Codex + Claude Workflows / Foundation

OpenCode Full Tutorial: Free Models, Skills & MCPs

A full OpenCode tutorial covering installation, connecting any model (free tiers, subscriptions like OpenAI/ChatGPT, API keys, or local), and using Claude Code-style features inside it: plan/build mode, multiple sessions, session sharing/export/timeline, skills from skills.sh, MCPs, and an agents.md file, ending with a personal 'second brain' board-of-advisors workflow.

Eric Tech23 minTranscript found

Quick learning frame

Read this before watching.

Coding-agent workflow is the loop of inspect, plan, edit, verify, summarize, and route the next task to the right tool.

New playlist item from Eric Tech; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Skill you build: The ability to set up OpenCode as a single, provider-agnostic coding agent and drive it with sessions, plan/build modes, skills, MCPs, and an agents.md so you are not locked into one model or vendor.

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.

01Inspect
02Plan
03Edit
04Verify
05Review
06Route

Deep lesson

Turn this video into working knowledge.

5,505 cleaned transcript words reviewed across 1,450 timed caption segments.

Thesis

OpenCode Full Tutorial: Free Models, Skills & MCPs teaches a practical codex + claude workflows move: A full OpenCode tutorial covering installation, connecting any model (free tiers, subscriptions like OpenAI/ChatGPT, API keys, or local), and using Claude Code-style features inside it: plan/build mode, multiple sessions, session sharing/export/timeline, skills from skills.sh, MCPs, and an agents.md file, ending with a personal 'second brain' board-of-advisors workflow.

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.

0:09

One agent, any model

“tool called open code which should solve exactly this problem. And with just only one AI agents, you can now be able to use any models you want with your existing subscriptions or with the free models or...”

OpenCode installs via a single curl command and connects to models through /connect, offering OpenCode Zen (pay-as-you-go API keys), OpenCode Go (~$5-10/mo subscription to top Chinese models like K2 and GLM 5.2), free endpoints like a DeepSeek flash-free model, or your own ChatGPT/OpenAI subscription via OAuth. Install OpenCode, run /connect, and authenticate at least one free model plus one subscription or API-key model so you can switch providers on demand.

6:05

Sessions and modes

“full AI builder road map where you're going to start with AI automations. Then later on moving into AI agents, research systems, SAS buildings, and eventually how to productize and market your AI skills. You're going to get...”

You can run multiple parallel sessions and switch with /sessions or start /new, change reasoning effort with /variance, share a conversation as an HTML link (or /unshare), /export it to Markdown, /timeline to roll back or fork from an earlier message, and toggle plan vs build mode with shift+tab so the agent plans and asks clarifying questions before executing. Start two OpenCode sessions on different tasks, toggle one into plan mode with shift+tab, and practice approving a plan then switching to build mode to execute.

17:03

Skills, MCP, agents.md

“sup power inside of open code here right off the bat. Now lastly, what I want to cover here is also the ability here to initialize your agents.mdv. That's kind of like your system prompt for your entire...”

Skills (workflows/SOPs that harness the agent) install from skills.sh by pasting a repo prompt into OpenCode's agents folder and require restarting the session to load; /init generates a concise agents.md system-prompt file capturing repo rules, folder map, and skill routing, which he uses to run an 'ask the board' skill of scraped advisor personas as a personal second brain. Install one skill from skills.sh into a project, restart OpenCode, run /init to generate an agents.md, then add one custom rule and confirm the agent obeys it in a new session.

01

Inspect

Start with this video's job: A full OpenCode tutorial covering installation, connecting any model (free tiers, subscriptions like OpenAI/ChatGPT, API keys, or local), and using Claude Code-style features inside it: plan/build mode, multiple sessions, session sharing/export/timeline, skills from skills.sh, MCPs, and an agents.md file, ending with a personal 'second brain' board-of-advisors workflow. Treat "Inspect" as the outcome you are trying to make visible, not a topic label. Anchor it to 0:09, where the video says: “tool called open code which should solve exactly this problem. And with just only one AI agents, you can now be able to use any models you want with your existing subscriptions or with the free models or...”

