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Most developers use the same workflow for every AI task.
That is the mistake.
Different work requires drastically different AI behavior. Wrong mode = wrong outcomes.
□ AI created 12 files for a button component
□ AI added 4 packages to format a date
□ Context window exploded mid-feature
□ API bill was ridiculous for a session that shipped nothing
□ Day 2 AI forgot everything decided on Day 1
□ The code works but nobody understands it
| Mode | Goal | Accepts | Refuses |
|---|---|---|---|
| ⚡ Economy | Minimize cost | Simpler solutions | Large context scans |
| 🚀 Builder | Ship fast | Some technical debt | Premature abstractions |
| 🛠 Maintainer | Preserve stability | Incremental improvements | Refactors & new dependencies |
| 🏛 Architect | Make decisions | Planning & tradeoffs | Production code generation |
Debugging in Builder Mode creates unreviewed refactors.
Designing in Maintainer Mode creates over-engineered fixes.
Investigating in Economy Mode creates context collapse.
Coding in Architect Mode creates premature implementations.
Using the wrong mode creates:
- technical debt
- token waste
- regressions
- unnecessary complexity
Healthy AI-assisted development involves switching modes as work evolves.
🏛 Architect
↓ (Decision approved)
🚀 Builder
↓ (Feature complete)
🛠 Maintainer
(Production ready)
Strict behavioral rules don't just prevent bugs—they reduce code bloat. In a recent benchmark against a qwen3:8b local model evaluating a standard coding task (Node.js filesystem ops):
- Baseline (Raw Model): 31 Lines of Code
- Ponytail Base Prompt: 21 Lines of Code
- Ponytail + Vibe Coding Essentials: 17 Lines of Code
Injecting our anti-hallucination guardrails shaved an additional 19% off the code length compared to the base "lazy dev" prompt by forcing the model to adhere strictly to native APIs instead of inventing unnecessary abstractions.
SKILL.md: The 11 core meta-skills required to manage AI agents effectively.GOLDEN_RULES.md: 25 rules for sustainable AI-assisted development.LESSONS_LEARNED.md: 10 real-world vibe coding failures.FAILURE_PATTERNS.md: 10 common AI workflow failures and how to recover from them.COST_OF_BAD_VIBE_CODING.md: The actual cost of bad AI sessions in time and tokens.SESSION_RECOVERY.md: 5 tools for transferring context between sessions.anti-hallucination/: Framework-specific guardrails.prompts/: Standardized templates enforcing scope and output formats.templates/: Ready-to-use configuration files for Cursor, Windsurf, Copilot, and CLI tools.
1. Install the runtime
Copy AGENTS.md to your project root. (Tool-specific versions are available in templates/).
2. Set your context
Update the 3-line MVP Context block at the top of AGENTS.md daily.
3. Command the AI
Builder is the default behavior. To change how the AI operates for a specific task, start your prompt with a mode tag:
"[ECONOMY] Why is the
validateTokenfunction throwing a TypeError?""[MAINTAINER] We have a regression in the billing calculator. Write a failing test first."
"[ARCHITECT] Let's design the database schema for the new auth flow."
4. Utilize the Framework The files in this repository are designed for distinct parts of your workflow:
anti-hallucination/: Find your framework, copy the rules, and paste them into yourAGENTS.mdso the AI stops using deprecated APIs.prompts/: When the AI starts stuck in a correction loop, stop. Open the relevant template here to hard-reset the context.SKILL.md&GOLDEN_RULES.md: Read these once. Treat them as the Clean Code of the AI era. They teach the meta-skills of driving an agent.LESSONS_LEARNED.md&production/hardening.md: Review these before shipping. They document the catastrophic AI failures you must manually check for.
The model generates.
The developer decides.
You own the workflow.