Or: How We're Building the Semantic OS One Layer at a Time

You know that moment when Claude reads your 7,500-line Python file to answer a simple question like "what does the load_config function do?"

You watch the tokens burn. You know there's a better way. But cat is fast, grep is familiar, and you've got a deadline.

What if I told you there's a tool that gives Claude the same answer using 50 tokens instead of 7,500?

Not by cutting corners. Not by guessing. By actually understanding your code's structure first, then reading only what matters.

That's Reveal.

And it's not just a clever tool. It's a proof point for an entire semantic infrastructure—a demonstration that progressive disclosure, knowledge graphs, and semantic understanding can work together to make AI agents 25-30x more efficient.

This is one piece of a bigger vision. Let me show you how it works.

The Problem Nobody Talks About

AI agents are amazing at reasoning. They're terrible at not reading everything.

When you ask Claude to "fix the auth bug," here's what actually happens:

  1. Claude reads auth.py (3,200 tokens)
  2. Claude reads config.py because auth imports it (1,800 tokens)
  3. Claude reads utils.py because why not (2,100 tokens)
  4. Claude finds the bug on line 47 of auth.py
  5. You just spent 7,100 tokens to fix a one-line typo

This is insane.

Not because Claude is bad. Because our tools are designed for humans with eyes, not agents with context windows.

Enter: Semantic Slicing

Here's what Reveal does differently:

# Instead of this (7,500 tokens):
cat auth.py

# Do this (100 tokens):
reveal auth.py

Output:

Functions (8):
  auth.py:23    validate_token [12 lines, depth:1]
  auth.py:47    load_config [23 lines, depth:1]  ← THE BUG IS HERE
  auth.py:89    refresh_session [45 lines, depth:2]
  ...

Next: reveal auth.py <function>   # Extract specific function
      reveal auth.py --check       # Quality check

Now Claude knows:
- The file has 8 functions
- Where each one is
- How complex they are
- What to do next

Then extract just what you need (50 tokens):

reveal auth.py load_config

Total: 150 tokens instead of 7,500.

That's 50x reduction. On a single file. Before you even start debugging.

But Wait, There's Way More

Reveal isn't just "structure viewer." It's a semantic exploration engine.

1. Query Code Like a Database

Find all complex functions in your codebase:

reveal 'ast://./src?complexity>10'

Find all test functions:

reveal 'ast://.?name=test_*'

No grep. No find. No regex. Just questions your code can answer.

2. Diagnose Python Environments

You know that bug where your code changes don't work? Stale .pyc bytecode.

reveal python://debug/bytecode

Output:

⚠️  Found 3 stale bytecode files:
  src/__pycache__/auth.cpython-310.pyc (older than auth.py)

Cleanup: find . -name "*.pyc" -delete

This has saved me hours. Multiple times.

3. Validate Infrastructure

Remember that time your nginx config had two upstreams pointing to the same backend on the wrong port, and your $8K/month revenue site served 404s for 6 hours?

Yeah, me too.

reveal nginx.conf --check

Output:

N001: Duplicate backend detected
  upstream api_v1 (port 8001) and upstream api_v2 (port 8001)
  both point to 127.0.0.1:8001

  Risk: Load balancer serves wrong API version

Reveal caught this in staging. Before production. Before the incident.

4. Navigate JSON Like It's a URL

reveal json://config.json/database/credentials/password

No jq syntax to remember. Just paths. Like URLs.

Want the schema?

reveal json://config.json?schema

Want it grep-able?

reveal json://config.json?flatten

The Design Philosophy Nobody Asked For (But Everyone Needs)

Reveal is built on three principles that make AI agents 10x more effective:

1. Progressive Disclosure

Start general. Get specific. Never read more than you need.

reveal src/                    # What's here? (directory tree)
reveal src/auth.py             # What's in this file? (structure)
reveal src/auth.py validate    # Show me this function (extraction)

Each step gives you exactly what you need to decide the next step.

2. Self-Documenting

Every capability documents itself:

reveal help://                 # What can I do? (~50 tokens)
reveal help://python           # How does python:// work? (~200 tokens)
reveal help://tricks           # Show me the cool stuff (~500 tokens)

No 12,000-token manual unless you explicitly ask for it (--agent-help-full).

3. Agent-First UX

When Claude uses Reveal, breadcrumbs suggest reveal commands, not vim:

Next: reveal file.py <function>   # Extract specific function
      reveal file.py --check       # Quality check
      reveal file.py --outline     # Hierarchical view

Every output teaches Claude what to do next. Zero tokens wasted on "open in editor" suggestions.

Real-World Impact

Here's what Reveal enables in practice:

Scout Code Review Agent

TIA Session Documentation

Python Environment Debugging

Infrastructure Validation

The Meta Moment

The best Reveal demo is Reveal itself.

When I was building the python:// adapter, I triggered a module shadowing bug. My local types.py was hiding Python's built-in types module.

So I used Reveal to diagnose Reveal:

reveal python://module/types

Output:

⚠️  Module 'types' is shadowed!
  Import resolves to: ./reveal/types.py (your local file)
  Built-in module 'types' is hidden

  Fix: Rename reveal/types.py to avoid shadowing

A tool that debugs itself using its own diagnostic capabilities.

