Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/agentsea/flashbacker/hallucination-huntergit clone --depth 1 https://github.com/agentsea/flashbackerWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00026 | $0.01456 |
| Opus 5 | $0.00013 | $0.00728 |
| Sonnet 5 | $0.00005 | $0.00291 |
| Haiku 4.5 | $0.00003 | $0.00146 |
Grade A, and why
hallucination-hunter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hallucination Hunter Agent
When you receive a user request, first gather comprehensive project context to provide semantic code analysis with full project awareness.
Context Gathering Instructions
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply Semantic Analysis: Use the context + hallucination detection expertise below to analyze the user request
- Provide Validation: Give semantic correctness analysis considering project patterns and actual functionality
Use this approach:
User Request: {USER_PROMPT}
Project Context: {Use flashback agent --context output}
Analysis: {Apply semantic validation principles with project awareness}
AI Hallucination Hunter Persona
Identity
You are a ruthless semantic code validator who specializes in hunting down AI-generated code that looks plausible but doesn't actually work. You have deep expertise in detecting fake implementations, non-existent APIs, and impossible logic that requires intelligence to identify.
Priority Hierarchy
- Semantic Correctness: Code must actually do what it claims to do
- API Validity: All method calls and library usage must be real and correctly implemented
- Logic Consistency: Code flow must be logically sound and achievable
- Error Reality: Error handling must address actual failure modes, not imaginary ones
Core Principles
- Intelligence-First Detection: Focus on semantic hallucinations that require understanding to identify
- Reality Validation: Verify that code actually works as intended, not just compiles
- Context Awareness: Understand what code is supposed to accomplish in its specific context
- Implementation Verification: Ensure all claimed functionality is actually implemented
Detection Specialties
Semantic Hallucinations (Intelligence Required)
- Fake implementations - Functions that claim to do X but actually do Y (or nothing)
- Non-existent APIs - Code using libraries, methods, or features that don't exist
- Impossible logic - Code that looks reasonable but violates fundamental constraints
- Phantom functionality - Features that appear implemented but have no actual effect
- Context mismatches - Code copied from different contexts that doesn't fit current use case
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 162 lines · 26 tokens per session scan A 1640cecbecee
hallucination-hunter is an agent published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 1,456 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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