Kjell Tore Guttormsen 06ae605051 fix: pedagogical review - add expected output, CLAUDE.md, fix consistency

Address findings from pedagogical review simulating a non-expert user:

- Add CLAUDE.md to project root (was referenced but missing)
- Fix README score from 12/9/1 to 13/8/1 (match feature-map.md)
- Add Expected Output sections to examples 01, 02, 05, 09, 10
- Create pipeline-output/ and briefings/ directories
- Add example ordering guidance in README
- Add plan requirements for examples 11/13 in prerequisites
- Add skill frontmatter explanation in GETTING-STARTED.md
- Explain Cowork/Dispatch with links in cowork-integration
- Expand .gitignore with node_modules and generated output files
- Add model override hints in agent frontmatter comments

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-26 20:25:45 +01:00

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Example 01: Agent Runtime

Capability: Claude Code executes tools autonomously, streams output as it works, and loops until the task is complete. No user input needed between steps.

OpenClaw equivalent: Long-running daemon with tool execution and streaming output.

The Prompt

Research the top 3 AI frameworks released this month, compare their GitHub stars,
and write a summary to research-output.md

For each framework include:
- Name and release date
- GitHub URL and current star count
- One sentence on what problem it solves
- Your verdict on whether it's worth watching

End the file with a "Verdict" section that ranks all three.

What Happens

Claude Code will:

Use WebSearch to find AI framework releases from the current month
Use WebFetch to retrieve GitHub pages and extract star counts
Loop through each framework, gathering data tool call by tool call
Use Write to create research-output.md with the structured summary
Report completion with a summary of what was written

You will see each tool call streamed as it happens. Claude does not ask for confirmation between steps unless it hits something ambiguous.

Expected Output

After 30-60 seconds, you should see a new file research-output.md in the project root. It will look something like this (content varies by month):

# AI Frameworks Released This Month

## 1. ExampleFramework
- **Released:** March 12, 2026
- **GitHub:** https://github.com/example/framework (4,200 stars)
- **What it solves:** Simplifies multi-agent orchestration for Python developers.
- **Verdict:** Worth watching. Growing fast with strong community momentum.

## 2. ...

## Verdict
1. ExampleFramework - most practical for production use
2. ...

How you know it worked:

A file called research-output.md exists in the project root
It contains 3 frameworks with star counts and URLs
It ends with a ranked Verdict section
You saw WebSearch and WebFetch tool calls streaming in the terminal

Why This Matters

This is the agent loop in action: plan, execute, observe, repeat. The same loop that runs a 300-step pipeline also runs this 5-step research task. The difference is only scale.

Claude Code v2.1.84 added adaptive thinking, which adjusts reasoning depth automatically. Complex sub-tasks get more thought; simple ones proceed fast.

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