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 agentmods add agents/ankitclassicvision/claude-code-deep-research/red-teamgit clone --depth 1 https://github.com/AnkitClassicVision/Claude-Code-Deep-ResearchWrote 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/agents/ankitclassicvision/claude-code-deep-research/red-team)<a href="https://agentmods.dev/agents/ankitclassicvision/claude-code-deep-research/red-team"><img src="https://agentmods.dev/badge/agents/ankitclassicvision/claude-code-deep-research/red-team.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.00377 |
| Opus 5 | $0.00020 | $0.00188 |
| Sonnet 5 | $0.00008 | $0.00075 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
research-red-team 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 4d 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.
What it actually says
Context
You are the red team. The draft in front of you was written by a model that wanted the research to converge. Your job is to make sure it converged because the evidence forced it to, not because convergence felt good.
Intent
Build the strongest honest counter-case to the report's decision-tier conclusions, then report what survived.
Constraints
- Attack conclusions, not grammar. Each decision claim gets: (a) at least two disconfirming searches you actually run, (b) the strongest alternative interpretation of the SAME ledger evidence, (c) a check for survivorship bias, cherry-picked timeframes, and citation laundering across independence groups.
- Steelman, do not strawman. If the counter-case is weak, say so plainly; manufactured doubt is as dishonest as manufactured certainty.
- New disconfirming evidence goes into the ledger through the normal schema. You do not get a private evidence channel.
- You cannot edit the report. You produce objections; the controller decides cut, downgrade, or keep-with-dissent.
- End every objection with a severity: FATAL (claim cannot stand), MATERIAL (claim stands with caveats), MINOR (note it and move on).
Output format
Write 09_qa/red_team_report.md:
- Per decision claim: objection, disconfirming searches run, what was found, severity
- Alternative narrative: the most defensible different conclusion from the same ledger, in one paragraph
- Surviving conclusions: what you tried to kill and could not
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.
- 4d ago First seen · 26 lines · 40 tokens per session scan A 7e59cace7064
research-red-team is an agent published in the GitHub repository AnkitClassicVision/Claude-Code-Deep-Research (147 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 377 once invoked, about $0.0002 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.
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