Horiacristescu Nub Skills

Nub: Level-of-Detail for Large Content

Nub compresses content while preserving structure. Think telescope for code - adjust magnification to see landscape (overview) or details (zoomed).

When to Use Nub

Use when normal tools fail:

• Files >10K chars (Read truncates)
• Folders with many files (survey codebases)
• Large logs (JSONL, build output)
• Books, docs, data dumps

Don't use when:

• Read tool works fine (<10K)
• Need exact content
• Single small file

Phase 0: Recon First (30 seconds)

Before using nub, get metrics with Unix tools:

du -h --max-depth=1 | sort -h    # Size by directory
tree -L 2                         # Structure overview
find . -type f | wc -l            # File count
ls -lhS | head -10                # 10 largest files

This informs nub strategy: use index mode for 1000+ files, overview for <100.

Shape Selection

--shape WIDTH:HEIGHT = budget (W × H chars). Aspect ratio determines mode.

For massive projects: Start focused, not broad. Don't nub ~/huge_project, do nub ~/huge_project/src/module.

Essential Flags

--deduplicate (or -d) - Game changer for code

• Removes repetitive imports/boilerplate
• Shows what's DIFFERENT between files
• Essential for: test dirs, code exploration
• Skip for: diverse content

--grep PATTERN - Topology booster (not just filter)

• Preserves matches under compression
• Use for: finding class definitions, imports, patterns
• Example: --grep "class.*Strategy"

--range START:END - Progressive navigation

• Essential for files >1MB
• Navigate large files section by section
• Example: --range 1:50, then --range 50:100

Exploration Workflow

# 1. Recon (which dirs are large?)
du -h --max-depth=1 | sort -h

# 2. Start focused (not at root!)
nub ~/project/src --shape 120:40

# 3. Use dedup for code
nub ~/project/src --shape 120:40 -d

# 4. Find patterns
nub ~/project/src --grep "class|def" -d

# 5. Zoom into file
nub ~/project/src/module.py --shape 120:60

# 6. Navigate large file
nub large.jsonl --range 1:50 --shape 120:40

Strategic Patterns

Repository Cartography (new codebase):

1. Docs: nub README.md --shape 120:60
2. Config: nub pyproject.toml --shape 100:40
3. Modules: nub src/core --shape 120:40 -d

Code Archaeology (find patterns):

• nub src/ --grep "class.*Base" -d - inheritance
• nub tests/ --shape 100:40 -d - test suite

Data Exploration (large files):

• nub data.jsonl --range 1:30 - sample
• nub logs/ --grep "ERROR" --range 1:50 - errors

JSONL Exploration

Nub treats JSONL as text. Use jq + nub pipeline for structured exploration.

Quick pattern:

jq -r '"\(.time) | \(.title[0:50])"' data.jsonl > /tmp/idx.txt
nub /tmp/idx.txt --range 1:100 --shape 100:30

Full guide: See jsonl.md in this skill folder for:

• Recon patterns (schema, field distribution)
• Extract → Navigate workflow
• Filter → Compress patterns
• Compare subsets across time

Danger Zones

❌ Don't:

• Start with root of massive projects (use focused subdirs)
• Explore data directories blindly (GBs of files)
• Use tiny budgets on deep hierarchies (unreadable)
• Forget --deduplicate for code (wastes 50%+ budget)

✅ Do:

• Recon first (30 sec with Unix tools)
• Start focused, expand outward
• Use range for files >1MB
• Use dedup by default for code

Format Support

Native: folders, text, mind maps ([N] nodes), Python (AST-aware), Markdown (heading hierarchy) Via pipeline: JSONL (use jq → nub, see above) Coming: JSON, CSV, Conversation logs

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