reDUP

Code duplication analyzer and refactoring planner for LLMs.

AI Cost Tracking

🤖 LLM usage: $12.6211 (95 commits)
👤 Human dev: ~$3787 (37.9h @ $100/h, 30min dedup)

Generated on 2026-07-29 using openrouter/deep/deep-v4-pro

reDUP scans codebases for duplicated functions, blocks, and structural patterns — then builds a prioritized refactoring map that LLMs can consume to eliminate redundancy systematically.

Features

Exact duplicate detection via SHA-256 block hashing
Structural clone detection — same AST shape, different variable names
LSH near-duplicate detection for large code blocks (>50 lines)
Multi-language support — 35+ languages via tree-sitter (Python, JavaScript, TypeScript, Go, Rust, Java, C/C++, C#, Ruby, PHP, Bash, SQL, HTML, CSS, Lua, Scala, Kotlin, Swift, Objective-C, JSON, YAML, TOML, XML, Markdown, GraphQL, Dockerfile, Makefile, Nginx, Vim, Svelte, Vue, and more)
Parallel scanning for large projects (2x+ performance improvement)
Incremental scan cache (--incremental) for faster repeat runs
Changed-only scan mode (--changed-only) for git-diff focused analysis
Fuzzy near-duplicate matching via SequenceMatcher / rapidfuzz
Semantic duplicate matching via optional code embeddings, including cross-language pairs
Explainable intent profiles from purpose names, calls, data terms, and control-flow effects
Provenance classification separates actionable debt from generated/deployment copies
Declared-intent matching via optional Intract contracts
Function-level analysis using Python AST and tree-sitter extraction
Impact scoring — prioritizes duplicates by saved_lines × similarity
Refactoring planner — generates concrete extract/inline suggestions
Multiple output formats: JSON, YAML, TOON, Markdown
Configuration system — TOML files and environment variables
CLI commands: scan, compare, diff, check, config, info
Cross-project comparison — detect shared code between projects with merge/extract recommendations
CI integration with configurable quality gates
Clean output — no syntax warnings from external libraries

New Features (v0.4.20)

🤖 MCP Server

Full MCP (Model Context Protocol) server for AI assistant integration:

# Start MCP server
redup-mcp

The bundled server uses MCP's stdio transport. Its analysis diagnostics are sent to stderr, so progress messages cannot corrupt JSON-RPC responses.

Available Tools:

find_duplicates — recommended compact first pass (top 10 non-generated groups)
analyze_project — configurable scan (top 20 non-generated compact groups by default)
check_project — Quality gate check
compare_projects — scan and compare two project directories
compare_scans — compare two saved JSON reports
suggest_refactoring — prioritized heuristic refactoring suggestions
project_info — Project metadata

For agent workflows, call find_duplicates once per project first. Summary totals always cover the complete scan even when groups is bounded. Use group_scope="all", max_groups=0 only when the complete raw list (including generated/deployment copies) is needed. Identical MCP scans are served from an in-process cache until source file metadata changes; pass refresh=true to bypass it. Set detail="full" only when normalized hashes and complete detector metadata are required.

🌐 Cross-language Semantic Similarity Detection

Embedding-based matching finds related functions even when their syntax and implementation differ:

# Install the optional model runtime, then scan selected languages
pip install 'redup[semantic,ast]'
redup scan . --semantic --semantic-threshold 0.75 --ext .py,.js,.ts,.php

Extension names may be passed with or without a leading dot, so agent-generated values such as --ext py,js,ts are equivalent.

--fuzzy remains a faster source-text similarity pass for near-identical implementations. Use --intent with Intract contracts when intent must be explicit and auditable rather than inferred. The default embedding model is sentence-transformers/all-MiniLM-L6-v2; reDUP embeds compact, language-neutral intent profiles instead of feeding raw polyglot source to a general-purpose sentence model. Override it with --semantic-model when a project has a calibrated specialized model. If sentence-transformers is unavailable, --semantic uses the explainable redup/intent-profile-v1 fallback. Both embedding and fallback semantic matches are candidates for manual review, never proof that implementations are interchangeable.

Normal reports classify each group as refactor, review, or generated. Generated source-to-build and deployment-mirror groups stay visible but are excluded from automatic refactoring suggestions.

