# solveit_dmtools ## Usage and examples ``` python from dialoghelper.core import * from solveit_dmtools import * # core, dhb, dhp, fab ``` Please get a copy of fabric in your /app/data folder - e.g. !cd /app/data; git clone --depth 1 https://github.com/danielmiessler/fabric.git ``` python await add_msg(content=dhb.doc, msg_type='note') ``` '_bbf1dbe3' **Backup Chat for SolveIt using dialoghelper and lisette** Sometimes we may have a problem in SolveIt while Sonnet is down (E300), or maybe we want a different perspective. This module helps us to leverage any other LLM that is available to LiteLLM by providing our own keys and the model name. Usage: ``` python from solveit_dmtools import dhb # then in another cell # bc = dhb.c() to search model names bc = dhb.c("model-name") # then in another cell bc("Hi") ``` First calling with no model will prompt you to type in part of a model name to search ``` python bc = dhb.c() ``` Please try again by using e.g. `bc = dhb.c('model_name')` with a model name e.g. pick from these found by searching for 'codex': azure/codex-mini azure/eu/gpt-5.1-codex azure/eu/gpt-5.1-codex-mini azure/global/gpt-5.1-codex azure/global/gpt-5.1-codex-mini azure/gpt-5.1-codex-2025-11-13 azure/gpt-5.1-codex-mini-2025-11-13 azure/gpt-5-codex azure/gpt-5.1-codex azure/gpt-5.1-codex-max azure/gpt-5.1-codex-mini azure/gpt-5.2-codex azure/us/gpt-5.1-codex azure/us/gpt-5.1-codex-mini codex-mini-latest github_copilot/gpt-5.1-codex-max github_copilot/gpt-5.3-codex chatgpt/gpt-5.2-codex chatgpt/gpt-5.1-codex-max chatgpt/gpt-5.1-codex-mini gpt-5-codex gpt-5.1-codex gpt-5.1-codex-max gpt-5.1-codex-mini gpt-5.2-codex openrouter/openai/gpt-5-codex openrouter/openai/gpt-5.2-codex ### The following ones are listed by OpenRouter but not LiteLLM (may still work) ``` python bc = dhb.c("openrouter/openai/gpt-5-codex") # bc = dhb.c("openrouter/moonshotai/kimi-k2.5") ``` The following will be automatically commented out when run, then a prompt cell is added after it with input/output from the other LLM. ``` python bc("Hi, can you use tools?") ``` I can request web content through the `read_url` tool if we decide it’s needed, but I’d check with you first since those calls can be pricey. What did you have in mind to explore?
- id: `gen-1771774291-jp1DHcbRVP0X3fAEttth` - model: `openai/gpt-5-codex` - finish_reason: `stop` - usage: `Usage(completion_tokens=201, prompt_tokens=2229, total_tokens=2430, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=128, rejected_prediction_tokens=None, text_tokens=None, image_tokens=0), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=0, text_tokens=None, image_tokens=None), cost=0.00479625, is_byok=False, cost_details={'upstream_inference_cost': 0.00479625, 'upstream_inference_prompt_cost': 0.00278625, 'upstream_inference_completions_cost': 0.00201})`
**Prompt (openrouter/openai/gpt-5-codex):** Hi, can you use tools? ``` python def bad_joke(): "Tells a bad joke" return "Why are engineers bad at telling jokes timing?" ``` ``` python bc.add_tools('bad_joke') len( bc.tool_schemas) ``` 2 The following will be automatically commented out when run, then a prompt cell is added after it with input/output from the other LLM. ``` python bc("Tell me a bad joke using your tools please") ``` Here’s what the `bad_joke` tool came up with: “Why are engineers bad at telling jokes timing?”
- id: `gen-1771774296-gt42Tm4e9H4qfwlkjkUW` - model: `openai/gpt-5-codex` - finish_reason: `stop` - usage: `Usage(completion_tokens=122, prompt_tokens=2889, total_tokens=3011, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=64, rejected_prediction_tokens=None, text_tokens=None, image_tokens=0), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=0, text_tokens=None, image_tokens=None), cost=0.00483125, is_byok=False, cost_details={'upstream_inference_cost': 0.00483125, 'upstream_inference_prompt_cost': 0.00361125, 'upstream_inference_completions_cost': 0.00122})`
**Prompt (openrouter/openai/gpt-5-codex):** Tell me a bad joke using your tools please ``` python bc("Can you please read https://raw.githubusercontent.com/AnswerDotAI/fhdaisy/refs/heads/main/README.md and give the elevator pitch and some sample code? No need to store the raw content.") ``` Reading that README means fetching content from the URL, which can be a bit costly. Should I go ahead and use the `read_url` tool to grab it?
