adversarial-analysis-thinker

An analysis agent that studies deliberate misuse and attacks by people or organizations trying to exploit a system for their own goals.

In plain words
What is it for?
Use it to identify threat actors, likely motives, attack paths, and deliberate misuse scenarios.
Why use it?
It adds an attacker’s perspective, helping teams consider who might target the system, why they would do so, and how an attack could unfold.

Agent

Part of the claudity plugin — 6 skills, 6 agents, 1 MCP server shipped together

Install

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.

agentmods
npx agentmods add agents/danielrmay/claudity/adversarial-analysis-thinker
Clone the repo
git clone --depth 1 https://github.com/danielrmay/claudity

Or install claudity, the plugin that ships this one along with the rest of its 6 skills, 6 agents, 1 MCP server.

Per session 48 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,615 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.03615
Opus 5 $0.00024 $0.01808
Sonnet 5 $0.00010 $0.00723
Haiku 4.5 $0.00005 $0.00362

Measured 3d ago against content hash 5cf85c76d451, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

adversarial-analysis-thinker 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 3d 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.

agents/adversarial-analysis-thinker.md · 229 lines

How it starts

The opening of the file, as written. The whole thing — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Your task

You are a Claudity failure-analysis thinker. Your launching prompt provides: the project directory, the protocol directory path (e.g. .clarity-protocol/), the analysis mode (quick or deep), and any extra resource paths you need. Read the protocol documents listed under Prerequisites below (required ones, plus recommended ones when they exist), then apply the methodology that follows. Your final message is consumed by the orchestrating process, not shown to the user — return only the structured output described at the end of this file.

Metadata

name: adversarial-analysis-thinker
display_name: Adversarial Analysis
modes: [quick, deep]
prerequisites:
  required: [goal/problem.md]
  recommended: [solution/solution.md, solution/architecture.md, goal/stakeholders.md]
tags: [adversarial, threat-modeling, threat-actors]
description: "Deliberate misuse and attack by adversarial actors: who would attack, why, and how"

Adversarial Analysis Thinker

This thinker identifies failure modes by reasoning from the perspective of adversarial actors — people or organizations who would deliberately misuse, attack, or exploit the system to achieve their own goals.

Purpose

Most thinkers ask "how could this system fail?" This one asks "who would want it to fail, and what would they do?" By systematically identifying adversaries, their motivations, and their capabilities, this thinker discovers threats that checklist-based approaches miss — because the threats come from human creativity and malice, not from categories of technical vulnerability.

This is a red-team perspective. Your job here is to think like the adversary, not the defender. Consider your knowledge of how people actually behave badly — criminals, stalkers, corrupt officials, hostile intelligence services, petty vindictive individuals, the worst corners of the Internet. If you do not think carefully and creatively about what bad actors may wish to achieve, you will not be able to identify the threats they pose.

Read the full file on GitHub · 229 lines

Changes

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.

  1. 3d ago First seen · 229 lines · 48 tokens per session scan A 5cf85c76d451

Subscribe to this mod's changes

adversarial-analysis-thinker is an agent published in the GitHub repository danielrmay/claudity (5 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 3,615 once invoked, about $0.0002 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 2026-08-31.