| layout | default |
|---|---|
| title | Guide |
Follow these prompts in order inside Claude Code or Codex CLI. Type each prompt, wait for the AI to finish, then move to the next. The target output is a reusable vibe coding harness: instructions plus reliable executable pieces such as scripts, CLIs, MCP servers, tests, and docs.
Prompts you need to type are marked with ▶ Prompt.
Prerequisites: Install your AI coding tool. See the Setup Guide if needed.
Then choose your model by typing /model:
- Claude Code: Opus 4.6 (high effort)
- Codex CLI: GPT-5.4 (xhigh effort)
Replace <describe your project idea here> with what you want to build. For example:
"Brainstorm with me: Build a symbolic engine for quantum symbolic simplification with rust. use superpower skill"
Optionally, point the AI to specific projects to learn from:
Choose the subagent-driven approach when prompted.
After initial code implementation, generate a project config:
This creates a CLAUDE.md or AGENTS.md that helps the AI understand your project in future sessions.
A harness is the reusable system around a workflow. It can include a skill file that teaches the AI how to reason, scripts or CLIs for deterministic operations, MCP servers for external systems, and tests that keep the workflow reliable. See the Complete Guide to Building Skills for Claude for the skill layer background.
Same as Part A — install superpowers and gh if not already done.
For example: "I want to automate literature survey from a list of arxiv links. Should this be a skill, a CLI/MCP tool, or a skill + tool harness?"
Use this when the harness needs a reusable instruction layer. If the workflow also needs deterministic execution, ask the agent to design the CLI, MCP server, Makefile target, or script interface before coding it.
This simulates a user interacting with your skill layer and produces a test report. Also run the normal software tests for any scripts, CLIs, MCP servers, or libraries included in the harness.
Copy-paste any of these at any time:
Questions worth asking the AI to deepen your project:
- What is Mathematica's design for symbolic simplification? What can we learn from it?
- Is egraph useful for symbolic simplification?
- If my goal is LLM-driven theorem proving, brainstorm with me, what is the best strategy to build a package?
Useful references:
- Makefile example — automate your build
- Agentic tests example — test your harness with AI agents
- mdBook — documentation tool for Rust projects
- D3.js — interactive visualizations for documentation
Add this to your Makefile to let Claude execute a plan file autonomously:
run-plan:
@NL=$$'\n'; \
BRANCH=$$(git branch --show-current); \
PLAN_FILE="$(PLAN_FILE)"; \
if [ "$(AGENT_TYPE)" = "claude" ]; then \
PROCESS="1. Read the plan file$${NL}2. Execute the plan — it specifies which skill(s) to use$${NL}3. Push: git push origin $$BRANCH$${NL}4. If a PR already exists for this branch, skip. Otherwise create one."; \
else \
PROCESS="1. Read the plan file$${NL}2. Execute the tasks step by step. For each task, implement and test before moving on.$${NL}3. Push: git push origin $$BRANCH$${NL}4. If a PR already exists for this branch, skip. Otherwise create one."; \
fi; \
PROMPT="Execute the plan in '$$PLAN_FILE'."; \
if [ -n "$(INSTRUCTIONS)" ]; then \
PROMPT="$${PROMPT}$${NL}$${NL}## Additional Instructions$${NL}$(INSTRUCTIONS)"; \
fi; \
PROMPT="$${PROMPT}$${NL}$${NL}## Process$${NL}$${PROCESS}$${NL}$${NL}## Rules$${NL}- Tests should be strong enough to catch regressions.$${NL}- Do not modify tests to make them pass.$${NL}- Test failure must be reported."; \
echo "=== Prompt ===" && echo "$$PROMPT" && echo "===" ; \
claude --dangerously-skip-permissions \
--model opus \
--verbose \
--max-turns 500 \
-p "$$PROMPT" 2>&1 | tee "$(OUTPUT)"