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I Built a Cognitive Threat Hunter on Hermes Agent — It Analyzed the Session Where I Built It and Found Three Blind Spots

A developer built ECHO Hunt, a cognitive threat hunter for vibe coding sessions that runs on Hermes Agent. The tool analyzes session logs to identify blind spots and cognitive TTPs, requiring users to declare their own blind spots before the AI hunts for evidence. When the developer ran ECHO Hunt on the session where they built it, the tool found three blind spots, including a lack of diagnostic precision in the developer's own debugging process.

read2 min publishedMay 31, 2026

This is a submission for the Hermes Agent Challenge: Build With Hermes Agent

ECHO Hunt — a cognitive threat hunter for vibe coding sessions built on Hermes Agent.

Vibe coding is how most people build with AI today. You describe what you want, the AI generates it, you run it, fix errors, iterate. It works. But did you actually learn anything, or did the AI just carry you through it?

ECHO Hunt finds out. Paste your session log, declare your blind spots before the evidence arrives, then face what Hermes actually found.

It's not a report generator. It's an investigation you participate in.

🔗 github.com/FlowArchitect895/echo-hunt Hermes Agent runs a custom skill called echo-hunt

. It takes a vibe coding session log and performs a cognitive forensic hunt — forming hypotheses before analyzing anything, hunting each one against the evidence, and mapping findings to four cognitive TTPs:

ECHO Hunt calls hermes -z

with the echo-hunt skill prompt. One call. Hermes pre-computes the entire investigation — hypotheses, findings, TTP classifications, attribution challenges with locked correct answers and plausible distractors. Zero API calls during gameplay. Everything runs on cached data.

Before Hermes hunts, you declare your blind spots. Three questions. You commit to answers before the evidence arrives. This is the adversarial layer — you vs your own perception of what happened.

Your declarations face what Hermes found. Three outcomes:

Each confirmed finding becomes a TTP attribution challenge. 4 options, 20-second timer. Wrong answer drops integrity 10%. Correct answer earns points. The timer is the pressure — forensic decisions don't wait.

The Evidence Integrity score is computed from actual player behavior — signals vs ghosts, correct vs wrong TTP attributions. Hermes doesn't generate the number. You produce it.

I ran ECHO Hunt on the session where I built ECHO Hunt. Here's what it found:

The confirmed finding that hit hardest: "The sequence of 'still not working' → 'try changing format' → 'config is getting corrupted' → 'paste in a clean config' shows a lack of diagnostic precision."

That's not a generated critique. That's evidence from my own session, hunted by the tool I was building while I was building it.

The downloadable Cognitive Threat Report captures everything — pre-hunt declarations, hunt hypotheses, confirmed findings, TTPs with severity, genuine understanding moments, and next session focus. It's a real document, not a game summary.

What makes it different from a standard AI analysis: the pre-hunt declarations are locked in before Hermes runs. So the report shows not just what happened in the session, but the gap between what you thought happened and what the evidence shows. That delta is the most useful thing in it.

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