9 Free AI Skills That Feel Like Cheat Codes

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

Source video:
- Title: 9 Free AI Skills That Feel Like Cheat Codes
- URL: https://www.youtube.com/watch?v=STH929HARLo
- Topic: Creative Automation
- My current learning frame: Install two or three of these free skills into your harness via their GitHub URLs, then use Graphify to turn a folder of your notes into a queryable graph and a design or animation skill to produce a shareable artifact, comparing the skill-assisted output against the plain model.
- Why this matters: New playlist item from Matt Wolfe; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:42 / Evidence 1: "completely free to install in CodeX or Claude Code or whatever your harness of choice is. These skills and plugins have become pretty universal, so they'll work in like any of the IDEs or agent harnesses that you..."
- 3:08 / Evidence 2: "turns Claude code, but it also works in all the other things as well, into a virtual engineering team, a CEO who rethinks the product, an engineering manager who locks architecture, a designer who catches AI slop, a..."
- 4:44 / Evidence 3: "or plugin that I install. But, just know that all of the plugins and skills that I show have been shown to work in Codex, Claude Code, Open Claw, Hermes, Visual Studio Code, GitHub Copilot. They work across..."
- 14:57 / Evidence 4: "going on inside of the code, but it does still create some pretty cool visuals. So for example, if I want to have Understand Anything map out everything that's going on inside of the Future Tools website, I..."
- 16:47 / Evidence 5: "this?" And it's actually going to look at the workflow, again, as sort of like a memory to help answer that question as opposed to digging through all of the entire code again. And it's going to give..."
- 24:28 / Evidence 6: "But those are two skills that you can use if the design you're trying to get for your site or your app aren't quite coming out the way you want them to by just using the model alone."
- 28:05 / Evidence 7: "one is the preferable version here. And that's what I got for you today. Some of the coolest skills and plugins that you can plug into Claude Co-work, OpenAI's Codex, or any agent or harness that you're using..."

Video-aware target:
- Prompt lane: Context/search
- Mechanism to extract: Extract how context is found, filtered, refreshed, and handed to the agent before it acts.
- Artifact to produce: A context retrieval map with source inventory, indexing/search path, query rules, freshness checks, and agent handoff.
- Artifact must include: source inventory; index/search layer; query rule; freshness check; agent handoff; proof behavior

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 how context is found, filtered, refreshed, and handed to the agent before it acts. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A context retrieval map with source inventory, indexing/search path, query rules, freshness checks, and agent handoff.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Work question -> Source inventory -> Index/search layer -> Retrieval rule -> Agent context -> Answer/proof -> Maintenance
   - answers to these source questions: What source is searched or indexed? | What query/retrieval rule is demonstrated? | How does the agent use the retrieved context?
   - 3 concrete examples that apply the video idea to real agentic work, such as codebase memory; personal wiki retrieval; Elastic search context engineering
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: dumping all context; stale memory; retrieval with no proof trail
   - a checklist for the next real workflow, focused on: sources, query, freshness, handoff, citation/proof
   - one practical exercise with a clear done signal: Write three retrieval queries for one real project and define what each must return.
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 "9 Free AI Skills That Feel Like Cheat Codes", not a generic Creative Automation essay.
- Cite the transcript wherever the prompt names a task boundary, review habit, context move, or verification standard.
- 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 context-window advice; memory hype without retrieval rules; source claims without freshness checks.
- 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.