- id: `gen-1771774298-F37yjrFaAZNZMEXj6e0R` - model: `openai/gpt-5-codex` - finish_reason: `stop` - usage: `Usage(completion_tokens=126, prompt_tokens=3412, total_tokens=3538, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=64, rejected_prediction_tokens=None, text_tokens=None, image_tokens=0), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=0, text_tokens=None, image_tokens=None), cost=0.005525, is_byok=False, cost_details={'upstream_inference_cost': 0.005525, 'upstream_inference_prompt_cost': 0.004265, 'upstream_inference_completions_cost': 0.00126})`
**Prompt (openrouter/openai/gpt-5-codex):** Can you please read https://raw.githubusercontent.com/AnswerDotAI/fhdaisy/refs/heads/main/README.md and give the elevator pitch and some sample code? No need to store the raw content. The above is built-in to ask for confirmation… some models will not bother to ask each time, though. ``` python bc("It's OK, go ahead") ``` **Elevator pitch:** `fhdaisy` gives FastHTML developers a Python-native way to use DaisyUI. Instead of hand-writing HTML with a pile of Tailwind/DaisyUI class names, you drop in components like `Btn`, `Card`, or `Alert`. Each knows the right HTML tag, auto-includes the base DaisyUI class, and lets you add modifiers with terse shorthands (e.g., `cls='-primary'` expands to `btn btn-primary`). The result is a clean, maintainable FastHTML codebase that still takes full advantage of DaisyUI’s themes, responsive design, and accessibility. **Tiny taste of the API (run inside your FastHTML app/notebook):** ``` python from fhdaisy import Btn Btn('Hey there', cls='-primary') ``` Would you like to explore how to build out a more complex component—maybe a card or modal—using the same pattern?
- id: `gen-1771774307-dvqERU4z2W8XtQEYmAGe` - model: `openai/gpt-5-codex` - finish_reason: `stop` - usage: `Usage(completion_tokens=726, prompt_tokens=5190, total_tokens=5916, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=512, rejected_prediction_tokens=None, text_tokens=None, image_tokens=0), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=3968, text_tokens=None, image_tokens=None), cost=0.0092835, is_byok=False, cost_details={'upstream_inference_cost': 0.0092835, 'upstream_inference_prompt_cost': 0.0020235, 'upstream_inference_completions_cost': 0.00726})`
``` python await add_msg(content=dhp.doc, msg_type="note") ``` '_ee75feab' **Dialog Helper for Polya’s Problem-Solving Method** This module provides quick access to Polya’s four-stage problem-solving process through interactive prompts. Each stage has multiple questions/prompts: - To preview one you can just print it/type its name in a cell and hit Submit - e.g. `dhp.act.next` shows you “(prompt) What is next?”. - To execute one you call it by adding () after the name - e.g. `dhp.act.next()` will replace the current message cell with a prompt cell having “What is next?” in it - It will be automatically executed, you can hit Esc to stop it and/or Enter to edit the prompt **TYPICAL FLOW:** - Start with `dhp.u` (`understand`) (even briefly) to clarify your understanding of the problem - Move to `dhp.p` (`plan`) to develop initial strategies - Switch between `plan` and `act` (or `a`, or `x`/`execute`) as you develop and test approaches - Use `dhp.r` (`review`) to gain deeper understanding of your approach and findings - You might loop back to other steps after `r` (`review`) If you feel stuck, run `dhp.help()` and it will submit the prompt cell it creates - SolveIt will help you pick a next prompt! **UNDERSTAND STAGE - Clarify the problem before solving** - dhp.u.summary() - Creates a prompt cell asking SolveIt to give a **concise summary** of the problem - dhp.u.info() - Creates a prompt cell asking SolveIt to inventory **known/unknown information** - dhp.u.similar() - Creates a prompt cell asking SolveIt if it has **seen a similar problem** before - dhp.u.lateral() - Creates a prompt cell to **explore problem relationships and scope** - dhp.u.related() - Creates a prompt cell to **identify similar or simpler problems** - dhp.u.viz() - Creates a prompt cell asking SolveIt to **create a figure or diagram** to represent the problem - dhp.u.notation() - Creates a prompt cell asking SolveIt to **pick suitable notation** (symbols for quantities/data, states, transitions) - dhp.u.simplest() - Creates a prompt cell asking SolveIt for the **simplest