Two-layer defense that eliminates AI "scope creep"
"I asked it to change one line, and it rewrote the entire file. All the code I didn't want touched was gone."
At the prompt layer, SCOPE ENFORCEMENT (using a "surgery robot" metaphor to constrain scope) instructs the LLM. At the implementation layer, a Diff Sanitizer mechanically strips any changes outside the allowed range. Two independent defenses operate simultaneously — scope creep becomes physically impossible.
Won't execute without proving correctness first
"Fixed A, broke B. Fixed B, broke C. Stuck in an infinite loop."
Before executing a fix, the LLM is forced to provide three correctness proofs: LOCALITY (is the scope limited?), ISOLATION (does it interfere with other files?), and NON-RECURRENCE (is this a systemic issue?). If any proof fails, the strategy is automatically escalated. Not "try and fail" — "prove correctness, then execute."
Detects invisible structural corruption and auto-repairs
"Code works. Tests pass. But weeks later, a critical bug surfaces. The cause was subtle structural decay."
Uses Python's ast module to compare structural snapshots before and after modifications. Detects 5 types of corruption patterns including class disappearance, attribute module-level leakage, and type hint loss. On SIC failure, attempts auto-repair via three-way comparison recovery using the "DNA" (original code) as reference.
Automatically eliminates framework-specific bugs
"Ran the generated code and got 'relationship xxx is not defined.' Took hours to understand why."
Automatically validates consistency across SQLAlchemy, Flask, Marshmallow, and HTML/CSS/JS with 32 validation rules (G-1 to G-32). Resolves detected bugs with 29 auto-fix rules. Foreign key references, relationship overlaps, reserved word conflicts — issues developers would need to debug manually vanish at generation time.
Maintains consistency even for large-scale projects
"Generated a large project all at once. Cross-file dependencies were completely broken."
Phase 1 (Foundation) generates models, schemas, and config first. Phase 2 (Application) builds routers and services. Phase 3 (Environment) finishes setup. Each phase verifies the previous phase's output before generating — cross-file consistency is guaranteed by design.
Auto-completes missing files after generation
"Right after generation: ModuleNotFoundError. A file simply wasn't generated."
Analyzes every import statement in generated files and verifies that referenced modules exist. Automatically detects missing files and generates completions using existing file context. The "import error on first run" problem disappears at generation time.
Never returns code that doesn't run
"Ran the generated code — instant crash. The generation showed 'success.'"
Actually executes imports on modified code to verify it works. If an import fails, changes to the failing file are automatically reverted. By going beyond static checks to runtime verification, "looks correct but doesn't run" code is never output.
Catches 25 types of problems that lint can't
"Syntax is correct. Lint passes. But the 'Save' button doesn't actually save. Found out a week later."
Beyond syntax, beyond lint — tracks 25 types of semantic issues exhaustively. Leftover TODOs, dead code, non-functional buttons, save/load inconsistencies, missing awaits, unhandled Promises, security holes, resource leaks, leftover debug code. Every generated file passes this logical completeness scan. "It compiles but doesn't work" is eliminated.