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 skills/trilwu/secskills/recognizing-deceptionnpx skills add trilwu/secskills --skill recognizing-deceptiongit clone --depth 1 https://github.com/trilwu/secskillsWrote 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/skills/trilwu/secskills/recognizing-deception)<a href="https://agentmods.dev/skills/trilwu/secskills/recognizing-deception"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/recognizing-deception.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 5.1 | $0.00107 | $0.02337 |
| Opus 5 | $0.00053 | $0.01169 |
| Sonnet 5 | $0.00021 | $0.00467 |
| Haiku 4.5 | $0.00011 | $0.00234 |
Grade A, and why
recognizing-deception scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sL "https://defuddle.md/<url>" # scheme in the path is optional How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recognizing Deception
Every other skill in this collection assumes the environment is telling you the truth. Deception technology exists specifically to break that assumption, and it is the failure mode an automated or semi-automated tester is least equipped to catch.
The distinction that matters: careful evidence-handling protects you against conclusions you invented. It does nothing against a false belief the environment deliberately planted. A honeypot presenting a convincingly vulnerable service produces real banners, real responses, and real artifacts. Every verification step you would normally run confirms it, because the evidence is genuine — it was manufactured to be.
Assume competent defenders have planted something. Your job is to notice before you touch it, because most deception fires on first use, and first use cannot be undone.
When to Use
- Anything is markedly easier than the rest of the environment
- Credentials turn up somewhere convenient — a share, a wiki, a config, a pastebin-shaped file
- A privileged account exists with a SPN, a weak password, and no logon history
- A service answers with a vulnerable banner but behaves oddly under real use
- You are about to use credentials whose provenance you cannot state
- A file, bucket, or database is named to attract attention
(
passwords.xlsx,backup-prod,domain_admins.txt) - Before authenticating with anything recovered from an unexpected location
When NOT to Use
- Evading detection generally — that is engagement OPSEC, not deception recognition; a canary is not something you evade, it is something you avoid triggering
- Analysing an adversary's own decoys during an incident — use
responding-to-incidentsandproducing-threat-intelligence - Building a deception capability — this skill is about encountering deception, not deploying it
- A finding that is merely surprising — real environments contain real misconfigurations; see the base-rate discussion below before crying honeypot
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 · 211 lines · 107 tokens per session scan A c180d43c53cd
recognizing-deception is a skill published in the GitHub repository trilwu/secskills (134 stars, last pushed yesterday), licensed MIT. It adds 107 tokens to every session and 2,337 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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