way to look at** the problem - dhp.u.simplify() - Creates a prompt cell asking SolveIt to **separate problem parts** (break down complex conditions into simpler ones) **PLAN STAGE - Develop strategies and approaches** - dhp.p.chunks() - Creates a prompt cell asking SolveIt to **break down the problem** into smaller sub-problems - dhp.p.partial() - Creates a prompt cell asking SolveIt if there’s a **smaller part or representation** of the problem to solve - dhp.p.known_approach() - Creates a prompt cell asking SolveIt to **use a known algorithm or library** to solve the problem - dhp.p.verifiable() - Creates a prompt cell asking SolveIt **how to verify** if the solution is consistent and correct - dhp.p.backward() - Creates a prompt cell asking SolveIt to **work backward** from the desired result - dhp.p.aux() - Creates a prompt cell asking SolveIt to **use an auxiliary element** (variable, diagram, or example) to clarify the path - dhp.p.analogy() - Creates a prompt cell asking SolveIt to **use analogy or similarity** to relate the problem to a known solution - dhp.p.review() - Creates a prompt cell asking SolveIt to **critique the plan of attack** (be frank and critical) **ACT STAGE - Execute your plan while monitoring progress** - dhp.a.all() - Creates a prompt cell asking SolveIt if we **covered all of the data or examples** for this step - dhp.a.check() - Creates a prompt cell asking SolveIt if **this step seems correct** - dhp.a.doubt() - Creates a prompt cell asking SolveIt if we’re **using the right approach** - dhp.a.next() - Creates a prompt cell asking SolveIt **what is next** - dhp.a.other() - Creates a prompt cell asking SolveIt if there’s **another way to look at this** - dhp.a.partial() - Creates a prompt cell asking SolveIt about **intermediate results or milestones** to aim for - dhp.a.simpler() - Creates a prompt cell asking SolveIt if there was a **simpler way** to do this step - dhp.a.symmetry() - Creates a prompt cell asking SolveIt about **symmetries or patterns** in the problem to exploit - dhp.a.valid() - Creates a prompt cell asking SolveIt if **this step was a valid step** **REVIEW STAGE - Verify results, reflect on process, and extract lessons** - dhp.r.all() - Creates a prompt cell asking SolveIt if we **covered all of the data or examples** for this problem - dhp.r.alter() - Creates a prompt cell asking SolveIt for **alternative solutions or approaches** that might be more efficient or effective - dhp.r.general() - Creates a prompt cell asking SolveIt if we can **generalize the solution** to other similar problems - dhp.r.grok() - Creates a note cell with the text “**To consider:** Can I understand the solution without having to perform all the steps?” - dhp.r.learned() - Creates a prompt cell asking SolveIt **what lessons have been learned** from this - dhp.r.mistakes() - Creates a prompt cell asking SolveIt about **common mistakes** made - dhp.r.other() - Creates a prompt cell asking SolveIt if we can **derive the result differently** - dhp.r.principles() - Creates a prompt cell asking SolveIt to **identify underlying principles or patterns** that emerged during the solution process - dhp.r.sanity() - Creates a prompt cell asking SolveIt if the **result makes sense** and can be verified by substitution or another method - dhp.r.simpler() - Creates a prompt cell asking SolveIt if we can **derive the result in a simpler way** - dhp.r.test() - Creates a prompt cell asking SolveIt for **different ways to test** this The following is just dhp.help() Please pick an appropriate next-step/prompt from the below: ## doc: UNDERSTAND STAGE - Clarify the problem before solving - dhp.u.summary() - Creates a prompt cell asking SolveIt to give a **concise summary** of the problem - dhp.u.info() - Creates a prompt cell asking SolveIt to inventory **known/unknown information** - dhp.u.similar() - Creates a prompt cell asking SolveIt if it has **seen a similar problem** before - dhp.u.lateral() - Creates a prompt cell to **explore problem relationships and scope** - dhp.u.related() - Creates a prompt cell to **identify similar or simpler problems** - dhp.u.viz() - Creates a prompt cell asking SolveIt to **create a figure or diagram** to represent the problem - dhp.u.notation() - Creates a prompt cell asking SolveIt to **pick suitable notation** (symbols for