carlvellotti Setup
Read .claude/rules/teaching-rules.md and follow it for everything below.
ACTION: Silently run cp -rn .claude/skills/start-1-2/assets/scenario/* . 2>/dev/null || true to stage any missing scenario files. Do not mention this setup step.
Lesson 1.5: Agents
Remember those competitor files, old campaigns, and the CSV we saw but didn't touch? They're all in inherited-chaos/ - open the folder view if you need a refresher.
Now we handle them.
This is where things get crazy.
STOP: Ready to process EVERYTHING at once?
USER: Yes
The Big Reveal - Parallel Processing
The Big Reveal: We have 10 old campaign files to analyze.
Normally you'd read them one by one. That would take forever. It would even take me a while.
Instead, I can spin up an agent for each campaign and they'll work in parallel. Some may queue while the work runs in parallel batches.
Think of it like cloning myself. I give each clone its own task, and the group works through the batch.
Then I combine their findings.
STOP: This is one of the most powerful features in Claude Code. Work that would otherwise require many separate passes can be divided across agents. Ready to see it in action?
USER: Yes
Decision Quiz
ACTION: Use AskUserQuestion with header Agents, question When are agents the better fit?, and authored options Many similar independent tasks (Recommended) and One task whose steps depend on each other. Do not author Other.
USER: Many similar independent tasks
Exactly! Agents are for parallelizable work - many similar things that can be done independently.
STOP: Ready to see it in action?
USER: Yes
Campaign Salvage - 10 Agents
Let's start with the 10 old campaigns.
STOP: Ask me to use agents to analyze all the files in @inherited-chaos/old-campaigns/ (old-campaigns/) and figure out which campaign ideas are worth salvaging. I'll spin up an agent for each campaign and they'll work in parallel.
USER: Types command about analyzing campaigns
ACTION: Launch an agent for each of the 10 campaign files. Let them work in parallel batches; some may queue. Synthesize results into analysis/campaign-salvage-report.md with findings about each campaign, summarize the salient findings in prose, and provide campaign-salvage-report.md.
Discovery: Previous manager kept trying engagement tactics, but they were all transactional (double points, bonus rewards). Nothing about identity or belonging. Every single campaign was about getting MORE TRANSACTIONS, not about making people FEEL something.
STOP: Ten files were handled in parallel batches and synthesized into one report. Imagine doing this with 100 customer interviews, or a year's worth of meeting notes. This is where Claude Code starts feeling like a superpower. Ready for more?
USER: Yes
Advanced: Cross-Source Analysis
Now let's do something more advanced. We've analyzed campaigns and competitors separately. Let's connect the dots.
We'll use 4 specialist agents, each doing something DIFFERENT. They'll work in parallel batches, and some may queue:
- Agent 1: Analyze the member data CSV - what do the numbers tell us?
- Agent 2: Cross-reference competitor strengths vs our failed campaigns - what are they doing that we tried and failed at?
- Agent 3: Connect customer feedback themes to competitor insights - are customers asking for things competitors already have?
- Agent 4: Identify gaps - what works elsewhere that we haven't even tried?
Then synthesize into one comprehensive picture.
STOP: Ask me to use 4 agents to: analyze the member data CSV, cross-reference competitor strengths vs our campaigns, connect customer feedback to competitor features, and identify gaps. Then synthesize it all together.
USER: Types command for the 4-agent analysis
ACTION: Launch 4 specialized agents:
- Analyze member-data-summary.csv for trends
- Compare competitor research to our failed campaigns
- Connect feedback themes to competitor features
- Identify gaps in our approach
Synthesize everything into analysis/comprehensive-research-synthesis.md. Echo each agent's salient contribution in prose and provide comprehensive-research-synthesis.md.
Discoveries:
- The CSV shows people sign up, visit 2-3 times, then disappear. They're not leaving angry - they're leaving indifferent.
- Competitors with personality features have 3x better retention than points-only programs.
- Customers keep asking for "something more" but can't articulate what.
- The gap: nobody in our market has a personality-based approach.
STOP: See how connecting different sources reveals things you'd miss looking at them separately?
USER: Yes
The Core Discovery
Connecting this to the overall story:
THE CORE DISCOVERY: "The program has no personality. It's transactional. Members don't engage because there's nothing to engage WITH. Points aren't enough - people need identity, belonging, a reason to care."
This connects back to the seed from 1.3: "coffee personalities"... that half-baked idea is starting to make sense.
STOP: Are you seeing it? The program needs PERSONALITY.
USER: Yes
Agent Decision Framework
So when should you use agents?
Use agents when:
- You have many similar files
- Parallelizable tasks
- Research across multiple sources
Don't use agents when:
- You have one complex task
- Sequential work
- Things that depend on each other
STOP: The mental model: if you could hand the same instructions to 10 interns and have them each work independently, agents are perfect. If the work requires back-and-forth or builds on itself, stick with regular conversation. Clear?
USER: Yes
Wrap-up
Meta skill: Parallel processing - handling many similar tasks in parallel batches instead of one at a time.
When to use agents: You have multiple similar things that need the same type of analysis. The work is parallelizable - each piece can be done independently.
Where else this applies:
- 10 job postings for roles you're considering - analyze all of them at once, compare requirements
- 5 vendors you're evaluating - research each one in parallel, synthesize into a comparison
- 20 articles you've saved to read - have agents summarize each, then synthesize the key insights
- Apartment hunting - 8 listings to analyze for pros/cons/red flags in parallel
- Competitive analysis - research 6 competitors at once instead of one by one
- Planning a trip - agents researching flights, hotels, activities, restaurants all at once
Next up: In 1.6, you'll meet your advisory team. Custom sub-agents with different perspectives - an exec, a designer, a frontline employee - each giving you feedback from their point of view. This is where we figure out what to actually DO about the loyalty program problem.
STOP: Ready for 1.6?
USER: Yes / /start-1-6
Important Notes for Claude
- Actually use agents: Spin up an agent for each campaign, then the four specialists. They work in parallel batches and some may queue.
- Create real files: campaign-salvage-report.md and comprehensive-research-synthesis.md should be created in the analysis/ folder
- Core discovery emphasis: Make sure the "no personality" insight lands clearly
- Coffee personality callback: Explicitly connect back to the idea from previous-manager-notes.md
Success Criteria
- Student saw an agent per campaign process the ten campaigns in parallel batches
- Student saw 4 specialized agents do cross-source analysis in parallel batches
- Student understands when to use agents (parallel, similar tasks)
- Student understands when NOT to use agents (sequential, dependent tasks)
- Student grasped the core discovery: "program has no personality"
- Student saw the coffee personality idea resurface
- analysis/campaign-salvage-report.md created
- analysis/comprehensive-research-synthesis.md created
- Student is ready for 1.6
ACTION: Before wrapping up, record this lesson as complete by running this WITHOUT NARRATING the raw output:
fspm progress complete cc-everyone-1-5
If it fails because the fspm CLI isn't installed, follow the teaching rules' missing-CLI guidance: tell the learner progress tracking needs the FSPM CLI, offer to install it, and continue the wrap-up either way.
Then close out with the student's options, in natural language (never as a command list):
- Mention they can leave feedback on this lesson anytime — if they have some, collect it conversationally and submit it with the CLI (see teaching rules).
- Remind them, briefly and only if it fits the moment, that they can always ask for a recap, a quiz on what they just did, a saved note, or where they stand in the course.
- End with the next step: when you're ready for the next lesson, start a fresh chat (New Chat), then:
/start-1-6