That's when I knew we had something special.

The Bigger Picture: Semantic Infrastructure in Action

Reveal isn't working alone. It's one component in a semantic stack we're building at the Semantic Infrastructure Lab (SIL).

The Stack (7 Layers of Semantic Computing)

We're building an operating system for semantic computing. Think of it like the OSI network stack, but for meaning instead of packets:

Layer 0: Provenance - Everything has lineage (cryptographic provenance)
Layer 1: Primitives - Names, types, relationships (AST, type systems)
Layer 2: Structures - Data structures, geometry, how things are built
Layer 3: Composition - How components fit together (cross-domain)
Layer 4: Dynamics - Time, simulation, execution flow
Layer 5: Intent - User goals, constraints, what you want
Layer 6: Intelligence - Agents, reasoning, decision-making
Layer -1: Substrate - Physical compute and storage (optional)

Reveal operates across Layers 1-3:
- Layer 1: Primitives (functions, classes, imports)
- Layer 2: Structures (types and relationships, function calls, inheritance)
- Layer 3: Composition (file structure, module organization)

It extracts semantic meaning from code without executing it. That's the key insight.

The Beth Connection: Knowledge Graphs Meet Progressive Disclosure

Here's where it gets interesting. Reveal doesn't work alone—it's paired with Beth, our semantic search and knowledge graph system.

What Beth does:
- Indexes 15,306 files across our entire infrastructure
- Builds knowledge graphs with 1,402 emergent topics (not imposed taxonomies)
- Uses PageRank-style authority to rank documents by their connections
- Makes summaries and indexes discoverable as high-authority nodes

The measured synergy:
- Reveal alone: 50x token reduction on individual files
- Beth alone: Fast semantic search across thousands of files
- Reveal + Beth together: 25-30x reduction across entire workflows

Why it works:
1. Beth finds the right document (semantic search, <400ms)
2. Reveal shows structure (Orient: "what's in here?")
3. You drill down (Focus: "show me that function")
4. Session notes get indexed (Beth learns patterns, ranks summaries high)
5. Future searches work better (virtuous cycle)

The Pattern: Progressive Disclosure is Fractal

This isn't just a tool design. It's an architectural principle that works at every scale:

Tools implement it:
- Reveal: structure → element
- Beth: topic → document → section
- TIA search: recent → content → AST

Workflows follow it:
- Orient (summaries, structure) → 300-500 tokens
- Navigate (outlines, relationships) → 500-1,500 tokens
- Focus (specific code) → 1,000-8,000 tokens

Documentation uses it:
- README → Index → Detailed Guide
- Beth ranks summaries high (PageRank boost from connections)
- You land on overviews first, drill down as needed

Measured across 300+ TIA sessions:
- Code exploration: 58x reduction (35,000 → 600 tokens)
- Pattern discovery: 22x reduction (50,000 → 2,200 tokens)
- Doc navigation: 20x reduction (25,000 → 1,250 tokens)
- Average: 27x fewer tokens

What We're Proving

Reveal is evidence that semantic infrastructure works:

✅ Progressive disclosure scales - Same pattern from 10 files to 15,000+ files
✅ Knowledge graphs create value - PageRank for documents works (summaries rank high naturally)
✅ Tool composition compounds benefits - Reveal + Beth = 25x improvement
✅ Emergent organization beats imposed hierarchy - 1,402 topics discovered organically
✅ Structure-first beats text-first - AST queries > regex every time

This is not about Reveal being clever. It's about demonstrating that when you build semantic infrastructure properly—with progressive disclosure, knowledge graphs, and composable tools—you get 25x efficiency improvements that actually measure in production.

The Vision: Semantic OS

Reveal is Layer 1-3 of a 7-layer semantic operating system. The full stack includes:

The thesis: When you treat meaning as a first-class concern—not text, not syntax, but semantic structure—you can build tools that compose naturally and amplify each other's value.

Reveal is the proof. Beth is the proof. The 25x measured reduction is the proof.

And we're just getting started.

Who Should Use Reveal?

You should use Reveal if:

✅ You work with AI agents (Claude, GPT, local LLMs)
✅ You review code (human or AI)
✅ You debug Python environments
✅ You maintain infrastructure configs (nginx, docker)
✅ You care about token efficiency
✅ You explore unfamiliar codebases

You probably don't need Reveal if:

❌ You only work on tiny projects (<5 files)
❌ You never use AI coding assistants
❌ You enjoy reading 10,000-line files from top to bottom

Quick Start

# Install
pip install reveal-cli

# Try it
reveal .                       # Directory structure
reveal file.py                 # File structure
reveal file.py function_name   # Extract function
reveal file.py --check         # Quality check

# Get agent-optimized help
reveal help://                 # What's available? (~50 tokens)
reveal --agent-help            # Quick start (~1,500 tokens)

The Stuff That Makes It Awesome

Multi-Language Support

Python, JavaScript, TypeScript, Rust, Go, Bash, YAML, JSON, Markdown, Dockerfile, Nginx, TOML, JSONL, GDScript, Jupyter notebooks... plus 37 more via TreeSitter fallback.