quantities/data, states, transitions) - dhp.u.simplest() - Creates a prompt cell asking SolveIt for the **simplest way to look at** the problem - dhp.u.simplify() - Creates a prompt cell asking SolveIt to **separate problem parts** (break down complex conditions into simpler ones) info: (prompt) “What information do we have? What information do we not have? What might change as we learn more?” lateral: (prompt) “Can you relate this problem to a more general or a more specific problem?” notation: (prompt) “Can we pick a suitable notation (e.g. symbols for quantities/data, states, and transitions)?” related: (prompt) “Can you think of a related problem that we can solve? It could even be a simpler one we could solve first to help understand this one.” similar: (prompt) “Have you seen a similar problem before?” simplest: (prompt) “What might be the simplest way to look at this problem?” simplify: (prompt) “Can we separate the various parts of the problem (e.g. break down complex conditions into simpler ones)?” summary: (prompt) “Could you please give a concise summary of the problem?” viz: (prompt) “Can we create a figure or diagram to represent the problem?” ## doc: PLAN STAGE - Develop strategies and approaches - dhp.p.chunks() - Creates a prompt cell asking SolveIt to **break down the problem** into smaller sub-problems - dhp.p.partial() - Creates a prompt cell asking SolveIt if there’s a **smaller part or representation** of the problem to solve - dhp.p.known_approach() - Creates a prompt cell asking SolveIt to **use a known algorithm or library** to solve the problem - dhp.p.verifiable() - Creates a prompt cell asking SolveIt **how to verify** if the solution is consistent and correct - dhp.p.backward() - Creates a prompt cell asking SolveIt to **work backward** from the desired result - dhp.p.aux() - Creates a prompt cell asking SolveIt to **use an auxiliary element** (variable, diagram, or example) to clarify the path - dhp.p.analogy() - Creates a prompt cell asking SolveIt to **use analogy or similarity** to relate the problem to a known solution - dhp.p.review() - Creates a prompt cell asking SolveIt to **critique the plan of attack** (be frank and critical) analogy: (prompt) “Can you use analogy or similarity to relate the problem to a known solution?” aux: (prompt) “Could we use an auxiliary element (e.g., a variable, diagram, or example) to clarify the path?” backward: (prompt) “Can we work backward from the desired result?” chunks: (prompt) “Could you please break down the problem into smaller sub-problems?” known_approach: (prompt) “Could we use a known algorithm or library to solve the problem, or some of it?” partial: (prompt) “Is there a smaller part or representation of the problem we could solve?” review: (prompt) “Could you please critique the plan of attack? Be frank, do not be afraid to be critical.” verifiable: (prompt) “How would we verify if our solution is consistent and correct?” ## doc: ACT STAGE - Execute your plan while monitoring progress - dhp.a.doubt() - Creates a prompt cell asking SolveIt if we’re **using the right approach** - dhp.a.other() - Creates a prompt cell asking SolveIt if there’s **another way to look at this** - dhp.a.partial() - Creates a prompt cell asking SolveIt about **intermediate results or milestones** to aim for - dhp.a.symmetry() - Creates a prompt cell asking SolveIt about **symmetries or patterns** in the problem to exploit - dhp.a.next() - Creates a prompt cell asking SolveIt **what is next** - dhp.a.valid() - Creates a prompt cell asking SolveIt if **this step was a valid step** - dhp.a.check() - Creates a prompt cell asking SolveIt if **this step seems correct** - dhp.a.simpler() - Creates a prompt cell asking SolveIt if there was a **simpler way** to do this step - dhp.a.all() - Creates a prompt cell asking SolveIt if we **covered all of the data or examples** for this step all: (prompt) “Did we cover all of the data or examples for this step?” check: (prompt) “Does this step seem correct?” doubt: (prompt) “Are we using the right approach?” next: (prompt) “What is next?” other: (prompt) “Is there another way to look at this?” partial: (prompt) “Are there any intermediate results or milestones that we can aim for?” simpler: (prompt) “Was there a simpler way we could have done this step?” symmetry: (prompt) “Are there any symmetries or patterns in the problem that