Borrowing it
Nothing to install: this file belongs to tusharagg1/aifolimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tusharagg1/aifolimizer/master/.claude/skills/adversarial-research/SKILL.mdgit clone --depth 1 https://github.com/tusharagg1/aifolimizerWrote 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/tusharagg1/aifolimizer/adversarial-research)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/adversarial-research"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/adversarial-research/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/tusharagg1/aifolimizer/adversarial-research"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/adversarial-research.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 | $0.00088 | $0.04502 |
| Opus 5 | $0.00044 | $0.02251 |
| Sonnet 5 | $0.00018 | $0.00900 |
| Haiku 4.5 | $0.00009 | $0.00450 |
Grade A, and why
adversarial-research 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Research Pipeline (Stage 0-5)
Data-grounding contract: every numeric claim made by ANY advocate or the PM must trace to a Layer-1 tool response in this run. Restate the verified figures up front; cite only those. No recalled or estimated numbers - if it wasn't fetched, it's "not available". This is what keeps the debate honest.
Modelled on TradingAgents multi-agent hedge fund workflow. Explicit DAG: memory recall → parallel data → parallel advocates → three-tier risk debate → portfolio manager synthesis → log decision. Each layer waits for prior layer to complete.
Layer 0 (serial): get_profile + get_ticker_decision_history + get_cross_ticker_lessons + recall_preferences
Layer 1 (parallel): get_portfolio | get_fundamentals | get_technicals |
get_news_headlines | get_macro_snapshot | get_positioning_signals |
get_stocktwits_sentiment | get_community_sentiment |
get_insider_sentiment | get_finnhub_news | get_recent_filings |
get_search_interest
Layer 2 (parallel): Bull Agent | Bear Agent | Consensus Agent
(all receive identical Layer 1 snapshot + Layer 0 memory context)
Layer 2.5 (serial): Probability Assignment - PM reads Layer 2 outputs, assigns scenario probs,
produces ARBITER_MEMO before risk managers see anything
Layer 3 (parallel): Risk Aggressive | Risk Neutral | Risk Conservative
(all receive Layer 1 + Layer 2 outputs + ARBITER_MEMO with anchored probs)
Layer 4 (serial): Portfolio Manager synthesis (this context window)
Layer 5 (serial): Decision output + log_trade_decision
Rules for DAG execution:
- Layer 0: 4 MCP calls in ONE message (true parallel)
- Layer 1: 12 MCP calls in ONE message (true parallel); the 4 US-only adds (insider/news/filings/search) return empty for .TO names - that's fine, agents note "unavailable"
- Layer 2: 3 Agent calls in ONE message (true parallel); pass identical data to all three
- Layer 2.5: IN MAIN CONTEXT - read all three Layer 2 outputs, produce ARBITER_MEMO (see below). Do NOT spawn agent.
- Layer 3: 3 Agent calls in ONE message (true parallel); pass Layer 2 outputs + Layer 1 data + ARBITER_MEMO
- Layer 4+5: synthesize in main context; do NOT spawn more agents
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 · 257 lines · 88 tokens per session scan A 0bc233ddd43e
adversarial-research is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 8d ago), licensed MIT. It adds 88 tokens to every session and 4,502 once invoked, about $0.0004 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.
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