PLANS For Fable 5: Rebuilding My /Plan Skill for Mythos Class Models
A deep agentic-engineering vlog rebuilding a reusable /plan 'meta skill' (a prompt that outputs plans) for state-of-the-art Mythos-class models, arguing that great planning is great engineering and upgrading the plan template to an HTML format with generated images so agents get more valuable tokens to work with.
IndyDevDan63 minTranscript found
Quick learning frame
Read this before watching.
Creative automation uses agents to accelerate production while keeping human taste in story, pacing, selection, and critique.
New playlist item from IndyDevDan; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Skill you build: The ability to write and iterate your own reusable planning meta skill with an explicit plan template so agents produce the exact outcomes you specify instead of guessing.
Watch for the shift from claim to mechanism. The learning value is the point where the transcript reveals a repeatable action, tool boundary, context move, review habit, or artifact.
Concept diagram
Where this video fits.
01Brief
02Source
03Generation
04Selection
05Edit
06Taste Review
Deep lesson
Turn this video into working knowledge.
12,575 cleaned transcript words reviewed across 3,791 timed caption segments.
Thesis
PLANS For Fable 5: Rebuilding My /Plan Skill for Mythos Class Models teaches a practical creative automation move: A deep agentic-engineering vlog rebuilding a reusable /plan 'meta skill' (a prompt that outputs plans) for state-of-the-art Mythos-class models, arguing that great planning is great engineering and upgrading the plan template to an HTML format with generated images so agents get more valuable tokens to work with.
The goal is not to remember the video. The goal is to extract the operating principle, tie it to timestamped evidence, test how far the claim transfers, and make something reusable.
0:18
Own your planning
“cares about the plan skill? Why is planning so important? Your planning skill is one of the most important tools you and your agent have. Most engineers hand this off to the model, they hand it off to...”
Most engineers hand planning off to the model's built-in /plan, which forces the model to guess what you want; the argument is you should write your own planning skill, prompt, and template because great planning is great engineering, more upfront planning investment means less reviewing, and this only compounds as model capability rises. Open a raw.md and just write out, in your own words, why and what you're planning for a real task before letting any agent touch it, to serve as context for both you and the agent.
29:31
Property-based engineering
“variable here. This is looking pretty good. I will pull the workflow just to give our agent a starting place here. Analyze the requirements, parse the user prompt to understand the core problem, desired outcome, explore the code...”
He does 'property-based engineering', starting from priorities (the trade-off trifecta of performance > speed >= cost, sacrificing speed and cost to win) and building a plan format the model must fill in while leaving non-templated sections untouched, with phases each containing a problem/solution, relevant files, tasks, and specific validation commands to prove the phase is complete. Draft a reusable plan template with named phases where each phase lists the problem, the files touched, and the exact commands that validate it's done, and reuse it on your next task.
51:04
HTML and image specs
“use PyCroco for research. It is pulling in the Py versus Claude code base, and this is publicly available, of course, on my GitHub repository. This is a public code base available to anyone, and it contains several...”
The key Mythos-class upgrade is outputting the plan in HTML rather than plain text because, per an Anthropic write-up he cites, more valuable tokens give agents a slight edge on producing the result you want; he also generates images (via a GPT image model script) so both humans and multimodal agents ingest the plan deeper, accepting the higher token cost knowingly, run on Opus 4.8 at high effort. Convert one of your plan templates into an HTML format and add a generated diagram or image, then compare whether the agent's output tracks your intent better than the plain-text version.
01
Brief
Start with this video's job: A deep agentic-engineering vlog rebuilding a reusable /plan 'meta skill' (a prompt that outputs plans) for state-of-the-art Mythos-class models, arguing that great planning is great engineering and upgrading the plan template to an HTML format with generated images so agents get more valuable tokens to work with. Treat "Brief" as the outcome you are trying to make visible, not a topic label. Anchor it to 0:18, where the video says: “cares about the plan skill? Why is planning so important? Your planning skill is one of the most important tools you and your agent have. Most engineers hand this off to the model, they hand it off to...”
02
Source
Use "Source" to locate the part of the creative automation workflow the video is demonstrating. Ask what changes in your real setup if this claim is true. Anchor it to 29:31, where the video says: “variable here. This is looking pretty good. I will pull the workflow just to give our agent a starting place here. Analyze the requirements, parse the user prompt to understand the core problem, desired outcome, explore the code...”
03
Generation
Turn "Generation" into the reusable artifact for this lesson: A creative workflow board with critique criteria and review checkpoints. This is where watching becomes something you can inspect and reuse.
04
Selection
Use "Selection" as the application surface. Decide whether the idea touches a browser flow, a local file, a model choice, a source document, a UI, or a review step.
05
Edit
Use "Edit" to prove the lesson. The evidence should connect back to the video title, transcript anchors, and a concrete output, not a generic best-practice claim.
06
Taste Review
Use "Taste Review" to carry the idea forward: save the prompt, checklist, diagram, or operating rule that would make the next agent run better.
