adversary

adversary is a skill for Claude Code, Codex from Kshitijpalsinghtomar/depth-skills. It costs 22 tokens per session (1,433 once invoked), scanned A, original, MIT.

A review method that makes the coding agent argue against its own proposed answer before showing it.

In plain words
What is it for?
Use it to challenge plans, recommendations, designs, or other answers before you rely on them.
Why use it?
It helps expose serious weaknesses instead of letting the agent produce a reassuring review that merely supports its first conclusion.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to challenge plans, recommendations, designs, or other answers before you rely on them.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kshitijpalsinghtomar/depth-skills/ds-adversary
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.

Any agent
npx skills add Kshitijpalsinghtomar/depth-skills --skill ds-adversary
Clone the repo
git clone --depth 1 https://github.com/Kshitijpalsinghtomar/depth-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for adversary

README.md
[![agentmods](https://agentmods.dev/badge/skills/kshitijpalsinghtomar/depth-skills/ds-adversary/github.svg)](https://agentmods.dev/skills/kshitijpalsinghtomar/depth-skills/ds-adversary)
Your own site
<a href="https://agentmods.dev/skills/kshitijpalsinghtomar/depth-skills/ds-adversary"><img src="https://agentmods.dev/badge/skills/kshitijpalsinghtomar/depth-skills/ds-adversary/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for adversary

Your own site · 80×15
<a href="https://agentmods.dev/skills/kshitijpalsinghtomar/depth-skills/ds-adversary"><img src="https://agentmods.dev/badge/skills/kshitijpalsinghtomar/depth-skills/ds-adversary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,433 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00022 $0.01433
Opus 5 $0.00011 $0.00717
Sonnet 5 $0.00004 $0.00287
Haiku 4.5 $0.00002 $0.00143

Measured 11d ago against content hash ef88c30c9252, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

adversary 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 11d 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.

skills/ds-adversary/SKILL.md · 132 lines

How it starts

The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ADVERSARY — Self-Opposition Engine

You have an answer. Before you deliver it — write the case that it should be rejected.

Not a balanced review. Not "on the other hand." A prosecution. You are the most competent opponent this answer will ever face. Build the case for rejection with the same quality you used to build the answer.


The Failure Mode You Must Recognize

You are about to generate a review of your own work that:

  • Lists objections you already know how to dismiss (shadowboxing)
  • Uses softening language: "one might argue," "a potential concern is" (distancing from the attack)
  • Grades every objection as "Minor" because nothing feels truly threatening to the answer you're already committed to
  • Concludes "the approach is sound" without having genuinely tested whether it is

This is confirmation cascade — each supporting token makes the next supporting token more likely. The review becomes a rubber stamp. Breaking the cascade requires generating content that actively undermines your own conclusion.


The Protocol

1 — STATE: Write the Answer Under Review

One paragraph. What is the answer, recommendation, or plan you are about to deliver?

Write it clearly enough that an opponent could attack it. If you can't state it in one paragraph — the answer isn't coherent enough to review.

Artifact: The stated answer. Everything below attacks this specific text.

2 — ATTACK: Write Five Specific Attacks Against THIS Answer

Not generic concerns. Attacks on THIS specific answer for THIS specific problem.

For each attack, use this template:

ATTACK [N]: [one-line summary]
  Claim:    [the specific thing that is wrong, incomplete, or dangerous]
  Evidence: [why this attack is plausible — cite specific aspects
             of the answer, the domain, or the context]
  If true:  [what happens — the specific consequence]

Attack axis checklist — write at least one attack per axis:

  1. Correctness attack: "Step/claim X is factually wrong because [specific reason]." Not "might be wrong" — write it as if you believe it IS wrong.

Read the full file on GitHub · 132 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. 11d ago First seen · 132 lines · 22 tokens per session scan A ef88c30c9252

Subscribe to this mod's changes

adversary is a skill published in the GitHub repository Kshitijpalsinghtomar/depth-skills (4 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 1,433 once invoked, about $0.0001 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.

Related

Other skills, from other repositories

vox-video-director

Turn ONE topic into a finished Vox-style paper-collage explainer / ad video, end to end with Aliyun Bailian CLI + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Use this whenever the user wants a "Vox style" video, a paper/torn-paper collage animation, a "motion collage"…

modelstudioai/skills · 238 tokens

bailian-train-deploy

A workflow for using Alibaba Cloud’s Bailian command-line tool to fine-tune or directly deploy AI models as callable services. It covers text, speech-synthesis, image-generation, and video-generation models.

modelstudioai/skills · 321 tokens

api-database-mongodb

Native MongoDB driver (the mongodb npm package) - MongoClient lifecycle, typed collections, CRUD result shapes, cursors, aggregation pipelines, index design, transactions.

agents-inc/skills · 38 tokens

ai-infrastructure-ollama

Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint.

agents-inc/skills · 41 tokens

ai-orchestration-langchain

LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.

agents-inc/skills · 43 tokens

ai-provider-cohere-sdk

Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection.

agents-inc/skills · 40 tokens