claude-code-complete-agent/examples/10-full-pipeline/prompt.md

Example 10: Full Pipeline

Capability: A complete workflow combining web search, multi-agent orchestration, file I/O, memory, hooks, and logging in a single Claude Code run.

OpenClaw equivalent: End-to-end agent pipeline with messaging, skills, and hooks.

Building on Examples 01-09. This is the culmination. Every capability you explored individually now runs as a single, automated pipeline. If you followed the Cumulative Path, you already built the pieces. This example connects them.

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What This Demonstrates

Every capability from examples 01-09 working together:

Step	Capability	Example
1	Web search	03-web-search
2	Researcher agent	06-multi-agent
3	Writer agent	06-multi-agent
4	Reviewer agent	06-multi-agent
5	File I/O	02-shell-and-files
6	Memory update	05-memory-system
7	Security logging	09-security-hooks
8	Agent runtime loop	01-agent-runtime

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The Prompt

Run a full research-to-output pipeline on the topic: "How Claude Code handles
permission modes: plan, autoEdit, and bypassPermissions"

Pipeline steps:
1. Use the researcher agent to gather information from the web and official docs
2. Pass the research to the writer agent to draft a 400-word explainer
3. Send the draft to the reviewer agent for accuracy and clarity feedback
4. Incorporate the reviewer's feedback into a final version
5. Save the final version to pipeline-output/permission-modes.md
6. Append a pipeline execution summary to memory/pipeline-log.md with:
   - Date and time
   - Topic researched
   - Word count of final output
   - Any issues encountered
7. Show me the first 10 lines of the output file to confirm everything worked

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What Happens

Claude Code will coordinate the full pipeline autonomously:

• The researcher agent uses WebSearch and WebFetch
• The writer agent produces structured prose
• The reviewer agent critiques and returns specific feedback
• A revision loop runs if needed (bounded by maxTurns in settings.json)
• File writes are intercepted by PostToolUse for audit logging
• Memory is updated so the next session knows this pipeline ran

You will see each agent invocation streamed in sequence. The entire pipeline typically completes in 2-4 minutes depending on web fetch latency.

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Expected Output

The pipeline takes 2-4 minutes. You will see agent invocations streaming:

1. Researcher agent runs WebSearch and WebFetch (30-60 seconds)
2. Writer agent produces a ~400-word draft (15-30 seconds)
3. Reviewer agent critiques the draft (15-30 seconds)
4. Revision if needed (15-30 seconds)
5. File writes to pipeline-output/ and memory/

When complete, two new files exist:

pipeline-output/permission-modes.md (the article):

# How Claude Code Handles Permission Modes

Claude Code provides three permission modes that control how much
autonomy the agent has...
[~400 words with accurate technical content]

memory/pipeline-log.md (the execution log):

## Pipeline Run - March 26, 2026

- **Topic:** How Claude Code handles permission modes
- **Word count:** 412
- **Issues:** None
- **Duration:** ~3 minutes

How you know it worked:

• Both files exist in the expected directories
• The article is accurate (check against security/permission-modes-explained.md)
• The pipeline log has today's date and a word count
• You saw three distinct agent invocations in the terminal

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Why This Matters

This is what Claude Code looks like as an actual agent platform, not a chat assistant. The same architecture, with different prompts and agents, runs the article production pipeline at fromaitochitta.com.

The companion repo you are reading is the minimal version of that setup. Clone it, open Claude Code, and run this prompt to see the full stack work.

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Carry Forward

You now have a working end-to-end pipeline. From here:

• Examples 11-13 add advanced capabilities (desktop control, remote access, autonomous mode) that can extend this pipeline
• Example 14 takes everything you learned and builds your own personalized agent

The pipeline pattern, research + draft + review + save + notify, works for any domain. Change the topic, swap the agents, adjust the output format. The architecture stays the same.

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The Cumulative Path

If you followed the Cumulative Path through Examples 01-09, you already have most of the pipeline output. This prompt runs the complete flow from scratch on a new topic, proving the pipeline works end-to-end.

Run a full research-to-output pipeline on the topic: "What can Claude Code
do that I did not know about before running these examples?"

Pipeline steps:
1. Read memory/research-state.md to understand what has been explored
2. Use the researcher agent to search for capabilities not yet covered
3. Use the writer agent to draft a 400-word personal discovery summary
4. Use the reviewer agent to verify all claims
5. Save to pipeline-output/personal-discoveries.md
6. Append execution log to memory/pipeline-log.md
7. Show me the first 10 lines of the output file

This is a genuinely useful output: a personalized summary of what you learned, produced by the pipeline you just built.

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Now Try It Yourself

Replace the topic with something your pipeline should produce regularly:

Run a full research-to-output pipeline on: "[your recurring research topic]"

Pipeline steps:
1. [your research source: web, files, APIs]
2. Use the researcher agent to [gather what you need]
3. Use the writer agent to draft a [format] for [audience]
4. Use the reviewer agent to check [what matters most]
5. Save to pipeline-output/[your-output].md
6. Log to memory/pipeline-log.md
7. Show me the first 10 lines

The pattern you just learned: the full pipeline is a recipe with interchangeable ingredients. Swap the research topic, change the output format, adjust the audience. The agent orchestration, file I/O, memory, and logging stay the same.

Ideas worth trying:

• Weekly industry briefing for your team
• Automated due diligence report for vendors or partners
• Content pipeline that drafts, reviews, and delivers blog posts