02

Plan

Use "Plan" to locate the part of the codex + claude workflows workflow the video is demonstrating. Ask what changes in your real setup if this claim is true. Anchor it to 6:05, where the video says: “full AI builder road map where you're going to start with AI automations. Then later on moving into AI agents, research systems, SAS buildings, and eventually how to productize and market your AI skills. You're going to get...”

03

Edit

Turn "Edit" into the reusable artifact for this lesson: A routing matrix for when to use Codex, Claude, browser checks, or manual review. This is where watching becomes something you can inspect and reuse.

04

Verify

Use "Verify" 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

Review

Use "Review" 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

Route

Use "Route" 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 routing matrix for when to use codex, claude, browser checks, or manual review..

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.

Transcript-derived moments

Use timestamps to study the actual video.

Quality check

Do not count this as learned until these are true.

01

State the transcript-backed claim in your own words: A full OpenCode tutorial covering installation, connecting any model (free tiers, subscriptions like OpenAI/ChatGPT, API keys, or local), and using Claude Code-style features inside it: plan/build mode, multiple sessions, session sharing/export/timeline, skills from skills.sh, MCPs, and an agents.md file, ending with a personal 'second brain' board-of-advisors workflow.

02

Explain the practical stakes without hype: New playlist item from Eric Tech; queued for transcript-backed review, topic mapping, and a practical learning artifact.

03

Map the idea onto the Inspect -> Plan -> Edit -> Verify -> Review -> Route sequence and name the weakest link.

04

Produce the artifact and include the evidence that proves it: A routing matrix for when to use Codex, Claude, browser checks, or manual review.

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: OpenCode Full Tutorial: Free Models, Skills & MCPs
- URL: https://www.youtube.com/watch?v=0xKE1UHpSfk
- Topic: Codex + Claude Workflows
- My current learning frame: In a real project, install OpenCode, connect a free and a subscription model, run /init to build an agents.md, install one skill from skills.sh, and use plan mode plus a shared session link to scaffold and refactor a small app end to end.
- Why this matters: New playlist item from Eric Tech; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:09 / Evidence 1: "tool called open code which should solve exactly this problem. And with just only one AI agents, you can now be able to use any models you want with your existing subscriptions or with the free models or..."
- 2:29 / Evidence 2: "the terminal session and now I'm already inside of that project. So if I were to do Ctrl C, you can see I'm in the app directory, right? So now if I were to type in like for..."
- 4:31 / Evidence 3: "flash free. So if you want to use the free model simply just going to click on enter and now you have access to deepcv4 flash free from the open code zen and if I were to say..."
- 6:05 / Evidence 4: "full AI builder road map where you're going to start with AI automations. Then later on moving into AI agents, research systems, SAS buildings, and eventually how to productize and market your AI skills. You're going to get..."
- 14:11 / Evidence 5: "have our coding agent here now switch from plan mode to execution mode. Okay, so finally what I want to talk about is how you can be able to integrate your skills MCPS into your open code. So..."
- 17:03 / Evidence 6: "sup power inside of open code here right off the bat. Now lastly, what I want to cover here is also the ability here to initialize your agents.mdv. That's kind of like your system prompt for your entire..."
- 19:39 / Evidence 7: "should respond with emojis because I have already mentioned this right inside of the agents file. So here you can see it has mentioned with emojis, which is pretty good, right? So that's exactly how you can be..."

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 Full Tutorial: Free Models, Skills & MCPs", 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.

Misconceptions

What to stop believing.

One agent should do every task.

Different tools have different strengths. Routing is part of the workflow.

More context is always better.

Relevant context helps; stale context causes drift and cost.

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 routing matrix for when to use codex, claude, browser checks, or manual review..

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.

What problem with other coding agents does OpenCode aim to solve?

How do you switch OpenCode into plan mode, and what does it do there?

What command generates an agents.md file, and what does that file capture?

Source shelf

Use the video as a doorway, then verify with primary sources.

ReadingOpenAI Codexopenai.com/codex/ReadingClaude Code Overviewdocs.anthropic.com/en/docs/claude-code/overview