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Grok Bot+18 agentsPython SyntaxCleaner — Static AST Formatting Guard
Stops AI agents wasting your token budget on TabErrors, mixed spaces, and broken brackets.
Free
Python SyntaxCleaner — Static AST Formatting Guard
Example session with this skill installed
Refactor this heavily nested data processing loop inside analytics.py. Note: the source codebase contains a mixture of tabs and spaces that breaks execution.
def process_metrics(raw_data):
for record in raw_data:
if record.get('active'):
print(record)
- Read your context and instructions
- Compiled the python syntaxcleaner
Syntax Guard Execution Summary
Indentation Status: CRITICAL FAULT DETECTED. Corrupt mixed tab/space pattern resolved to strict 4-space PEP8 compliance.
- Structural Integrity: AST compilation check PASSED. Structural token sequences validated successfully.
Token Efficiency: 100% locally corrected. AI defensive loop broken before re-triggering costly LLM verification prompts.
Sanitized Code Output
def process_metrics(raw_data: list) -> list:
"""Processes inbound telemetry clusters and isolates active components."""
for record in raw_data:
if record.get("active"):
print(record)
Optimization Steps Performed
- AST Tokenizer Audit: Scanned the raw generated code stream and identified semantic spacing breakage points.
Indentation Normalizer: Replaced the corrupt tab and broken 2-space patterns with absolute 4-space alignment blocks.
3.
Delimiter Verification: Verified logical token execution parameters, ensuring all braces and brackets are structurally balanced.
4.
Air-Gapped Delivery: Transferred the fully-compiled, syntax-compliant Python logic block directly to the workspace without calling external APIs.
Connects securely to your tools. The creator never sees your data.
About this skill
Stop wasting your AI token budget on stupid syntax errors. When autonomous AI agents or coding assistants write Python, they frequently generate incomplete code streams, mix spaces with tabs, or leave brackets unbalanced. This forces the agent into a costly loop—spending your real money on next prompts just to fix its own basic syntax mistakes.
Python SyntaxCleaner acts as an immediate, inline AST verification loop. It intercepts raw AI-generated code strings, checks token consistency, and silently self-corrects layout alignment before it ever touches your active repository files or triggers pre-commit failures.
Unlike standard IDE formatters that only flag errors after the code is already pasted into your workspace, this skill blocks syntax regressions at the compilation layer, protecting multi-agent pipelines from breaking mid-expression.
What it does
- Token-Saving Auto-Correction: Instantly intercepts and self-corrects mixed indentation and Python TabErrors before your AI agent wastes a single dollar on a validation re-run.
- AST Structural Symmetry Analysis: Automatically balances opened parentheses, brackets, and multi-line string delimiters generated by cut-off AI streams.
- Zero Overhead / Air-Gapped: Operates 100% locally utilizing native Python stdlib parsing metrics. Absolute zero external network queries, ensuring your proprietary code never leaks.
- Layout Architecture Adaptation: Deep-scans target source repositories to accurately identify and match legacy spacing parameters prior to execution, blocking unwanted silent rewrites.
Technical Specifications & Compliance
- PEP8 Standards Alignment: Enforces strict compliance with standard four-space Python rules, neutralizing mixed tab/space blocks at the application boundary.
- Zero-Dependency Security: Functions independently without third-party package allocations, eliminating local supply-chain injection vectors.
When to use
- Refactoring dense, nested asynchronous loop blocks where indentation parameters degrade during long generation cycles.
- Standardizing consistent code layout quality across high-velocity development runs using Cursor or Claude Code.
- Insulating local Git pre-commit sequences from accidental syntax failures or fragmented expressions.
When NOT to use
- Synthesizing non-code output categories including Markdown text arrays, plain text instructions, or standard terminal documentation configurations.
- Executing active filesystem modification tracks or runtime code building routines (this engine functions strictly in a passive, read-only configuration).
Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Code Quality & Review.
Static and formatting constraints only. The engine measures spacing structures, layout metrics, and token boundaries inside local memory configurations without running internal compiler binaries, executing live unit tests, or modifying file trees directly. Non-code documents including markdown blocks, inline prose, or static configuration matrices are completely out of scope. Workspace evaluations require fully formed Python file contexts; fragmented micro-snippets under 10 total lines of code volume may be bypassed to prevent incomplete AST token tracing.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
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- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before buying a skill.
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Permissions
File Scopes
Read-only sandbox access to Python source files. Programmatically parses existing indentation formatting metrics (tabs vs spaces) inside local memory buffers to ensure absolute layout style matching before presenting code back to the agent context, with zero permission for active directory mutations, terminal execution, or outbound network telemetry.