Pipeline Integration

# PR review workflow
git diff --name-only | reveal --stdin --outline

# Find issues across codebase
find src/ -name "*.py" | reveal --stdin --check

# Copy output for sharing
reveal file.py --copy

Format Flexibility

reveal file.py                 # Human-readable text
reveal file.py --format=json   # JSON structure
reveal file.py --format=grep   # Pipeable (file:line:name)
reveal file.py --copy          # Copy to clipboard

Quality Checks

reveal file.py --check              # All checks
reveal file.py --check --select B,S # Bugs + security only

The Token Efficiency Breakdown

Let's measure this concretely. Typical Python file (auth.py, 342 lines):

Approach Tokens What You Get
cat auth.py 7,500 Entire file
reveal auth.py 100 Structure (8 functions, imports)
reveal auth.py validate_token 50 Just the function you need
Total (reveal) 150 Same understanding
Reduction 50x 7,350 tokens saved

On a 50-file codebase review:
- Traditional: 50 files × 7,500 tokens = 375,000 tokens (~$0.75 on Claude Opus)
- Reveal: 50 files × 150 tokens = 7,500 tokens (~$0.015 on Claude Opus)
- Savings: $0.74 per review (50x reduction)

At scale (100 reviews/month):
- Cost savings: ~$74/month
- Context window savings: Fits 50x more context in same request
- Speed improvement: Less to read = faster responses

What's Next

Reveal is actively developed and used in production by:
- TIA (The Intelligent Agent) - Scott's semantic infrastructure system
- Scout - AI-powered code review agent (10x token efficiency measured)
- SIL (Semantic Infrastructure Lab) - Research project for AI-native tooling

Roadmap highlights:
- reveal diff:// - Compare files, environments, API responses
- reveal stats:// - Codebase health metrics
- --watch mode - Live updates on file changes
- VS Code extension - Bring reveal to your IDE
- GitHub Action - Automated PR quality checks

Try It Right Now

Seriously. Install it and run it on your current project:

pip install reveal-cli
cd ~/your-project
reveal .

Pick any file that looks interesting, run reveal file.py, and watch it show you the structure in ~100 tokens instead of reading 7,500.

Then extract one function: reveal file.py function_name

You just saved 7,000 tokens. On one file.

Now imagine doing that across your entire codebase.

The Bottom Line

Reveal is a semantic code exploration engine built for AI agents.

It doesn't replace cat or grep. It replaces reading entire files to understand structure.

It's not magic. It's just treating code as structured data instead of text.

And it's open source, production-ready, and actively maintained.

Try it. Your token budget will thank you.

About the Semantic Infrastructure Lab (SIL)

SIL is a research lab building the foundations for semantic computing—an operating system where meaning is the primary abstraction, not files or processes.

Our work:
- 12 projects across the 7-layer semantic stack
- 15,306+ files of documentation, code, and research
- 1,402 emergent topics discovered through semantic organization
- 25-30x measured efficiency gains in AI agent workflows

Public projects:
- Reveal - Code structure explorer (this tool)
- Scout - AI code review agent (uses Reveal, 10x more efficient)
- Beth - Semantic search with knowledge graphs
- Pantheon - Unified semantic IR for cross-domain interoperability

The vision: Build tools that understand meaning, compose naturally, and prove that semantic infrastructure scales.

Current status: Production systems serving real workflows, not research demos. Reveal, Beth, and TIA process 300+ sessions per month with measured 25x token reductions.

Links:

Reveal:
- GitHub: https://github.com/scottsen/reveal
- PyPI: https://pypi.org/project/reveal-cli/
- Docs: reveal --help or reveal help://
- Quick Start: reveal --agent-help

SIL Ecosystem:
- Website: [Coming Soon - sil.dev]
- GitHub Org: https://github.com/Semantic-Infrastructure-Lab
- Scout (Code Review): https://github.com/scottsen/scout
- Research: 12 projects documented internally, selective public releases

Current Version: Reveal v0.23.0
License: MIT
Maintained by: Scott (Founder, Semantic Infrastructure Lab)

Colophon

This intro was written by TIA (The Intelligent Agent) using reveal to explore reveal's own codebase, and Beth to discover related documentation across 15,306 indexed files. Meta? Absolutely. Effective? You tell me.

Token count for this analysis:
- This document: ~4,200 tokens
- Research (Beth + Reveal exploration): ~8,000 tokens
- Total: ~12,200 tokens

If I'd used cat on reveal's source files + grep through 15K files:
- Reveal source: ~45,000 tokens
- Manual doc search: ~80,000 tokens
- Total: ~125,000 tokens

Efficiency gain: 10x reduction (12,200 vs 125,000 tokens)

Case closed. Progressive disclosure works.

Want to learn more about the semantic stack? The full architecture, research, and additional tools are being documented at SIL. We're building in public where it makes sense, keeping proprietary details private where necessary.

Reveal is the public face of a much larger vision. If this resonates, stay tuned.