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

Demo

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My Tech Stack

• Hermes Agent + echo-hunt skill


• Node.js + Express


• Vanilla HTML/CSS/JS

How I Used Hermes Agent

The echo-hunt skill

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:

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  Borrowed Confidence — accepted AI output without verification


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  Shallow Resolution — fixed the error, didn't understand why


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  Pattern Blindness — repeated the same error class without noticing


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  Premature Exit — moved on before understanding was solid

The declaration layer

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.

The challenge layer

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.

What Hermes found about me

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

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  Shallow Resolution [MODERATE] — Configuration issues were handled by repeatedly replacing files rather than analyzing why the specific settings were failing


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  Borrowed Confidence [HIGH] — Acceptance of a large-scale UI rewrite immediately following a minor skill update, assuming the logic was correct without testing


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  Premature Exit [LOW] — Using a wait-time heuristic to resolve a loading screen issue instead of implementing a proper readiness check

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.

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.