Claude-skill-registry engram-summarize

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/engram-summarize" ~/.claude/skills/majiayu000-claude-skill-registry-engram-summarize && rm -rf "$T"

manifest: skills/data/engram-summarize/SKILL.md

source content

Engram Summarize

Extract structured learnings from Claude Code and OpenClaw session history using LLM-based summarization.

When to Use

After completing significant work on a project
Before onboarding someone new to a codebase
Periodically to capture accumulated knowledge
When you want to distill decisions and patterns

What It Extracts

Category	Description	Example
decision	Architectural and design choices	"Chose SQLite over PostgreSQL for local CLI"
pattern	Code patterns and idioms	"Uses Result<T, E> for error handling"
gotcha	Things that went wrong and fixes	"index.json must sync with posts/"
convention	Project-specific norms	"Tests colocated with source files"
context	Background knowledge	"This is a Rust 2021 edition project"

Workflow

Step 1: Set up API key

export ANTHROPIC_API_KEY=your-key

Step 2: Summarize sessions

# Summarize last 30 days for current workspace
npx engram summarize --workspace . --days 30

# Save to file
npx engram summarize --workspace . -o learnings.json

# Higher confidence threshold
npx engram summarize --workspace . --min-confidence 0.7

Step 3: Review learnings

The output includes:

Session count processed
Learnings by category
Top learnings ranked by confidence

Example output:

📊 Summary:
  Sessions: 12
  Learnings: 23

  By category:
    Decisions:   5
    Patterns:    8
    Gotchas:     4
    Conventions: 3
    Context:     3

📝 Top learnings:
  [gotcha] Blog index.json must be updated when posts are renamed
  [pattern] All exports go through index.ts barrel files
  [decision] Used vitest over jest for faster test execution

Step 4: Integrate with skill generation

Use summarized learnings to enhance generated skills:

# Generate skill with richer context
npx engram generate-skill --workspace . --days 30

CLI Options

Option	Description	Default
`-w, --workspace <path>`	Workspace to filter sessions	`.`
`-d, --days <number>`	Days of history to analyze	`30`
`--no-openclaw`	Exclude OpenClaw sessions	include
`-a, --agent <id>`	OpenClaw agent ID filter	all
`-c, --min-confidence <n>`	Confidence threshold (0-1)	`0.5`
`-o, --output <path>`	Save learnings to JSON file	stdout
`--json`	Output as JSON	pretty print

Integration with Other Skills

Skill	When to Combine
engram-generate	Summarize before generating for richer skills
engram-recall	Recall + summarize for full context
describe-codebase	Add summarized learnings to codebase description

Comparison: Generate vs Summarize

Aspect	engram-generate	engram-summarize
Method	Pattern matching	LLM extraction
Output	Structural patterns	Semantic learnings
Cost	Free (local)	API tokens
Depth	File co-edits, commands	Decisions, rationale

Use both together for best results:

```
summarize
```
extracts the meaning
```
generate
```
captures the structure

Example Learnings

{
  "learnings": [
    {
      "category": "gotcha",
      "summary": "Blog index.json must be updated when posts are renamed",
      "detail": "The blog uses a static index.json. Renaming a post without updating causes 404 errors.",
      "files": ["index.json", "posts/*.md"],
      "confidence": 0.95
    },
    {
      "category": "decision", 
      "summary": "Chose TypeScript for type safety",
      "confidence": 0.85
    }
  ]
}

Important

Requires
```
ANTHROPIC_API_KEY
```
environment variable
Uses Claude Sonnet for extraction (cost-effective)
Review learnings before sharing (may contain sensitive info)
Higher confidence threshold reduces noise but may miss insights