35 Claude Code skills on GitHub: claude-video, WRITING.md, paper2code, skill-doctor, skills-manage

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: 35 Claude Code skills on GitHub: claude-video, WRITING.md, paper2code, skill-doctor, skills-manage
- URL: https://www.youtube.com/watch?v=48-lCMO4sQY
- Topic: AI Strategy
- My current learning frame: Pick three skills described in this video that target distinct problems (e.g. claude-video for modality, Writing.md for prose, skill-doctor for hygiene), install them, and document for each one what input it takes and what concrete output or behavior change it produces.
- Why this matters: This expands the atlas from single prompts into a durable skills library for better agent behavior.

Transcript anchors from this exact video:
- 0:55 / Evidence 1: "conclusion. Senior developer voice enforced by a text file. Claude code builds functional UIs that all look identical by default. The fancy AI official skills repository is a focused pack of agent skills for premium UI generation. Load..."
- 2:37 / Evidence 2: "grade new drafts, infographic and thumbnail creation. Your content system built around your actual voice. Most Claude code prompts are kitchen sink files that give the AI too much room to overthink. Matt Pocock's skills repo is the..."
- 4:21 / Evidence 3: "skills, GitHub import wizard, and a discover scanner that finds project-level skills hiding on your machine. If you want your Claude code CLI to act less like a chat window and more like a 24/7 operating system, you..."
- 8:42 / Evidence 4: "and transpiles them directly into modular Swift UI views, mapping design tokens to your existing Xcode asset catalog and custom view modifiers instead of hardcoding hex colors and absolute font sizes. One AI agent making a major architectural..."
- 10:19 / Evidence 5: "Claude code. PRD creation, rigorous code reviews, step-by-step implementation checks. The entire software development life cycle covered. Install via NPX or GitHub CLI and your agent inherits battle-tested workflows immediately. Hitting a rate limit mid-refactor and losing all..."
- 12:40 / Evidence 6: "own prompts for maximum accuracy. Language-specific tools like the Moonbit best practices plugin stop agents from hallucinating niche syntax. Third Brain V5 skills is a Claude code skill collection to power them. High-level architectural skills, cost-aware LLM pipeline..."
- 14:41 / Evidence 7: "into Claude code that sends your code base to OpenAI Codex, Google Gemini, and open code in parallel. A true cross-lineage peer review. Each model independently reviews and hunts bugs. The main Claude agent cannot merge code until..."

Video-aware target:
- Prompt lane: Creative automation
- Mechanism to extract: Extract the creative production loop, especially where the human keeps taste, selection, and final judgment.
- Artifact to produce: A creative production board with source inputs, prompt recipe, selection criteria, edit pass, and taste-review checkpoints.
- Artifact must include: brief; source inputs; generation recipe; selection criteria; edit/review checkpoint

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, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Extract the creative production loop, especially where the human keeps taste, selection, and final judgment. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A creative production board with source inputs, prompt recipe, selection criteria, edit pass, and taste-review checkpoints.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Brief -> Source material -> Generation -> Selection -> Edit -> Taste review -> Reusable recipe
   - answers to these source questions: What asset is being produced? | What inputs and tools drive it? | Where does human taste intervene?
   - 3 concrete examples that apply the video idea to real agentic work, such as Claude-generated video campaign; image-to-site workflow; voice or video editing loop
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: mistaking novelty for quality; no source/brief discipline; shipping generated media without taste review
   - a checklist for the next real workflow, focused on: brief, inputs, generation, selection, critique
   - one practical exercise with a clear done signal: Build one reusable creative recipe and define what would make the result rejectable.
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 "35 Claude Code skills on GitHub: claude-video, WRITING.md, paper2code, skill-doctor, skills-manage", not a generic AI Strategy essay.
- Anchor each creative step to transcript evidence about inputs, model/tool choices, iteration, editing, or critique.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: generic content advice; tool hype; creative output without selection criteria.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.