Example
Source-backed work packet
Convert the video into a scoped task that includes the transcript claim, target workflow, acceptance criteria, and proof. The output should be a creative workflow board with critique criteria and review checkpoints..
Example
Claim vs. demo brief
Separate what the speaker claims, what the demo actually proves, and what still needs outside verification before you adopt the workflow.
Example
Teach-back module
Transform the lesson into a definition, a mechanism diagram, one misconception, one practice exercise, and a check-for-understanding question.
Do not learn it wrong
Treating the title as the lesson without checking what the transcript actually says.
Letting the prompt drift into generic advice that could apply to any video in the playlist.
Copying the tool setup without identifying the operating principle that transfers to your own stack.
Skipping the artifact, which means the learning never becomes operational or inspectable.
Do not count this as learned until these are true.
01
State the transcript-backed claim in your own words: A deep agentic-engineering vlog rebuilding a reusable /plan 'meta skill' (a prompt that outputs plans) for state-of-the-art Mythos-class models, arguing that great planning is great engineering and upgrading the plan template to an HTML format with generated images so agents get more valuable tokens to work with.
02
Explain the practical stakes without hype: New playlist item from IndyDevDan; queued for transcript-backed review, topic mapping, and a practical learning artifact.
03
Map the idea onto the Brief -> Source -> Generation -> Selection -> Edit -> Taste Review sequence and name the weakest link.
04
Produce the artifact and include the evidence that proves it: A creative workflow board with critique criteria and review checkpoints.
Put it into practice
Give this grounded prompt to Codex or Claude after watching.
You are helping me turn one specific YouTube video into real, durable learning.
Source video:
- Title: PLANS For Fable 5: Rebuilding My /Plan Skill for Mythos Class Models
- URL: https://www.youtube.com/watch?v=DzbqeO_diOQ
- Topic: Creative Automation
- My current learning frame: Write a raw.md rationale and priorities, build a reusable HTML plan-template meta skill with phased problem/solution/validation-command sections, then run it against a real feature spec on a capable model and judge whether the output matches your intended outcome.
- Why this matters: New playlist item from IndyDevDan; queued for transcript-backed review, topic mapping, and a practical learning artifact.
Transcript anchors from this exact video:
- 0:18 / Evidence 1: "cares about the plan skill? Why is planning so important? Your planning skill is one of the most important tools you and your agent have. Most engineers hand this off to the model, they hand it off to..."
- 6:34 / Evidence 2: "when you ask them the right way, only when you present them with the right information. So this is our API. We're going to run any coding agent. I like to use the PyCoding agent, and I like..."
- 20:14 / Evidence 3: "With these powerful models, this is unnecessary, but I'm doing this not just for the agents, I'm doing it for my team and myself. So, that's the purpose of this. Create a detailed implementation plan based on the..."
- 29:31 / Evidence 4: "variable here. This is looking pretty good. I will pull the workflow just to give our agent a starting place here. Analyze the requirements, parse the user prompt to understand the core problem, desired outcome, explore the code..."
- 31:33 / Evidence 5: "right? That's the whole idea. So, we're just using some really basic agent of coding. We could pretty much throw any model we want to at but for now, we're just going to keep it simple. We're going..."
- 51:04 / Evidence 6: "use PyCroco for research. It is pulling in the Py versus Claude code base, and this is publicly available, of course, on my GitHub repository. This is a public code base available to anyone, and it contains several..."
- 61:56 / Evidence 7: "places to spend time to increase our ability to move at the agentic speed while building out very, very valuable, very hand-picked, hand-structured, heavily engineered foundations and fabrics and meta skills and meta prompts for our agents to..."
Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A creative workflow board with critique criteria and review checkpoints.
5. Include:
- a plain-English definition of the core idea
- a diagram or structured model using this sequence: Brief -> Source -> Generation -> Selection -> Edit -> Taste Review
- 3 concrete examples that apply the video idea to real agentic work
- 2 failure modes the video helps prevent
- a checklist I can use the next time I run Codex or Claude
- one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.
Quality bar:
- Make this specific to "PLANS For Fable 5: Rebuilding My /Plan Skill for Mythos Class Models", not a generic Creative Automation essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.
Misconceptions
What to stop believing.
Creative AI removes the need for taste.
It increases the need for taste because output volume explodes.
The best prompt is enough.
References, critique, iteration, and post-production matter just as much.
Practice studio
Learning only counts when you make something.
01
Transcript evidence map
Separate what the video actually says from what you already believe about the topic.
3 source-backed takeaways with timestamps, confidence, and a transfer note.02
One useful artifact
Apply the video to a real workflow and produce a creative workflow board with critique criteria and review checkpoints..
A reusable artifact with a done signal and one verification step.03
Teach-back card
Explain the lesson to someone who has not watched the video yet.
A 90-second explanation, one diagram, one example, and one misconception to avoid.
Recall check
Answer first, then reveal — without rewatching.
Why does the presenter argue you should write your own planning skill instead of using the model's built-in /plan?
What is the trade-off trifecta and how does the new plan template prioritize it?
Why does he switch the plan format to HTML and add generated images?
Source shelf
Use the video as a doorway, then verify with primary sources.