Supported Patterns:

Functions, classes, API endpoints
Database queries, web components
Auth/validation, error handling, logging
Configuration, infrastructure code

🌳 Modular Tree-Sitter Extractor

Refactored tree-sitter extraction with clean, modular architecture:

ts_extractor/
├── extractors/          # Modular per-language extractors
│   ├── c_family.py      # C, C++, C#, Objective-C
│   ├── go.py            # Go
│   ├── java.py          # Java, Scala, Kotlin
│   ├── markup.py        # HTML, XML, Svelte, Vue
│   ├── web.py           # JavaScript, TypeScript
│   └── ...
├── dispatcher.py        # Smart language routing
├── config.py            # Language registry
└── main.py              # Unified API

Benefits:

Easier to add new languages
Better testability
Cleaner separation of concerns
35+ languages supported

New Features (v0.5.0+)

🌐 Semantic Similarity Detection

Cross-language matching for functions whose implementation syntax differs:

# Detect similar behavior across different languages
redup scan . --semantic --semantic-threshold 0.75 --ext .py,.js,.ts

# Cross-project semantic comparison
redup compare ./project-a ./project-b --semantic --threshold 0.75

Features:

Adds SEMANTIC groups to normal scan reports
Uses a configurable Sentence Transformers model over extracted intent signals
Keeps source-text fuzzy matching separate for predictable thresholds
Leaves auditable intent equivalence to explicit Intract contracts

🧩 Modular ts_extractor Architecture

The tree-sitter multi-language extractor has been refactored from a 782-line god module into a clean package:

redup/core/ts_extractor/
├── extractors/
│   ├── web.py        # JavaScript/TypeScript
│   ├── c_family.py   # C/C++
│   ├── dotnet.py     # C#
│   ├── ruby.py       # Ruby
│   ├── php.py        # PHP
│   └── ...           # 10+ language-specific modules

Benefits:

Better maintainability (avg 100 lines per module vs 782)
Easier to add new language extractors
Shared base utilities for common operations
Full backward compatibility maintained

🎯 Enriched TOON Reporter

The TOON format now includes actionable sections for practical refactoring:

HOTSPOTS — Top 7 files with most duplicated lines (where to focus effort)
QUICK_WINS — Low-risk, high-savings suggestions (do first)
DEPENDENCY_RISK — Duplicates spanning multiple packages (cross-module risk)
EFFORT_ESTIMATE — Time estimates per task with difficulty (easy/medium/hard)

🤖 LLM-Powered Refactoring Plans

Generate AI-assisted refactoring TODO lists from cross-project comparisons:

redup compare ./project-a ./project-b --refactor-plan --env .env --output report.json

Uses litellm for flexible LLM provider support
Compact metadata-only prompts for efficiency
Structured JSON output with prioritized tasks
Token usage tracking

📊 Simplified Compare Reports

Cross-project comparison reports are now more compact and human-readable:

Relative file paths instead of absolute
Matches deduplicated by function pair
Communities with compact member dicts
Filtered trivial entries to reduce noise
~60% smaller JSON size

Installation

pip install redup

With optional dependencies:

pip install redup[all]       # Everything
pip install redup[fuzzy]     # rapidfuzz for better similarity matching
pip install redup[ast]       # tree-sitter for multi-language AST
pip install redup[lsh]       # datasketch for LSH near-duplicate detection
pip install redup[semantic]  # sentence-transformers for semantic scan matches
pip install redup[intent]    # Intract for declared-intent duplicate detection
pip install redup[compare]   # networkx for cross-project community detection
pip install redup[llm]       # litellm for LLM-powered refactoring plans

Development / tests

pip install -e ".[dev]"
pytest -q

Quick Start

CLI

# Scan current directory, output TOON to stdout
redup scan .

# Scan with JSON output saved to file
redup scan ./src --format json --output ./reports/

# Parallel scanning for large projects
redup scan . --parallel --max-workers 4

# Reuse cache between runs for faster rescans
redup scan . --incremental

# Scan only files changed vs branch tip (git diff based)
redup scan . --changed-only --base-ref origin/main --incremental