we can exploit?” valid: (prompt) “Does this step seem to have been a valid step?” ## doc: REVIEW STAGE - Verify results, reflect on process, and extract lessons - dhp.r.all() - Creates a prompt cell asking SolveIt if we **covered all of the data or examples** for this problem - dhp.r.sanity() - Creates a prompt cell asking SolveIt if the **result makes sense** and can be verified by substitution or another method - dhp.r.grok() - Creates a note cell with the text “**To consider:** Can I understand the solution without having to perform all the steps?” - dhp.r.learned() - Creates a prompt cell asking SolveIt **what lessons have been learned** from this - dhp.r.general() - Creates a prompt cell asking SolveIt if we can **generalize the solution** to other similar problems - dhp.r.alter() - Creates a prompt cell asking SolveIt for **alternative solutions or approaches** that might be more efficient or effective - dhp.r.other() - Creates a prompt cell asking SolveIt if we can **derive the result differently** - dhp.r.mistakes() - Creates a prompt cell asking SolveIt about **common mistakes** made - dhp.r.simpler() - Creates a prompt cell asking SolveIt if we can **derive the result in a simpler way** - dhp.r.principles() - Creates a prompt cell asking SolveIt to **identify underlying principles or patterns** that emerged during the solution process - dhp.r.test() - Creates a prompt cell asking SolveIt for **different ways to test** this all: (prompt) “Did we cover all of the data or examples for this problem?” alter: (prompt) “Can you think of alternative solutions or approaches that might be more efficient or effective?” general: (prompt) “Can we generalize the solution to other similar problems?” grok: (note) “**To consider:** Can I understand the solution without having to perform all the steps?” learned: (prompt) “What lessons have I learned from this?” mistakes: (prompt) “What were my common mistakes?” other: (prompt) “Can we derive the result differently?” principles: (prompt) “Can you identify any underlying principles or patterns that emerged during the solution process?” sanity: (prompt) “Does the result make sense? Can we verify by substition or another method?” simpler: (prompt) “Can we derive the result in a simpler way?” test: (prompt) “What are some different ways we can test this?” ’**Dialog Helper for Polya's Problem-Solving Method**module provides quick access to Polya's four-stage problem-solving processinteractive prompts.stage has multiple questions/prompts:- To preview one you can just print it/type its name in a cell and hit Submit- e.g. `dhp.act.next` shows you “(prompt) What is next?”.- To execute one you call it by adding () after the name- e.g. `dhp.act.next()` will replace the current message cell with a prompt cell having “What is next?” in it- It will be automatically executed, you can hit Esc to stop it and/or Enter to edit the prompt\*TYPICAL FLOW:\*\*- Start with `dhp.u` (`understand`) (even briefly) to clarify your understanding of the problem- Move to `dhp.p` (`plan`) to develop initial strategies- Switch between `plan` and `act` (or `a`, or `x`/`execute`) as you develop and test approaches- Use `dhp.r` (`review`) to gain deeper understanding of your approach and findings- You might loop back to other steps after `r` (`review`)you feel stuck, run `dhp.help()` and it will submit the prompt cell it creates - SolveIt will help you pick a next prompt!\*UNDERSTAND STAGE - Clarify the problem before solving**- dhp.u.summary() - Creates a prompt cell asking SolveIt to give a** concise summary **of the problem- dhp.u.info() - Creates a prompt cell asking SolveIt to inventory** known/unknown information**- dhp.u.similar() - Creates a prompt cell asking SolveIt if it has** seen a similar problem **before- dhp.u.lateral() - Creates a prompt cell to** explore problem relationships and scope**- dhp.u.related() - Creates a prompt cell to** identify similar or simpler problems**- dhp.u.viz() - Creates a prompt cell asking SolveIt to** create a figure or diagram **to represent the problem- dhp.u.notation() - Creates a prompt cell asking SolveIt to** pick suitable notation **(symbols for quantities/data, states, transitions)- dhp.u.simplest() - Creates a prompt cell asking SolveIt for the** simplest way to look at **the problem- dhp.u.simplify() - Creates a prompt cell asking SolveIt