Added example reasoning prompts

examples/thinker/critique_answer.llm

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+Create a Gendo subroutine called critique_answer that accepts one string 
+parameter named candidate and returns a critique string. Inside use prompt with 
+model gpt-4 and temperature 0.0 asking the LLM to act as a strict logician who 
+inspects the supplied candidate for logical, factual or reasoning errors. If 
+the candidate is flawless the LLM must reply exactly with No issues found. 
+Otherwise it must list the problems it detects in plain text sentences. The 
+subroutine simply outputs whatever the LLM returns so later stages can decide 
+whether refinement is required.

examples/thinker/generate_candidates.llm

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+Create a Gendo subroutine named generate_candidates that takes a single string 
+parameter called question and returns an array of three candidate answers. 
+Inside the subroutine call prompt with model gpt-4, temperature 0.7 and n set 
+to 3. The prompt must instruct the LLM to think step by step, label its 
+reasoning with the exact tag THOUGHT and its conclusion with the exact tag 
+ANSWER, and to follow that format without any extra text. Each of the three 
+completions should be collected into a list so the caller receives 
+candidates[0], candidates[1] and candidates[2] as separate strings in 
+THOUGHT / ANSWER format.

examples/thinker/judge_best.llm

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+Create a Gendo subroutine named judge_best that receives an array called 
+candidates containing three candidate strings and returns the best candidate’s 
+ANSWER text. Call prompt with model gpt-4 and temperature 0.0, giving the LLM 
+the three numbered candidates and asking it to choose which is most correct and 
+logically sound. Instruct it to respond in two lines where the first begins 
+with BEST_ANSWER: followed by the full THOUGHT / ANSWER content of the chosen 
+candidate and the second begins with REASON: followed by a brief justification. 
+After the prompt call extract only the text after BEST_ANSWER: and return that 
+as the final answer.

examples/thinker/reasoning_loop.llm

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+Create a top-level Gendo routine called reasoning_loop that accepts the user’s 
+question string and produces a final answer string. The routine must call 
+generate_candidates to obtain an array of candidates, map critique_answer 
+across that array to gather critiques, iterate through the indexes 0 to 2 and 
+for each position either keep the original candidate if its critique is No 
+issues found. or call refine_answer to replace it with a corrected version, 
+collect the resulting candidates into a new array and pass that array to 
+judge_best, then output the string returned by judge_best. The routine ends by 
+printing the final answer so users see only the selected, vetted ANSWER.

examples/thinker/refine_answer.llm

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+Create a Gendo subroutine called refine_answer that takes two string 
+parameters, candidate and critique, and returns a revised candidate. If the 
+critique text equals No issues found. the subroutine should bypass any LLM call 
+and return the original candidate unchanged. Otherwise call prompt with model 
+gpt-4, temperature 0.5 and include both the original candidate and the critique 
+in the prompt. Instruct the LLM to rewrite the answer fixing every listed issue 
+while preserving as much correct reasoning as possible, and to output the 
+result in the exact THOUGHT / ANSWER template. The revised text becomes the 
+return value.