# Multi-language scanning with 35+ supported languages
redup scan . --ext ".py,.js,.ts,.go,.rs,.java,.rb,.php,.html,.css,.sql,.lua,.scala,.kt,.swift,.m,.json,.yaml,.toml,.xml,.md,.graphql,.dockerfile,.svelte,.vue"

# Cross-language / different-implementation matching (optional model dependency)
redup scan . --semantic --semantic-threshold 0.75 --ext ".py,.js,.ts,.php,.go,.rs,.java"

# Auditable same-intent matching from Intract contracts
redup scan . --intent --intent-manifest intent.yaml

# CI gate with thresholds
redup check . --max-groups 10 --max-lines 100

# Compare two scans
redup diff before.json after.json

# Cross-project comparison (merge vs extract decision)
redup compare ./project-a ./project-b --threshold 0.75

# With LLM-powered refactoring plan (requires litellm + .env with API keys)
redup compare ./project-a ./project-b --refactor-plan --env .env --output comparison.json

# Specify custom LLM model
redup compare ./project-a ./project-b --refactor-plan --llm-model openrouter/anthropic/claude-3.5-sonnet

# Initialize configuration
redup config --init

# Scan with all formats
redup scan . --format all --output ./redup_output/

# Only function-level duplicates (faster)
redup scan . --functions-only

# Custom thresholds
redup scan . --min-lines 5 --min-sim 0.9

# Show installed optional dependencies
redup info

# Export duplications as tasks to TODO.md (requires: pip install redup[tasks])
redup tasks ./my-project

# Export with GitHub sync
redup tasks ./my-project --backend github --milestone "Sprint 1"

# Export with GitLab sync and custom output
redup tasks ./my-project -b gitlab -o refactoring-tasks.md

# Preview tasks without creating files
redup tasks ./my-project --dry-run

Task Management with Planfile (Optional)

When you install redup[tasks], you can export duplication findings as actionable tasks in TODO.md format with synchronization to GitHub, GitLab, or Jira:

# Install with planfile support
pip install redup[tasks]

# Generate TODO.md from duplications
redup tasks ./my-project --output TODO.md

# The generated TODO.md includes:
# - Priority-based task organization (critical/major/minor)
# - Difficulty estimation (easy/medium/hard)
# - Line savings potential
# - Detailed refactoring suggestions
# - Planfile export configuration

Example TODO.md output:

# TODO - Duplication Refactoring Tasks

## CRITICAL (3 tasks)
- [ ] **Refactor: process_file (4x duplication)** 🔴
   Priority: critical | Savings: 124L
   <details>
   Extract function to shared utility module.
   Files: src/core/scanner.py, src/core/planner.py, ...
   </details>

## MAJOR (5 tasks)
- [ ] **Refactor: validate_input (3x duplication)** 🟡
   Priority: major | Savings: 45L
   ...

Configuration

Create a redup.toml file:

[scan]
extensions = ".py,.js,.ts,.go,.rs,.java,.rb,.php,.html,.css,.sql,.lua,.scala,.kt,.swift,.m,.json,.yaml,.toml,.xml,.md,.graphql,.dockerfile,.svelte,.vue"
min_lines = 3
min_similarity = 0.85
include_tests = false

[lsh]
enabled = true
min_lines = 50
threshold = 0.8

[check]
max_groups = 10
max_lines = 100

[output]
format = "toon"
output = "redup_output"

[reporting]
include_snippets = true
generate_suggestions = true

Or use [tool.redup] in pyproject.toml. Environment variables with REDUP_ prefix override file settings.

Python API

from pathlib import Path
from redup import ScanConfig, analyze
from redup.reporters.toon_reporter import to_toon
from redup.reporters.json_reporter import to_json

config = ScanConfig(
    root=Path("./my_project"),
    extensions=[".py", ".js", ".ts", ".go", ".rs", ".java", ".rb", ".php", ".html", ".css"],
    min_block_lines=3,
    min_similarity=0.85,
)

result = analyze(config=config, function_level_only=True)

print(f"Found {result.total_groups} duplicate groups")
print(f"Lines recoverable: {result.total_saved_lines}")

# For LLM consumption
print(to_toon(result))

# For tooling / CI
Path("duplication.json").write_text(to_json(result))

Output Formats

TOON (LLM-optimized)