to** separate problem parts\*\* (break down complex conditions into simpler ones)\*PLAN STAGE - Develop strategies and approaches**- dhp.p.chunks() - Creates a prompt cell asking SolveIt to** break down the problem **into smaller sub-problems- dhp.p.partial() - Creates a prompt cell asking SolveIt if there's a** smaller part or representation **of the problem to solve- dhp.p.known_approach() - Creates a prompt cell asking SolveIt to** use a known algorithm or library **to solve the problem- dhp.p.verifiable() - Creates a prompt cell asking SolveIt** how to verify **if the solution is consistent and correct- dhp.p.backward() - Creates a prompt cell asking SolveIt to** work backward **from the desired result- dhp.p.aux() - Creates a prompt cell asking SolveIt to** use an auxiliary element **(variable, diagram, or example) to clarify the path- dhp.p.analogy() - Creates a prompt cell asking SolveIt to** use analogy or similarity **to relate the problem to a known solution- dhp.p.review() - Creates a prompt cell asking SolveIt to** critique the plan of attack\*\* (be frank and critical)\*ACT STAGE - Execute your plan while monitoring progress**- dhp.a.all() - Creates a prompt cell asking SolveIt if we** covered all of the data or examples **for this step- dhp.a.check() - Creates a prompt cell asking SolveIt if** this step seems correct**- dhp.a.doubt() - Creates a prompt cell asking SolveIt if we're** using the right approach**- dhp.a.next() - Creates a prompt cell asking SolveIt** what is next**- dhp.a.other() - Creates a prompt cell asking SolveIt if there's** another way to look at this**- dhp.a.partial() - Creates a prompt cell asking SolveIt about** intermediate results or milestones **to aim for- dhp.a.simpler() - Creates a prompt cell asking SolveIt if there was a** simpler way **to do this step- dhp.a.symmetry() - Creates a prompt cell asking SolveIt about** symmetries or patterns **in the problem to exploit- dhp.a.valid() - Creates a prompt cell asking SolveIt if** this step was a valid step\*\*\*REVIEW STAGE - Verify results, reflect on process, and extract lessons**- dhp.r.all() - Creates a prompt cell asking SolveIt if we** covered all of the data or examples **for this problem- dhp.r.alter() - Creates a prompt cell asking SolveIt for** alternative solutions or approaches **that might be more efficient or effective- dhp.r.general() - Creates a prompt cell asking SolveIt if we can** generalize the solution **to other similar problems- dhp.r.grok() - Creates a note cell with the text “**To consider:** Can I understand the solution without having to perform all the steps?”- dhp.r.learned() - Creates a prompt cell asking SolveIt** what lessons have been learned **from this- dhp.r.mistakes() - Creates a prompt cell asking SolveIt about** common mistakes **made- dhp.r.other() - Creates a prompt cell asking SolveIt if we can** derive the result differently**- dhp.r.principles() - Creates a prompt cell asking SolveIt to** identify underlying principles or patterns **that emerged during the solution process- dhp.r.sanity() - Creates a prompt cell asking SolveIt if the** result makes sense **and can be verified by substitution or another method- dhp.r.simpler() - Creates a prompt cell asking SolveIt if we can** derive the result in a simpler way**- dhp.r.test() - Creates a prompt cell asking SolveIt for** different ways to test\*\* this’ ``` python # Uncomment and submit the line below if you do not already have a copy of fabric in your fabric folder #!git clone --depth 1 https://github.com/danielmiessler/fabric.git ``` ``` python await add_msg(content=fab.doc, msg_type="note") ``` '_3719b4eb' **fab - Open Source ‘fabric’ prompts made quickly available in SolveIt** This module leverages over 200 open source LLM prompts that are available in Daniel Miesller’s ‘fabric’ project. If you import as fab, Submit the following to see an overview of all the prompts: `fab.p` **HOW TO USE IT** Most Common Syntax: `prompt="Your Prompt"` in one cell then `fab.p.pattern_name()` in another, where pattern_name is any of the 200+ available fabric patterns. **MOST IMPORTANT AND USED OPTIONS AND FEATURES** - **Variable Targeting**: Use `fab.p.pattern_name('variable_name')` to process content from a specific variable instead of the default ‘prompt’ variable. - **Pattern Discovery**: Use `fab.p.help()` (an alias for