The Agent Skills Directory

[COMMAND_EXECUTION]: In the example script examples/03_full_optimization.py, a mock calculator tool uses the Python eval() function to evaluate mathematical expressions. The implementation attempts to mitigate risk by restricting the execution environment (disabling __builtins__ and providing a limited whitelist of allowed functions). While common for simple calculators, eval() is technically a potential vector for arbitrary code execution if sandboxing is bypassed.
[EXTERNAL_DOWNLOADS]: The library performs network operations to api.minimax.io for its core model communication. It also references numerous reputable technology and research domains, such as anthropic.com, openai.com, arxiv.org, and python.langchain.com, primarily for the purpose of simulating research tasks in its examples and providing reference documentation.
[PROMPT_INJECTION]: The skill creates a surface for indirect prompt injection (Category 8) because it ingests reasoning traces and thinking blocks from LLM responses and uses them to generate analysis reports, optimized prompts, and new Agent Skill files.
Ingestion points: Untrusted data from LLM thinking blocks enters the system via the TraceCapture output and is processed by analyzer.py and optimizer.py.
Boundary markers: The prompts used for analysis and optimization utilize markdown headers and JSON code blocks to distinguish instructions from the ingested trace data.
Capability inventory: The tool possesses the capability to write files to the local filesystem, specifically trace artifacts and generated .md skill files in loop.py and skill_generator.py.
Sanitization: The code relies on JSON schema validation and parsing to interpret structured LLM outputs, which provides basic verification of the generated content structure.

reasoning-trace-optimizer