auto-summary

name	auto-summary
context	fork
skill	auto-summary
model	haiku
description	Batch populate summary fields using content analysis
tags	["activity/governance","domain/tooling","automation"]

/auto-summary

Batch populate the summary frontmatter field on notes where it is null or missing. The summary field is the highest-value AI triage field — it enables fast note discovery without reading full content.

Usage

/auto-summary                    # Process all notes with null summary
/auto-summary <path>             # Process specific file or folder
/auto-summary --dry-run          # Preview summaries without applying
/auto-summary --type Concept     # Only process specific note type
/auto-summary --limit 50         # Process at most 50 notes

Instructions

Phase 1: Find Notes Needing Summary

Use Grep to find notes with null or missing summary:

# Find notes with summary: null
Grep for "summary: null" in *.md files

# Also find notes with no summary field at all
# (These should have had summary added by template cleanup)

Filter results:

• Exclude Templates/, .obsidian/, .claude/, Archive/, Attachments/
• Exclude Daily notes (journals don't need summaries)
• If --type specified, filter by frontmatter type
• Sort by type for batch efficiency

Phase 2: Generate Summaries (Parallel)

Launch parallel Haiku sub-agents, batching 20 notes per agent:

For each note, the agent should:

1. Read the full note (frontmatter + body)
2. Generate a one-line summary following these rules:

Summary Writing Rules

• Length: 10-25 words. One sentence. No period at the end
• Voice: Active, descriptive. State what the note IS or DOES
• Content: Capture the core purpose, not details
• Avoid: Starting with "This note...", "A document about...", "Summary of..."
• Include: Key entities, technologies, or decisions where relevant

Summary Patterns by Type

Type	Pattern	Example
Concept	What X is	Continuous Airworthiness Management Organisation responsible for aircraft safety compliance
Pattern	How to do X	Event-driven architecture pattern using Kafka for real-time system integration
Meeting	What was discussed/decided	Alpha sprint review covering data migration progress and API blockers
ADR	What was decided and why	Selected AWS Bedrock over Azure OpenAI for AlertHub safety processing
Project	What the project delivers	SAP to DataPlatform data integration enabling unified engineering analytics
System	What the system does	MRO Vendor MRO platform managing aircraft maintenance scheduling
Person	Role and context	Solutions Architect in Engineering IT, Alpha project lead
Task	What needs to be done	Implement Kafka consumer for Alpha work order events
Incubator	What idea is being explored	Exploring voice-activated Claude Code workflows for hands-free note capture
Research	What question was investigated	Analysis of vault structure identifying efficiency improvements for human and AI workflows
Reference	What the resource covers/teaches	AWS documentation on Bedrock guardrails for AI model safety
Email	What the email communicates	Proposal to Beta programme board for Claude Code adoption across architecture team

3. Return — list of (filepath, summary) tuples

Phase 3: Apply Summaries

For each note with a generated summary:

1. Read current frontmatter
2. If summary: null — replace with generated summary (quoted string)
3. If no summary field — add summary: field after tags
4. Write updated file using Edit tool

Format:

summary: "Continuous Airworthiness Management Organisation responsible for aircraft safety compliance"

Always quote the summary value since it may contain special YAML characters (colons, brackets).

Phase 4: Report

## Auto-Summary Results

**Notes processed:** {{count}}
**Summaries added:** {{added_count}}
**Notes skipped:** {{skipped_count}} (already has summary or insufficient content)

### By Type
| Type | Summarised | Avg Length |
|------|-----------|------------|
| Concept | 45 | 15 words |
| Meeting | 80 | 18 words |
| ADR | 30 | 20 words |

### Sample Summaries
| Note | Summary |
|------|---------|
| {{note}} | {{summary}} |
| {{note}} | {{summary}} |

Safety

• Always use --dry-run first for vault-wide operations
• Never overwrite existing non-null summaries
• Commit to git before running
• If note body is too short (<50 words), skip rather than guess
• Summary is additive only — never removes existing summaries

Quality Checks

After running, verify quality by:

1. Spot-check 10-15 summaries across different types
2. Ensure summaries are accurate (not hallucinated)
3. Check length (10-25 words target)
4. Verify no YAML quoting issues

Related Skills

• /summarize — Detailed single-note summarisation
• /quality-report — Includes summary coverage metrics
• /auto-tag — Companion batch field population skill