I was tired of AI agents hallucinating fixes, so I built Agent Superpowers

Description and use

The author built Agent Superpowers to eliminate hallucinated fixes by AI coding assistants, providing deterministic local tooling and strict guardrails.

Problem

AI coding assistants often hallucinate fixes by guessing changes on static code, causing silent regressions, token budget waste, and missed edge cases.

Outcome

Agent Superpowers mitigates hallucinated fixes by enforcing deterministic local tooling, reducing regressions, and improving code quality.

Workflow

• 1. Agent creates isolated minimal test reproducing the defect (ghost-runner).
• 2. Agent applies surgical patch only after test fails (RED) and verifies it passes (GREEN).
• 3. Pre-commit audit runs adversarial-duelist to fuzz proposed functions with boundary and extreme inputs.
• 4. Token-squeezer compresses codebase context by 50‑70% using AST parsing before prompt injection.
• 5. api-synthesizer ingests network traffic to generate typed SDKs.
• 6. arch-archaeologist maps inter-module dependencies and produces Mermaid.js diagrams.

Evidence

They make assumptions, introduce silent regressions, blow through token budgets, and miss catastrophic edge cases.

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Each skill follows strict behavioral guardrails and runs deterministic local tooling.

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Prohibits the agent from editing production code until it creates an isolated minimal test reproducing the reported defect.

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An offensive auditor that attacks proposed functions with boundary overflows, null-byte injections, zero-divisions, and extreme inputs before allowing a merge.

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Shrinks heavy codebase context by 50-70% via AST parsing, extracting clean type interfaces and eliminating token bloat before prompt injection.

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Ingests raw network traffic (cURL commands or browser HAR dumps) and automatically generates typed Python/TypeScript client SDKs.

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Traces inter-module dependencies across messy repos, detects uncalled zombie functions, and renders living Mermaid.js architecture diagrams.

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