Redact text from PDF documents for blind review anonymization
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Automated Planning utilities for loading PDDL domains and problems, generating plans using classical planners, validating plans, and saving plan outputs. Supports standard PDDL parsing, plan synthesis, and correctness verification.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
load-problem(domain_path, problem_path)Description:
Loads a PDDL domain file and problem file into a unified planning problem object.
Parameters:
domain_path (str): Path to PDDL domain file.problem_path (str): Path to PDDL problem file.Returns:
problem_object: A unified_planning.model.Problem instance.Example:
problem = load_problem("domain.pddl", "task01.pddl")
Notes:
generate-plan(problem_object)Description: Generates a plan for the given planning problem using a classical planner.
Parameters:
problem_object: A unified planning problem instance.Returns:
plan_object: A sequential plan.Example:
plan = generate_plan(problem)
Notes:
unified_planning.shortcuts.OneshotPlanner.pyperplan.save-plan(plan_object, output_path)Description: Writes a plan object to disk in standard PDDL plan format.
Parameters:
plan_object: A unified planning plan.
output_path (str): Output file path.
Example:
save_plan(plan, "solution.plan")
Notes:
unified_planning.io.PDDLWriter.validate(problem_object, plan_object)Description: Validates that a plan correctly solves the given PDDL problem.
Parameters:
problem_object: The planning problem.plan_object: The generated plan.Returns:
Example:
ok = validate(problem, plan)
Notes:
unified_planning.shortcuts.SequentialPlanValidator.# Load
problem = load_problem("domain.pddl", "task01.pddl")
# Generate plan
plan = generate_plan(problem)
# Validate plan
if not validate(problem, plan):
raise ValueError("Generated plan is invalid")
# Save plan
save_plan(plan, "task01.plan")
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Redact text from PDF documents for blind review anonymization
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.
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DC power flow analysis for power systems. Use when computing power flows using DC approximation, building susceptance matrices, calculating line flows and loading percentages, or performing sensitivity analysis on transmission networks.
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A library for building, validating, visualizing, and serializing dialogue graphs. Use this when parsing scripts or creating branching narrative structures.
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Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
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Generator economic dispatch and cost optimization for power systems. Use when minimizing generation costs, computing optimal generator setpoints, calculating operating margins, or working with generator cost functions.
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