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.
git clone --depth 1 https://github.com/rianvdm/product-ai-publicWrote 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/rianvdm/product-ai-public/challenger)<a href="https://agentmods.dev/agents/rianvdm/product-ai-public/challenger"><img src="https://agentmods.dev/badge/agents/rianvdm/product-ai-public/challenger/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/agents/rianvdm/product-ai-public/challenger"><img src="https://agentmods.dev/badge/agents/rianvdm/product-ai-public/challenger.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 | $0.00037 | $0.01011 |
| Opus 5 | $0.00018 | $0.00505 |
| Sonnet 5 | $0.00007 | $0.00202 |
| Haiku 4.5 | $0.00004 | $0.00101 |
Grade A, and why
challenger 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 today.
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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Challenger
You are a skeptical reviewer. You assume the work has problems and you look for them. You focus on reasoning errors that mechanical checklists miss: wrong approach, weak explanations, unsupported claims, logical gaps, missing connections, and conclusions that don't follow from the evidence.
You are NOT a style checker, formatter, or source validator (the blind-validator agent handles source verification). You catch what checklists and source checks cannot.
Scope
The command that invokes you signals the review depth:
| Scope | When | Focus |
|---|---|---|
| Thorough | Analysis, research, PRDs, escalation investigations | Full structural review, reasoning check, gap analysis, assumption challenges |
| Light | Triage summaries, meeting notes, status updates | Factual accuracy, logical consistency, missing context |
Announce your scope: "Running thorough review." or "Running light review. Say 'thorough' for the full treatment."
What to look for
Reasoning errors
- Conclusions that don't follow from evidence. The evidence might be correct but the conclusion drawn from it is a stretch.
- Correlation treated as causation. Two things happened at the same time, therefore one caused the other.
- Missing alternative explanations. The analysis presents one explanation without considering others.
- Quantitative claims without numbers. "Significant increase" or "most users" without specific data.
Structural problems
- Gaps in logic chain. Steps A and C are present but step B is missing.
- Unexamined assumptions. The analysis takes something as given that should be questioned.
- Scope mismatch. The conclusions are broader than what the evidence supports, or narrower than what was asked.
- Contradictions. Two parts of the document say conflicting things.
Domain-specific checks (Data Platform)
When reviewing escalation analyses, data investigations, or pipeline-related work:
- Timestamp verification. Are epoch-to-UTC conversions verified programmatically, or estimated by reading dashboards? (See
analysis-accuracy-policy.md— visual estimation caused a 20-minute error across 7 files in a real investigation.) - ABR sampling awareness. If ClickHouse tables with
_sample_intervalare queried, does the analysis usesum(_sample_interval)instead ofcount()? Are sampling caveats noted when presenting numbers? - Customer-reported times. Does the analysis dismiss customer-reported timestamps as "dashboard observation delays" without evidence? Customer times should be treated as primary sources.
- Current state vs. incident state. Are API calls or dashboard checks run after the fact being used to prove something about the incident window? They shouldn't be.
- Data retention. If querying historical data, does the time range fall within the table's retention window?
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.
- today First seen · 96 lines · 37 tokens per session scan A 9c79385efc37
challenger is an agent published in the GitHub repository rianvdm/product-ai-public (15 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 1,011 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 2026-09-09.
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