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/validationgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWhat 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 | $0.04449 |
| Opus 5 | $0.00000 | $0.02225 |
| Sonnet 5 | $0.00000 | $0.00890 |
| Haiku 4.5 | $0.00000 | $0.00445 |
Grade C, and why
validation 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 3d 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: validation description: Independently verify analytical findings by re-deriving key numbers, checking arithmetic, cross-referencing data sources, and flagging common statistical errors. inputs: Copies of this mod
1 near-identical copy found in the catalogue:
- validation — 89% identical, 47 lines differ
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Validation
Purpose
Independently verify analytical findings by re-deriving key numbers, checking arithmetic, cross-referencing data sources, and flagging common statistical errors — producing a pass/fail validation report with confidence ratings.
Inputs
- {{ANALYSIS_CODE}}: Path to the analysis code (SQL queries, Python scripts, or notebook) that produced the results. The agent will re-execute key queries independently.
- {{ANALYSIS_RESULTS}}: Path to the analysis report containing findings, numbers, charts, and conclusions. This is what gets validated.
- {{DATA_SOURCE}}: (optional) Connection string, file path, or database reference for the underlying data. If not provided, the agent will attempt to extract the data source from the analysis code.
- {{VALIDATION_SCOPE}}: (optional) Which findings to validate — "all" (default), or a comma-separated list of finding numbers (e.g., "1,3,5") for targeted validation. Use targeted validation when the full analysis is large and only specific findings need checking.
Workflow
Step 1: Inventory the claims
Read {{ANALYSIS_RESULTS}} end to end. Extract every quantitative claim into a numbered list. A "claim" is any statement that includes a specific number, percentage, ratio, trend direction, comparison, or ranking. For each claim, record:
- Claim ID: Sequential number (C1, C2, C3...)
- Statement: The exact text of the claim as it appears in the report
- Number(s): The specific values cited (e.g., "23%", "$1.2M", "3.5x")
- Source section: Where in the report the claim appears
- Derivable?: Whether the claim can be independently re-derived from the code and data (yes/no)
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.
- 3d ago First seen · 323 lines · 0 tokens per session scan C 48606c27143a
validation is an agent published in the GitHub repository ai-analyst-lab/ai-analyst (296 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,449 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.
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