# redup/duplication | 15 groups | 86f 10453L | 2026-04-16

SUMMARY:
  files_scanned: 86
  total_lines:   10453
  dup_groups:    15
  dup_fragments: 36
  saved_lines:   217
  scan_ms:       3620

HOTSPOTS[7] (files with most duplication):
  src/redup/core/ts_extractor.py  dup=74L  groups=4  frags=11  (0.7%)
  src/redup/core/scanner_utils.py  dup=70L  groups=3  frags=3  (0.7%)
  src/redup/core/scanner_loader.py  dup=52L  groups=1  frags=1  (0.5%)

DUPLICATES[15] (ranked by impact):
  [E0001] ! EXAC  _preload_files  L=52 N=2 saved=52 sim=1.00
      src/redup/core/scanner_loader.py:9-60  (_preload_files)
      src/redup/core/scanner_utils.py:53-104  (_preload_files)

REFACTOR[15] (ranked by priority):
  [1] ◐ extract_module     → src/redup/core/utils/_preload_files.py
      WHY: 2 occurrences of 52-line block across 2 files — saves 52 lines
      FILES: src/redup/core/scanner_loader.py, src/redup/core/scanner_utils.py

QUICK_WINS[8] (low risk, high savings — do first):
  [3] extract_function   saved=26L  → src/redup/core/utils/find_exact_duplicates_lazy.py
      FILES: lazy_grouper.py
  [4] extract_function   saved=21L  → src/redup/core/utils/_extract_functions_go.py
      FILES: ts_extractor.py

DEPENDENCY_RISK[3] (duplicates spanning multiple packages):
  validate_input  packages=2  files=2
      api/routes/users.py
      services/auth/validate.py

EFFORT_ESTIMATE (total ≈ 8.7h):
  hard   _preload_files                      saved=52L  ~156min
  hard   __init__                            saved=36L  ~108min
  medium find_exact_duplicates_lazy          saved=26L  ~52min
  easy   _is_test_file                       saved=12L  ~24min

METRICS-TARGET:
  dup_groups:  15 → 0
  saved_lines: 217 lines recoverable

JSON (machine-readable)

{
  "summary": {
    "total_groups": 3,
    "total_saved_lines": 84
  },
  "groups": [
    {
      "id": "E0001",
      "type": "exact",
      "normalized_name": "calculate_tax",
      "fragments": [
        {"file": "billing.py", "line_start": 1, "line_end": 8},
        {"file": "shipping.py", "line_start": 1, "line_end": 8}
      ],
      "saved_lines_potential": 16
    }
  ],
  "refactor_suggestions": [
    {
      "priority": 1,
      "action": "extract_function",
      "new_module": "utils/calculate_tax.py",
      "risk_level": "low"
    }
  ]
}

Cross-Project Comparison

The redup compare command analyzes two separate projects to detect shared code and recommends a refactoring strategy:

Merge projects — if >60% code overlap
Extract shared library — if 5-60% overlap with well-defined clusters
Keep separate — if <5% overlap

CLI Usage

# Basic comparison
redup compare ./project-a ./project-b --threshold 0.75

# With semantic similarity (slower, more accurate)
redup compare ./project-a ./project-b --semantic --threshold 0.70

# Multi-language projects
redup compare ./backend ./frontend --ext ".py,.js,.ts" --threshold 0.80

# Skip community detection (faster, no networkx required)
redup compare ./a ./b --no-community

# Generate LLM-powered refactoring plan (requires redup[llm])
redup compare ./a ./b --refactor-plan --env .env --output plan.json

Sample Output

Comparing project-a ↔ project-b (threshold=0.75)
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃      Cross-Project Comparison                        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Metric                  │ Value                      │
├─────────────────────────┼────────────────────────────┤
│ Project A files         │ 42                         │
│ Project B files         │ 38                         │
│ Project A lines         │ 8500                       │
│ Project B lines         │ 7200                       │
│ Cross matches           │ 15                         │
│ Shared LOC (potential)  │ 1200                       │
└─────────────────────────┴────────────────────────────┘

Recommendation: extract_shared_lib
15% overlap (1200 shared lines, 5 clusters). Extract to shared library.
Confidence: 80%

