2491f5c732
40 files demonstrating every major OpenClaw capability using Claude Code: - 3 agents (researcher, writer, reviewer) - 3 skills (daily-briefing, slack-message, web-research) - 2 security hooks (pre-tool-use blocker, post-tool-use logger) - 10 self-contained examples with copy-paste prompts - Complete feature map (20 capabilities, 11 full match, 7 different, 2 gap) - Security docs including NemoClaw comparison - Automation, messaging, browser, memory documentation Zero dependencies. Clone and run. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
49 lines
1.5 KiB
Markdown
49 lines
1.5 KiB
Markdown
# Example 01: Agent Runtime
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**Capability:** Claude Code executes tools autonomously, streams output as it works,
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and loops until the task is complete. No user input needed between steps.
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**OpenClaw equivalent:** Long-running daemon with tool execution and streaming output.
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---
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## The Prompt
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```
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Research the top 3 AI frameworks released this month, compare their GitHub stars,
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and write a summary to research-output.md
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For each framework include:
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- Name and release date
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- GitHub URL and current star count
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- One sentence on what problem it solves
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- Your verdict on whether it's worth watching
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End the file with a "Verdict" section that ranks all three.
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```
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---
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## What Happens
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Claude Code will:
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1. Use WebSearch to find AI framework releases from the current month
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2. Use WebFetch to retrieve GitHub pages and extract star counts
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3. Loop through each framework, gathering data tool call by tool call
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4. Use Write to create `research-output.md` with the structured summary
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5. Report completion with a summary of what was written
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You will see each tool call streamed as it happens. Claude does not ask for
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confirmation between steps unless it hits something ambiguous.
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---
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## Why This Matters
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This is the agent loop in action: plan, execute, observe, repeat. The same
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loop that runs a 300-step pipeline also runs this 5-step research task.
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The difference is only scale.
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Claude Code v2.1.84 added adaptive thinking, which adjusts reasoning depth
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automatically. Complex sub-tasks get more thought; simple ones proceed fast.
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