confirmation:

• Link to the created distilled file using an Obsidian-style internal link
• One-sentence summary of what was extracted
• Note any significant confidence_notes or gaps

Quality guidelines

Preserve intent and nuance

• Do not "improve" the source's tone or claims
• If something is speculative in the source, preserve that uncertainty
• Avoid marketing language or hype

Be explicit about uncertainty

• Use confidence_notes front matter for extraction risks
• In the body, flag inferred vs. explicit information
• If author/date is missing, say so

Favor principles over tools

• Extract transferable ideas, not tool-specific instructions
• Highlight trade-offs and decision factors
• Patterns should be adaptable across contexts

Short, precise, reusable

• Clarity over verbosity
• Make each section independently useful
• Link concepts instead of duplicating them

Avoid exposing private information

• Do not include private repository names, local paths, or personal data
• Sanitize examples if they reference non-public systems

Examples

Example 1: Blog post about AI agent patterns

Input:

raw_paths:
  - raw/20260102-095107Z--patterns-for-ai-assisted-software-development.md

Process:

1. Read the raw file and extract metadata
2. Identify main patterns: interview-driven specs, progressive disclosure, etc.
3. Extract key principles: preserve intent, think in systems, avoid hype
4. Select 5-6 notable quotes
5. Note any follow-up questions (e.g., "How do these patterns scale to larger teams?")

Output:

• Creates distilled/20260102-095107Z--patterns-for-ai-assisted-software-development.md
• Front matter includes raw_refs: ["[[raw/20260102-095107Z--patterns-for-ai-assisted-software-development]]"]
• Raw front matter updated with distilled_refs: ["[[distilled/20260102-095107Z--patterns-for-ai-assisted-software-development]]"]
• Structured summary + key points + concepts + quotes + next steps

Example 2: YouTube transcript

Input:

raw_paths:
  - raw/20260105-140000Z--building-production-agents.md

Process:

1. Read the transcript
2. Extract main themes (often less linear than written content)
3. Identify practical advice vs. theoretical discussion
4. Build a topic outline (timestamped if present in the source)
5. Note any tools or frameworks mentioned
6. Flag areas where transcript was unclear or incomplete

Output:

• Creates distilled/20260105-140000Z--building-production-agents.md
• confidence_notes might mention: "Transcript had several unclear segments; some technical terms may be misspelled"
• Entities section lists tools and frameworks discussed
• Raw front matter updated with distilled_refs (append if already present)

Failure modes and edge cases

Truncated or incomplete raw content

• Note in confidence_notes
• Extract what's available
• Suggest follow-up: "Consider re-capturing with full content"

Highly technical content with domain-specific jargon

• Preserve technical terms as-is
• Add brief context in Entities section if helpful
• Do not oversimplify at the cost of accuracy

Opinion pieces vs. technical guides

• For opinion: focus on arguments, not just conclusions
• For guides: emphasize patterns and principles over step-by-step

Multiple topics in one source

• Consider suggesting multiple distilled files focused on different aspects
• Or use clear sub-sections in a single distilled file

Tools typically used

• read_file — to load the raw content
• create_file — to write the distilled output
• update_file (or equivalent) — to update raw front matter with distilled_refs
• run_in_terminal — to generate UTC timestamp if needed

References

• Distillation pipeline: docs/distillation/distillation-pipeline.md
• Agent skills standard: notes/agent-skills.md
• Repository guidance: AGENTS.md