summarization

name	summarization
description	Transform large, noisy, or short-term memory into compact, durable, high-signal summaries. Activate when session memory grows large, decisions accumulate, or memory retrieval starts returning too many files.
license	MIT
compatibility	Works with any filesystem-based AI coding agent
metadata	{"author":"gaai-framework","version":"1.0","category":"cross","track":"cross-cutting","id":"SKILL-SUMMARIZATION-001","updated_at":"2026-01-30T00:00:00.000Z","status":"stable"}
inputs	["contexts/memory/index.md (registry — read first to discover all active categories)","contexts/memory/** (any category registered in index.md — resolved at runtime)"]
outputs	["contexts/memory/summaries/*.summary.md","contexts/memory/archive/**","contexts/memory/index.md (updated)"]

Summarization

Purpose / When to Activate

Activate when:

• Session memory grows large
• Decisions accumulate across sessions
• Project context becomes fragmented
• Memory retrieval returns too many files
• Token usage increases noticeably

This skill is both preventive and corrective.

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Process

Step 0: Enumerate categories. Read contexts/memory/index.md. List all registered categories. For each category, assess whether summarization is warranted: trigger if >5 files in the category OR estimated token count >5000 tokens OR memory-retrieve is returning too many results for this category. Skip categories that do not meet any threshold.

For each category that meets the threshold:

1. Identify durable information — extract confirmed decisions, stable constraints, validated assumptions, current priorities, key outcomes, known risks. Ignore brainstorming noise, intermediate reasoning, abandoned ideas.

2. Compress into structured summary — use the template below. Prefer bullets over prose.

3. Archive raw memory — move original files to contexts/memory/archive/{category}-{YYYY-MM-DD}.archive.md. Concatenate multiple files from the same category into a single archive file. Only summaries remain active.

4. Update memory index — record new summary files, archived sources, affected categories.

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Summary File Template

# {Category} — Summary

> Summarized from {N} files on {YYYY-MM-DD}
> Compression: ~{original_tokens} → ~{summary_tokens} ({percentage}%)

## Decisions

- {bullet per confirmed decision}

## Constraints

- {bullet per active constraint}

## Priorities

- {bullet per current priority}

## Open Questions

- {bullet per unresolved question, if still relevant}

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Outputs

• contexts/memory/summaries/{category}.summary.md — compressed, structured summary for each processed category
• contexts/memory/archive/{category}-{YYYY-MM-DD}.archive.md — concatenated originals, retained for audit
• contexts/memory/index.md — updated to reflect new summaries and archived sources

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Quality Checks

A good summary:

• Summary token count is ≤20% of original — verify before replacing active memory
• Every summarized category has its archive file created before the summary replaces it
• Preserves all actionable knowledge
• Removes all conversational fluff
• Supports future decisions without rereading history

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Non-Goals

This skill must NOT:

• Invent new knowledge
• Reinterpret decisions
• Remove active constraints
• Keep long narrative text

Distill knowledge. Delete noise. Small, sharp context always beats full history.