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/ai-analyst-lab/ai-analyst-plus/cross-verificationgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-plusWrote 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/ai-analyst-lab/ai-analyst-plus/cross-verification)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plus/cross-verification"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plus/cross-verification.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 | $0.00000 | $0.02985 |
| Opus 5 | $0.00000 | $0.01492 |
| Sonnet 5 | $0.00000 | $0.00597 |
| Haiku 4.5 | $0.00000 | $0.00298 |
Grade C, and why
cross-verification scanned grade C with 1 finding 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 5d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- CONTRACT_START name: cross-verification description: Verify analytical findings via same-source, different-calculation-path checks. Produces confidence scores and provenance records. inputs: - name: ANALYSIS_RESULTS How it starts
The opening of the file, as written. The whole thing — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Cross-Verification
Purpose
Verify analytical findings by running same-source, different-calculation-path checks. Instead of comparing two data sources (old dual-path tie-out), this agent re-derives key findings through alternative calculations and validates boundary conditions. Produces confidence scores and structured provenance records that downstream agents (story-architect, deck-creator, export agents) use to show their work.
Inputs
- {{ANALYSIS_RESULTS}}: Path(s) to analysis reports from upstream agents. Read all available:
outputs/analysis_report_{{DATE}}.md,outputs/trend_report_{{DATE}}.md,working/cohort_analysis_{{DATASET}}.md,working/investigation_{{DATASET}}.md. - {{QUERY_LOG}}: Path to the JSONL query log (
working/query_log_{{DATASET_NAME}}_{{DATE}}.jsonl). If not provided, check the standard path. - {{DATASET_NAME}}: Short name for output file naming.
- {{CONNECTION_TYPE}}: Warehouse type (duckdb, snowflake, bigquery, postgres, csv). Determines tolerance adjustments.
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.
- 5d ago First seen · 353 lines · 0 tokens per session scan C b59df7cd57bc
cross-verification is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plus (19 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,985 tokens. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.