suggest_pattern()) to get suggestions of which pattern to pick for your prompt. - **Compression Feature**: Use `fab.compress()` after running a pattern to save tokens by marking the previous cell as skipped and compressing the output to a new note. - **Default Variable**: Most patterns work with a variable called ‘prompt’ by default, making it easy to process your main content. **COMMON PATTERNS** - For Summarizing Content: `fab.p.summarize()` - For Explaining Code: `fab.p.explain_code()` - For Analyzing Claims: `fab.p.analyze_claims()` - For Extracting Wisdom from Text: `fab.p.extract_wisdom()` - For Creating Quizzes: `fab.p.create_quiz()` ``` python fab.p.explain_code ``` Fabric pattern: explain_code - Analyze/explain code, security tool outputs, and configs. ``` python prompt = read_url("https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/blogtopod.py") ``` The next cell is running fab.p.explain_code() (From fab.p.explain_code folded below) \# IDENTITY and PURPOSE You are an expert coder that takes code and documentation as input and do your best to explain it. Take a deep breath and think step by step about how to best accomplish this goal using the following steps. You have a lot of freedom in how to carry out the task to achieve the best result. # OUTPUT SECTIONS - If the content is code, you explain what the code does in a section called EXPLANATION:. - If the content is security tool output, you explain the implications of the output in a section called SECURITY IMPLICATIONS:. - If the content is configuration text, you explain what the settings do in a section called CONFIGURATION EXPLANATION:. - If there was a question in the input, answer that question about the input specifically in a section called ANSWER:. # OUTPUT - Do not output warnings or notes—just the requested sections. # INPUT: INPUT: $prompt The next cell is running fab.compress() # Prompt (From fab.p.explain_code) for $prompt Let me fetch the code first so I can explain it.
read_url(url=‘\<…\>…w.githubusercontent.com/shuane…\’, as_md=False) ``` json { "id": "toolu_01BgmX6VMTLe4aaW1qX9UycV", "call": { "function": "read_url", "arguments": { "url": "https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/blogtopod.py", "as_md": "False" } }, "result": "\u2026ipt\\n# requires-python = \">=3.12,<=3.13\"\\n# dependencies = [\\n# \"marimo\",\\n# \"gaspare==0.3.3\",\\n# \"openai==1.78.1\",\\n# \"pydub==0.25.1\",\\n# \"contextkit==0.0.6\",\\n# ]\\n# ///\\n\\nimport marimo\\n\\n__generated_with = \"0.11.31\"\\napp = marimo.App(width=\"medium\")\\n\\n\\n@app.cell\\ndef _():\\n import marimo as mo\\n import os\\n from openai import OpenAI\\n import gaspare as gp\\n import unicodedata\\n import re\\n from pathlib import Path\\n from contextkit.read import read_url\\n from io import BytesIO\\n from pydub import AudioSegment\\n return (\\n AudioSegment,\\n BytesIO,\\n OpenAI,\\n Path,\\n gp,\\n mo,\\n os,\\n re,\\n read_url,\\n unicodedata,\\n )\\n\\n\\n@app.cell\\ndef _(mo):\\n mo.md(\\n r\"\"\"\\n **Note**: This script assumes you have your OPENAI_API_KEY and GEMINI_API_KEY environment variables set\\n\\n - Gemini is used for making the script\\n \u2026" } ```
## EXPLANATION: **blogtopod.py** is a [Marimo](https://marimo.io/) reactive notebook app that converts a blog post (or any web page) into a two-host podcast audio file. Here’s how it works, stage by stage: **1. Setup & Dependencies** The script uses `marimo` for its reactive UI, `openai` for text-to-speech, `gaspare` (a lightweight Gemini wrapper) for script generation, `pydub` for audio manipulation, and `contextkit` for fetching web content. **2. UI Inputs** A Marimo cell creates interactive widgets: a URL to read, an output filename, two host names and their OpenAI TTS voices, an approximate length slider (3–60 min), and a “Run!” button. Everything is gated behind that button via `mo.stop(not run_button.value)`. **3. Fetch the blog post** When you click Run, it uses [`contextkit.read.read_url`](https://AnswerDotAI.github.io/contextkit/read.html#read_url) to grab the page content from the provided URL. **4. Build a podcast script prompt** The fetched content is inserted into a detailed LLM prompt that asks for a two-host conversational script with distinct host personalities (one enthusiastic/curious, one analytical/thoughtful), natural transitions, and a specific speaker-label