OpenCode + gstack: Turn Your AI Agent Into a Full Startup Team for Free

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

Source video:
- Title: OpenCode + gstack: Turn Your AI Agent Into a Full Startup Team for Free
- URL: https://www.youtube.com/watch?v=ybwvvo0CMSU
- Topic: Codex + Claude Workflows
- My current learning frame: Install gstack into your agent (Claude Code one-liner, or clone with the host flag for OpenCode/Codex/Cursor), run office-hours on a real idea, and follow the full chain through engineering-review, design-review, review, and ship on a small app.
- Why this matters: New playlist item from AI Stack Engineer; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:00 / Evidence 1: "Most people who try to build a product with an AI coding agent hit the same wall. You open Claude Code or Codex or whatever agent you like. You type a big prompt and it just starts writing."
- 1:36 / Evidence 2: "earlier this year and said he hasn't typed a line of code since December with AI agents now handling around 80% of what he used to write himself. And he described the whole thing as feeling like a..."
- 4:32 / Evidence 3: "random tools. It's a process. The skills run in the order a real sprint runs. Think, then plan, then build, then review, then test, then ship, then reflect. And each step feeds the next one. The office hours..."
- 6:14 / Evidence 4: "on Cloud Code, and that's the main path Gary designed it for. But it works on 10 different agents, and I want to show it running on Open Code, which is the open-source terminal agent a lot of..."
- 6:41 / Evidence 5: "does is drop all the G stack skills into your Open Code config folder. Specifically, the skills directory under your Open Code config. Once that's done, the skills live right there, and Open Code can call them. So,..."
- 9:05 / Evidence 6: "professionals never skip. It puts guardrails on the whole thing so the agent can move fast without doing something destructive. Quick note on the other agents, since not everyone uses open code. If you're on Claude code, it's..."

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 routing matrix for when to use Codex, Claude, browser checks, or manual review.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Inspect -> Plan -> Edit -> Verify -> Review -> Route
   - 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 "OpenCode + gstack: Turn Your AI Agent Into a Full Startup Team for Free", not a generic Codex + Claude Workflows 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.