adversarial-analysis

adversarial-analysis is a skill for Claude Code, Codex from adilkalam/orca. It costs 47 tokens per session (1,826 once invoked), scanned A, original, MIT.

A six-step method for stress-testing a proposal before committing to it, including failure scenarios, assumptions, edge cases, objections, and a final decision.

In plain words
What is it for?
Use it to run a pre-mortem, audit assumptions, examine edge cases, catalog failure modes, consider counterarguments, and reach a go or no-go verdict.
Why use it?
It helps reveal weaknesses and prevent avoidable failures before implementation begins.

Skill for Claude CodeCodex

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

Good fit Use it to run a pre-mortem, audit assumptions, examine edge cases, catalog failure modes, consider counterarguments, and reach a go or no-go verdict.

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Install with agentmods
npx agentmods add skills/adilkalam/orca/adversarial-analysis
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 adilkalam/orca --skill adversarial-analysis
Clone the repo
git clone --depth 1 https://github.com/adilkalam/orca

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 adversarial-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/adilkalam/orca/adversarial-analysis/github.svg)](https://agentmods.dev/skills/adilkalam/orca/adversarial-analysis)
Your own site
<a href="https://agentmods.dev/skills/adilkalam/orca/adversarial-analysis"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/adversarial-analysis/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 adversarial-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/adilkalam/orca/adversarial-analysis"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/adversarial-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,826 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 $0.00047 $0.01826
Opus 5.5 $0.00019 $0.00730
Sonnet 5.5 $0.00009 $0.00365
Haiku 4.5 $0.00005 $0.00183

Measured 1mo ago against content hash 0753ec6bea12, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

adversarial-analysis 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 1mo 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/adversarial-analysis/SKILL.md · 309 lines

How it starts

The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adversarial Analysis

RULE: Before implementing any significant proposal, systematically attack it to find weaknesses.

The Principle

Proposals that survive adversarial scrutiny are more robust. This skill provides a 6-phase framework for stress-testing ideas before commitment.

Loading This Skill

Agents can load via:

required_skills:
  - adversarial-analysis

Or dynamically invoke:

skill: adversarial-analysis

The 6-Phase Framework

Phase 1: Pre-mortem

"It's 6 months from now. This failed. Why?"

Assume complete failure and work backwards. What went wrong?

Process:

  1. Vividly imagine the failure state
  2. Identify 5-7 distinct failure scenarios
  3. Trace each back to root causes
  4. Note which causes were preventable

Output format:

### Pre-mortem
> "It's 6 months from now. This failed. Why?"
- [Failure scenario 1]: [Root cause]
- [Failure scenario 2]: [Root cause]
- [Failure scenario 3]: [Root cause]

Phase 2: Assumption Audit

Every proposal rests on assumptions. Surface and stress-test them.

Process:

  1. List every assumption (explicit and implicit)
  2. Rate confidence: H (high) / M (medium) / L (low)
  3. For each, answer: "If wrong, what breaks?"

Output format:

### Assumptions (confidence: H/M/L)
| Assumption | Confidence | If Wrong |
|------------|------------|----------|
| [Assumption 1] | M | [Impact if false] |
| [Assumption 2] | L | [Impact if false] |
| [Assumption 3] | H | [Impact if false] |

Prioritize low-confidence, high-impact assumptions for deeper analysis.


Phase 3: Edge Case Storm

Generate 10+ scenarios that could break the proposal.

Process:

  1. Think about boundary conditions
  2. Consider rare but possible inputs
  3. Imagine hostile actors
  4. Consider scale extremes (0, 1, many, millions)
  5. Consider timing/ordering issues
  6. Consider resource exhaustion
  7. Consider integration failures

Output format:

### Edge Cases
- [Breaking scenario 1]
- [Breaking scenario 2]
- [Breaking scenario 3]
... (minimum 10)

Read the full file on GitHub · 309 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. 1mo ago First seen · 309 lines · 47 tokens per session scan A 0753ec6bea12

Subscribe to this mod's changes

adversarial-analysis is a skill published in the GitHub repository adilkalam/orca (2 stars, last pushed 5d ago), licensed MIT. It adds 47 tokens to every session and 1,826 once invoked, about $0.0002 per session on Opus 5.5. 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.

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