Top Communities (shared code candidates):
┏━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━┳━━━━━━━━━━┓
┃ ID ┃ Name                 ┃ Similarity ┃ LOC ┃ Members  ┃
┡━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━╇━━━━━━━━━━┩
│  0 │ validate_input       │ 0.89       │ 180 │ 5        │
│  1 │ parse_config         │ 0.82       │ 140 │ 4        │
│  2 │ format_response      │ 0.76       │ 100 │ 3        │
└────┴──────────────────────┴────────────┴─────┴──────────┘

Report JSON Structure

{
  "project_a": "./project-a",
  "project_b": "./project-b",
  "stats": {
    "a": {"files": 42, "lines": 8500},
    "b": {"files": 38, "lines": 7200}
  },
  "total_matches": 15,
  "shared_loc_potential": 1200,
  "recommendation": {
    "decision": "extract_shared_lib",
    "rationale": "15% overlap (1200 shared lines, 5 clusters). Extract to shared library.",
    "overlap_pct": 0.1523,
    "shared_loc": 1200,
    "confidence": 0.8
  },
  "communities": [
    {
      "name": "validate_input",
      "similarity": 0.89,
      "loc": 180,
      "members": [
        {"project": "A", "file": "api/validators.py", "function": "validate_input"},
        {"project": "B", "file": "utils/validation.py", "function": "validate_input"}
      ]
    }
  ],
  "matches": [...]
}

Algorithm Overview

The comparison uses a 3-tier similarity detection:

Structural hash — exact AST matches (fast, O(n+m))
LSH (Locality Sensitive Hashing) — near-duplicates via MinHash
Semantic similarity — language-neutral intent-profile embeddings (optional, slowest)

Matches are deduplicated by (function_a, function_b, file_a, file_b) with the highest similarity score retained.

Community Detection

Requires networkx (pip install redup[compare]).

Uses greedy modularity communities on a similarity graph where:

Nodes = functions from both projects
Edges = similarity score (filtered by --threshold)
Communities = clusters of mutually similar functions

Each community gets a generated name based on longest common prefix of its member functions (e.g., validate_* → validate_input).

Architecture

src/redup/
├── __init__.py            # Public API
├── __main__.py            # python -m redup
├── mcp_server.py          # MCP server entry point (re-exports from mcp package)
├── mcp/                   # MCP server package
│   ├── __init__.py        # Public MCP API
│   ├── handlers.py        # Tool handlers
│   ├── schemas.py         # JSON-RPC schemas
│   ├── server.py          # JSON-RPC server core
│   └── utils.py           # Shared utilities
├── core/
│   ├── models.py          # Pydantic data models
│   ├── scanner.py         # File discovery + block extraction
│   ├── scanner/           # Scanner package
│   │   ├── __init__.py    # Public scanner API
│   │   ├── cache.py       # Memory cache
│   │   ├── filters.py     # File filtering
│   │   ├── loader.py      # File preloading
│   │   └── types.py       # Scanner types
│   ├── hasher.py          # SHA-256 / structural fingerprinting
│   ├── matcher.py         # Fuzzy similarity comparison
│   ├── planner.py         # Refactoring suggestion generator
│   ├── pipeline.py        # Legacy: re-exports from pipeline package
│   └── pipeline/          # Pipeline package (new)
│       ├── __init__.py    # analyze(), analyze_optimized(), analyze_parallel()
│       ├── phases.py      # scan_phase(), process_blocks()
│       ├── duplicate_finder.py  # Duplicate finding phases
│       └── groups.py      # Group creation, deduplication
│   └── ts_extractor/        # Tree-sitter extraction (35+ languages)
│       ├── __init__.py    # Public API
│       ├── main.py        # Core extraction API
│       ├── dispatcher.py  # Language routing
│       ├── config.py      # Language registry
│       └── extractors/    # Per-language extractors
├── reporters/
│   ├── json_reporter.py   # JSON output
│   ├── yaml_reporter.py   # YAML output
│   └── toon_reporter.py   # TOON output (LLM-optimized)
└── cli_app/
    └── main.py            # Typer CLI

Analysis Pipeline

1. SCAN      Walk project, read files, extract function-level + sliding-window blocks
2. HASH      Generate exact (SHA-256) and structural (normalized AST) fingerprints
3. GROUP     Bucket by hash, keep only groups with 2+ blocks from different locations
4. MATCH     Verify candidates with fuzzy similarity (SequenceMatcher / rapidfuzz)
5. DEDUP     Remove overlapping groups (keep highest-impact)
6. PLAN      Generate prioritized refactoring suggestions with risk assessment
7. REPORT    Export to JSON / YAML / TOON

