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 skills add Demerzels-lab/elsamultiskillagent --skill adversarial-promptinggit clone --depth 1 https://github.com/Demerzels-lab/elsamultiskillagentWrote 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/demerzels-lab/elsamultiskillagent/adversarial-prompting)<a href="https://agentmods.dev/skills/demerzels-lab/elsamultiskillagent/adversarial-prompting"><img src="https://agentmods.dev/badge/skills/demerzels-lab/elsamultiskillagent/adversarial-prompting/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.
<a href="https://agentmods.dev/skills/demerzels-lab/elsamultiskillagent/adversarial-prompting"><img src="https://agentmods.dev/badge/skills/demerzels-lab/elsamultiskillagent/adversarial-prompting.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00054 | $0.00862 |
| Opus 5 | $0.00027 | $0.00431 |
| Sonnet 5 | $0.00011 | $0.00172 |
| Haiku 4.5 | $0.00005 | $0.00086 |
Grade A, and why
adversarial-prompting 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Prompting
This skill applies a structured adversarial methodology to problem-solving by generating multiple solutions, rigorously critiquing each for weaknesses, developing fixes, validating those fixes, and consolidating into ranked recommendations. The approach forces deep analysis of failure modes, edge cases, and unintended consequences before committing to a solution.
When to Use This Skill
Use this skill when:
- Facing complex technical problems requiring thorough analysis (architecture decisions, debugging, performance optimization)
- Solving strategic or business problems with multiple viable approaches
- Needing to identify weaknesses in proposed solutions before implementation
- Requiring validated fixes that address root causes, not symptoms
- Working on high-stakes decisions where failure modes must be understood
- Seeking comprehensive analysis with detailed reasoning visible throughout
Do not use this skill for:
- Simple, straightforward problems with obvious solutions
- Time-sensitive decisions requiring immediate action without analysis
- Problems where exploration and iteration are more valuable than upfront analysis
How to Use This Skill
Primary Workflow
When invoked, apply the following 7-phase process to the user's problem:
Phase 1: Solution Generation
Generate 3-7 distinct solution approaches. For each solution:
- Explain the reasoning behind the approach
- Describe the core strategy
- Outline the key steps or components
Phase 2: Adversarial Critique
For each solution, rigorously identify critical weaknesses. Show thinking while examining:
- Edge cases and failure modes
- Security vulnerabilities or risks
- Performance bottlenecks
- Scalability limitations
- Hidden assumptions that could break
- Resource constraints (time, money, people)
- Unintended consequences
- Catastrophic failure scenarios
Be creative and thorough in identifying what could go wrong.
Phase 3: Fix Development
For each identified weakness:
- Propose a specific fix or mitigation strategy
- Explain why this fix addresses the root cause
- Describe how the fix integrates with the original solution
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 103 lines · 54 tokens per session scan A 2eb542c320b6
adversarial-prompting is a skill published in the GitHub repository Demerzels-lab/elsamultiskillagent (10 stars, last pushed 4mo ago), licensed MIT. It adds 54 tokens to every session and 862 once invoked, about $0.0003 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 2026-09-03.
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