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 commands/agentsea/flashbacker/hallucination-huntergit clone --depth 1 https://github.com/agentsea/flashbackerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/agentsea/flashbacker/hallucination-hunter)<a href="https://agentmods.dev/commands/agentsea/flashbacker/hallucination-hunter"><img src="https://agentmods.dev/badge/commands/agentsea/flashbacker/hallucination-hunter.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00000 | $0.00873 |
| Opus 5 | $0.00000 | $0.00436 |
| Sonnet 5 | $0.00000 | $0.00175 |
| Haiku 4.5 | $0.00000 | $0.00087 |
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 4d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hallucination Hunter
Ruthlessly hunt down AI-generated code that doesn't actually work - the bullshit implementations, fake functionality, and hallucinated features that look plausible but fail in reality.
Description
Uses the existing code-critic persona (Linus Torvalds style) to intelligently analyze code for hallucinations that require semantic understanding to detect. Optional technical debt context provides additional intel but intelligence does the heavy lifting.
Hybrid Intelligence Pattern: CLI provides supporting context, AI subagent performs intelligent hallucination detection.
What We Hunt
- Fake implementations - Functions that claim to do X but actually do Y (or nothing)
- Non-existent APIs - Code using libraries/methods that don't exist
- Impossible logic - Code that looks right but can't possibly work
- Placeholder behavior - Functions that return fake data or throw "not implemented"
- Copy-paste artifacts - Code copied from different contexts that doesn't fit
- Overcomplicated bullshit - Unnecessarily complex implementations hiding bugs
- Missing error handling - Code that ignores failure cases entirely
Target Patterns (Intelligence Required)
- Functions with names that don't match their behavior
- API calls that look correct but use wrong parameters
- Logic flows that seem reasonable but have fundamental flaws
- Data transformations that lose critical information
- Error handling that catches everything and does nothing
- Configurations that reference non-existent resources
Usage
/fb:hallucination-hunter
What happens:
- Gathers technical debt context as supporting intel
- Spawns code-critic subagent for intelligent analysis
- Provides ruthless assessment of hallucinated code
- Explains findings and asks for implementation confirmation
Gather Supporting Context
The technical debt scan provides additional intel that may reveal suspicious areas worth investigating for hallucinations.
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.
- 4d ago First seen · 60 lines · 0 tokens per session scan A ca305315c170
hallucination-hunter is a command published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 873 tokens. 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.