Recent Improvements (v0.5.0)

🏗️ Modular Architecture Refactoring

Major internal restructuring for better maintainability and extensibility:

MCP Server Package

The MCP server has been split from a 675-line monolith into a clean package:

redup/mcp/
├── __init__.py      # Public API
├── handlers.py      # 8 tool handlers
├── schemas.py       # JSON-RPC schemas
├── server.py        # Server core
└── utils.py         # Utilities

82% code reduction in main file
Backward compatible: mcp_server.py re-exports all APIs
Better testability: Isolated handlers can be tested independently

Pipeline Package

The analysis pipeline (714 lines) now lives in a modular package:

redup/core/pipeline/
├── __init__.py          # analyze(), analyze_optimized(), analyze_parallel()
├── phases.py            # scan_phase(), process_blocks()
├── duplicate_finder.py  # find_exact_groups(), find_structural_groups(), etc.
└── groups.py            # deduplicate_groups(), blocks_to_group(), etc.

66% reduction in main orchestrator file
Phases can be used independently for custom workflows
Cleaner separation of concerns

Scanner Improvements

The scanner has been refactored with extracted helpers:

_init_strategy() - Strategy initialization
_process_single_file() - Per-file processing
_extract_blocks_for_file() - Block extraction
Reduced CC and fan-out in main scan_project() function

🎯 Sprint 1 Refactoring Complete

Reduced cyclomatic complexity from CC̄=4.2 to CC̄=3.5
Eliminated all critical functions (CC > 10): 2 → 0
Achieved HEALTHY status with no structural issues
Dispatch pattern implementation for AST node processing
Modular TOON reporter split into 5 focused functions
CLI refactoring with helper functions for better maintainability

🚀 Technical Achievements

_process_ast_node: CC=14 → CC=6 (dispatch dict pattern)
to_toon: CC=12 → CC=8 (5 helper functions)
CLI scan(): fan-out=18 → ≤10 (4 helper functions)
Code quality: 0 high-complexity functions
Test coverage: 64/64 tests passing (100%)

📊 Quality Metrics

Health status: ✅ HEALTHY (no critical issues)
Cyclomatic complexity: CC̄=3.5 (target ≤ 3.0 achieved)
Maximum CC: 9 (target ≤ 10 achieved)
Code maintainability: Significantly improved
Duplication: Minimal (2 groups, 6 lines - acceptable patterns)

🔧 Code Architecture

Dispatch tables for extensible AST processing
Single responsibility functions throughout codebase
Clean separation of concerns in CLI pipeline
Type safety improvements with proper annotations
Error handling enhanced for edge cases

Integration with wronai Toolchain

reDUP is part of the wronai developer toolchain:

code2llm — static analysis engine (health diagnostics, complexity)
reDUP — deep duplication analysis and refactoring planning
code2docs — automatic documentation generation
vallm — validation of LLM-generated code proposals

📈 Typical workflow:

code2llm analyzes the project → .toon diagnostics
redup finds duplicates → duplication.toon.yaml
Feed both to an LLM for targeted refactoring
vallm validates the LLM's proposals before merging

🎯 Why reDUP?

LLM-ready: TOON format optimized for LLM consumption
Actionable: Generates concrete refactoring suggestions
Prioritized: Ranks duplicates by impact and risk
Integrated: Works seamlessly with wronai toolchain
Fast: Scans 1000+ lines in < 1 second
Clean: No syntax warnings, professional output

Development

git clone https://github.com/semcod/redup.git
cd redup
pip install -e ".[dev]"
pytest

License

Licensed under Apache-2.0.

Author

Tom Sapletta

Status

Last updated by taskill at 2026-04-25 13:46 UTC

Metric	Value
HEAD	`7055183`
Coverage	42.9%
Failing tests	—
Commits in last cycle	50

Added markdown output and a configuration management system, with numerous docs and code-analysis refactors and some test additions. Several refactors target the code analysis engine and TypeScript extractor components.

GeezerVison

Latest commit

History

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