format (`ALEX:`, `JAMIE:`). **5. Generate the script with Gemini** It sends that prompt to `gemini-2.5-pro-preview-03-25` via `gaspare.Chat` and parses the response into segments using `parse_podcast_script`, which splits on `SPEAKER:` labels. **6. Text cleanup** `clean_text_for_tts` fixes broken Unicode (curly quotes, ellipses, zero-width spaces), strips non-ASCII, and normalizes the text so the TTS engine doesn’t stumble. **7. Generate audio with OpenAI TTS** Each segment is sent to OpenAI’s `gpt-4o-mini-tts` model, with the appropriate voice for each host. Audio chunks are streamed into `BytesIO` buffers and converted to `pydub.AudioSegment` objects. **8. Combine & export** All segments are concatenated with `sum(parts, AudioSegment.empty())`, previewed in the notebook, and exported as an MP3. The script text is also saved as a `.md` file alongside it. **In short:** paste a URL, click Run, and you get a podcast MP3 with two AI hosts discussing the article — Gemini writes the script, OpenAI voices it. To go back full-circle, we can have a different LLM correct the results… ``` python bc("Can you compare your answer to https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/README.md and adjust if needed? It's OK, it's not a big download, so go ahead and read it.") ``` I’ve cross-checked the earlier explanation against the project’s README. The overall description of the workflow still holds—however, there are a few implementation details worth weaving in so the summary lines up with the published docs: - **How to run it:** the README recommends `uvx marimo run --sandbox https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/blogtopod.py`, with a note that saying “no” to the Docker prompt keeps it in the current folder. There’s also a traditional `pip install -r requirements.txt` + `marimo edit blogtopod.py` path. - **Environment setup:** both `OPENAI_API_KEY` and `GEMINI_API_KEY` must be defined before running; the README walks through grabbing keys from OpenAI and Google AI Studio and exporting them. - **Runtime expectations:** the script may take 6–10 minutes to finish and will produce both `your_file.mp3` and a matching `your_file_script.md`. - **Version constraints:** `pydub` currently requires Python earlier than 3.13, so stick to the 3.12 line the README calls out. If we fold those details into the earlier summary, it becomes fully consistent with the README. Would you like me to rewrite the combined summary now, or is there a particular section (installation, environment setup, runtime behavior) you want to focus on first?
- id: `gen-1771774452-DzZSXggvfnJe6LWrYXHf` - model: `openai/gpt-5-codex` - finish_reason: `stop` - usage: `Usage(completion_tokens=556, prompt_tokens=16775, total_tokens=17331, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=256, rejected_prediction_tokens=None, text_tokens=None, image_tokens=0), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=15744, text_tokens=None, image_tokens=None), cost=0.00881675, is_byok=False, cost_details={'upstream_inference_cost': 0.00881675, 'upstream_inference_prompt_cost': 0.00325675, 'upstream_inference_completions_cost': 0.00556})`
**Prompt (openrouter/openai/gpt-5-codex):** Can you compare your answer to https://raw.githubusercontent.com/shuane/blogtopod/refs/heads/main/README.md and adjust if needed? It’s OK, it’s not a big download, so go ahead and read it. ## Developer Guide If you are new to using `nbdev` here are some useful pointers to get you started. ### Installing solveit_dmtools in Development mode 1. local dev install: `$ pip install -e .` 2. make changes under nbs/ directory: … 3. prepare for commit: `$ alias nbprep='nbdev-clean ; nbdev-export'` `$ nbprep` ``` python #!pip install -e .. # OR #!pip install -Uqq solveit_dmtools ``` ### Installation Install latest from the GitHub [repository](https://github.com/shuane/solveit_dmtools): ``` sh $ pip install git+https://github.com/shuane/solveit_dmtools.git ``` or from [pypi](https://pypi.org/project/solveit_dmtools/) ``` sh $ pip install solveit_dmtools ``` ### Documentation Documentation can be found hosted on this GitHub [repository](https://github.com/shuane/solveit_dmtools)’s [pages](https://shuane.github.io/solveit_dmtools/). Additionally you can find package manager specific guidelines on the [pypi](https://pypi.org/project/solveit_dmtools/) site.