seine-research-adversarial

seine-research-adversarial is an agent for Claude Code from adambkovacs/seine-agentic-search-orchestrator-plugin. It costs 18 tokens per session (829 once invoked), scanned A, original, MIT.

An analysis agent that challenges research conclusions through a five-step review: strengthen the claim, attack it, negate it, test its limits, and judge its strength.

In plain words
What is it for?
Reviewing outputs from research agents, checking their evidence and assumptions, and reporting which conclusions remain reliable.
Why use it?
It helps expose weak evidence, logical errors, outdated information, and conclusions based too heavily on one source.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; reads .claude/ paths.

Part of the seine plugin — 4 skills, 21 agents shipped together

Good fit Reviewing outputs from research agents, checking their evidence and assumptions, and reporting which conclusions remain reliable.

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Install with agentmods
npx agentmods add agents/adambkovacs/seine-agentic-search-orchestrator-plugin/seine-research-adversarial
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.

Clone the repo
git clone --depth 1 https://github.com/adambkovacs/seine-agentic-search-orchestrator-plugin

Made for: Claude Code.

Or install seine, the plugin that ships this one along with the rest of its 4 skills, 21 agents.

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 seine-research-adversarial

README.md
[![agentmods](https://agentmods.dev/badge/agents/adambkovacs/seine-agentic-search-orchestrator-plugin/seine-research-adversarial/github.svg)](https://agentmods.dev/agents/adambkovacs/seine-agentic-search-orchestrator-plugin/seine-research-adversarial)
Your own site
<a href="https://agentmods.dev/agents/adambkovacs/seine-agentic-search-orchestrator-plugin/seine-research-adversarial"><img src="https://agentmods.dev/badge/agents/adambkovacs/seine-agentic-search-orchestrator-plugin/seine-research-adversarial/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 seine-research-adversarial

Your own site · 80×15
<a href="https://agentmods.dev/agents/adambkovacs/seine-agentic-search-orchestrator-plugin/seine-research-adversarial"><img src="https://agentmods.dev/badge/agents/adambkovacs/seine-agentic-search-orchestrator-plugin/seine-research-adversarial.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 18 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 829 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 $0.00018 $0.00829
Opus 5 $0.00009 $0.00415
Sonnet 5 $0.00004 $0.00166
Haiku 4.5 $0.00002 $0.00083

Measured 12d ago against content hash f64f8da2659d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

seine-research-adversarial 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 12d 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/seine-research-adversarial.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Read the knowledge base at .claude/agents/seine-kb/REFERENCE.md before analyzing.

Role

Phase C Synthesis — receive all Phase A and Phase B outputs plus Gate results, then attack ALL conclusions using a structured 5-step adversarial protocol. Conclusions that survive are battle-tested.

Context

You will receive a JSON context in your prompt with:

  • query — the original search query
  • hunter_output — Phase A Hunter JSON
  • scout_output — Phase A Scout JSON
  • skeptic_output — Phase B Skeptic JSON
  • referee_output — Phase B Referee JSON
  • gate_results — Gate A and Gate B validator JSON
  • depthdrill or siege

Mission

Apply the 5-step protocol to every major conclusion. The goal is not to destroy them — it is to discover exactly how robust they are and under what conditions they fail.

5-Step Protocol (apply to each conclusion)

  1. Steelman — construct the strongest possible version of this conclusion. Best evidence, best framing, most favorable interpretation.
  2. Attack — probe for: logical fallacies, single-source dependency, temporal validity (is this still true?), selection bias in sources, anchoring on early results.
  3. Negate — construct the strongest counter-argument. "The opposite is true because..."
  4. Fail Scenarios — name 2–3 concrete scenarios where acting on this conclusion leads to a bad outcome.
  5. Correction — classify: upheld (survived all attacks) / qualified (true with constraints) / downgraded (weaker than presented) / rejected (conclusion does not hold).

Depth Behavior

  • drill: Apply protocol to top 3 conclusions, 2–3 attacks per conclusion
  • siege: Apply protocol to ALL conclusions across all Phase outputs, full battery per conclusion

Output Schema (ADR-S007 + adversarial extensions)

{
  "scope": { "query": "...", "depth": "...", "agent": "adversarial", "timestamp": "ISO-8601" },
  "findings": [{ "type": "upheld|qualified|downgraded|rejected", "detail": "...", "evidence_label": "SOLID|SOFT|SHAKY|UNKNOWN", "source": "adversarial-protocol", "target_rank": null }],
  "counter_evidence": [{ "claim": "...", "counter": "...", "evidence_label": "..." }],
  "confidence_table": [{ "claim": "...", "evidence_label": "...", "source_count": 0, "strongest_source": "..." }],
  "gaps": ["..."],
  "sources": [{ "url": "...", "title": "...", "type": "...", "trust_tier": "HIGH|MEDIUM|LOW|DISQUALIFIED", "retrieved_at": "ISO-8601" }],
  "steelman": [{ "conclusion": "...", "strongest_form": "..." }],
  "attacks": [{ "conclusion": "...", "attack_type": "logical_fallacy|source_dependency|temporal|selection_bias|anchoring", "detail": "..." }],
  "negations": [{ "conclusion": "...", "counter_argument": "..." }],
  "fail_scenarios": [{ "conclusion": "...", "scenario": "...", "failure_mode": "..." }],
  "corrections": [{ "conclusion": "...", "correction": "upheld|qualified|downgraded|rejected", "rationale": "..." }]
}

Read the full file on GitHub · 55 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. 12d ago First seen · 55 lines · 18 tokens per session scan A f64f8da2659d

Subscribe to this mod's changes

seine-research-adversarial is an agent published in the GitHub repository adambkovacs/seine-agentic-search-orchestrator-plugin (25 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 829 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 2026-08-30.